Business Intelligence
Robotics, Social Networks, AI and IoT 579
Caveats of
Analytics and AI
725
Chapter 14
Implementation Issues: From Ethics and Privacy to Organizational and Societal
Impacts 726
Glossary 770 Index 785
iii
Preface xxv
About the Authors xxxiv
Introduction to Analytics and AI 1
Chapter 1 Overview of Business Intelligence, Analytics, Data
Science, and Artificial Intelligence: Systems for Decision
Support 2
1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and
Escalators Company 3
1.2 Changing Business Environments and Evolving Needs for
Decision Support and Analytics 5
Decision-Making Process 6
The Influence of the External and Internal Environments on the Process 6
Data and Its Analysis in Decision Making 7
Technologies for Data Analysis and Decision Support 7
1.3 Decision-Making Processes and Computerized Decision Support Framework 9
Simon’s Process: Intelligence, Design, and Choice 9
The Intelligence Phase: Problem (or Opportunity) Identification 10
0 APPLICATION CASE 1.1 Making Elevators Go Faster! 11
The Design Phase 12
The Choice Phase 13
The Implementation Phase 13
The Classical Decision Support System Framework 14
PART IV
Chapter 10 Robotics: Industrial and Consumer Applications 580
Chapter 11 Group Decision Making, Collaborative Systems, and AI
Support 610
Chapter 12 K nowledge Systems: Expert Systems, Recommenders,
Chatbots, Virtual Personal Assistants, and Robo A
dvisors 648
Chapter 13 The Internet of Things as a Platform for Intelligent
Applications 687
PART V
PART I
A DSS Application 16
Components of a Decision Support System 18
The Data Management Subsystem 18
The Model Management Subsystem 19
0 APPLICATION CASE 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions 20
The User Interface Subsystem 20
The Knowledge-Based Management Subsystem 21
1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science 22
A Framework for Business Intelligence 25
The Architecture of BI 25
The Origins and Drivers of BI 26
Data Warehouse as a Foundation for Business Intelligence 27
Transaction Processing versus Analytic
Processing 27 A Multimedia Exercise in
Business Intelligence 28
iv
v Contents
1.5 Analytics Overview 30
Descriptive Analytics 32
0 APPLICATION CASE 1.3 Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities 32
0 APPLICATION CASE 1.4 Siemens Reduces Cost with the Use of Data Visualization 33
Predictive Analytics 33
0 APPLICATION CASE 1.5 Analyzing Athletic Injuries 34
Prescriptive Analytics 34
0 APPLICATION CASE 1.6 A Specialty Steel Bar Company Uses Analytics to Determine
Available-to-Promise Dates 35
1.6 Analytics Examples in Selected Domains 38
Sports Analytics—An Exciting Frontier for Learning and Understanding
Applications of Analytics 38
Analytics Applications in Healthcare—Humana Examples 43
0 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50
1.7 Artificial Intelligence Overview 52
What Is Artificial Intelligence? 52
The Major Benefits of AI 52
The Landscape of AI 52 0 APPLICATION CASE 1.8 AI Increases Passengers’ Comfort and
Security in Airports and Borders 54
The Three Flavors of AI Decisions 55
Autonomous AI 55
Societal Impacts 56 0 APPLICATION CASE 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits
58
1.8 Convergence of Analytics and AI 59
Major Differences between Analytics and AI 59
Why Combine Intelligent Systems? 60
How Convergence Can Help? 60
Big Data Is Empowering AI Technologies 60
The Convergence of AI and the IoT 61
The Convergence with Blockchain and Other Technologies 62
0 APPLICATION CASE 1.10 Amazon Go Is Open for Business 62 IBM and Microsoft
Support for Intelligent Systems Convergence 63
1.9 Overview of the Analytics Ecosystem 63
1.10 Plan of the Book 65
1.11 Resources, Links, and the Teradata University Network Connection 66
Resources and Links 66
Vendors, Products, and Demos 66
Periodicals 67
The Teradata University Network Connection 67
vi Contents
The Book’s Web Site 67
Chapter Highlights 67 • Key Terms 68
Questions for Discussion 68 • Exercises 69 References 70
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications 73
2.1 Opening Vignette: INRIX Solves Transportation Problems 74
2.2 Introduction to Artificial Intelligence 76
Definitions 76
Major Characteristics of AI Machines 77
Major Elements of AI 77
AI Applications 78
Major Goals of AI 78
Drivers of AI 79
Benefits of AI 79
Some Limitations of AI Machines 81
Three Flavors of AI Decisions 81
Artificial Brain 82
2.3 Human and Computer Intelligence 83
What Is Intelligence? 83
How Intelligent Is AI? 84
Measuring AI 85
0 APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner Be? 86
2.4 Major AI Technologies and Some Derivatives 87
Intelligent Agents 87
Machine Learning 88
0 APPLICATION CASE 2.2 How Machine Learning Is Improving Work in
Business 89
Machine and Computer Vision 90
Robotic Systems 91
Natural Language Processing 92
Knowledge and Expert Systems and Recommenders 93
Chatbots 94
Emerging AI Technologies 94
2.5 AI Support for Decision Making 95
Some Issues and Factors in Using AI in Decision Making 96
AI Support of the Decision-Making Process 96
Automated Decision Making 97
0 APPLICATION CASE 2.3 How Companies Solve Real-World Problems Using Google’s Machine-Learning Tools 97
Conclusion 98
Contents vii
2.6 AI Applications in Accounting 99
AI in Accounting: An Overview 99
AI in Big Accounting Companies 100
Accounting Applications in Small Firms 100
0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI 100 Job of Accountants
101
2.7 AI Applications in Financial Services 101
AI Activities in Financial Services 101
AI in Banking: An Overview 101
Illustrative AI Applications in Banking 102
Insurance Services 103 0 APPLICATION CASE 2.5 US Bank Customer Recognition and Services 104
2.8 AI in Human Resource Management (HRM) 105
AI in HRM: An Overview 105
AI in Onboarding 105 0 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) Is
Using AI to Support the Recruiting Process 106
Introducing AI to HRM Operations 106
2.9 AI in Marketing, Advertising, and CRM 107
Overview of Major Applications 107
AI Marketing Assistants in Action 108
Customer Experiences and CRM 108
0 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketing and CRM 109
Other Uses of AI in Marketing 110
2.10 AI Applications in Production-Operation Management (POM) 110
AI in Manufacturing 110
Implementation Model 111
Intelligent Factories 111
Logistics and Transportation 112 Chapter Highlights 112 • Key Terms 113
Questions for Discussion 113 • Exercises 114 References 114
Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117
3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio
Consumers with Data-Driven Marketing 118
3.2 Nature of Data 121
3.3 Simple Taxonomy of Data 125
0 APPLICATION CASE 3.1 Verizon Answers the Call for Innovation: The Nation’s Largest
Network Provider uses Advanced Analytics to Bring the Future to its Customers 127
Contents
3.4 Art and Science of Data Preprocessing 129
0 APPLICATION CASE 3.2 Improving Student Retention with Data-
Driven Analytics 133
3.5 Statistical Modeling for Business Analytics 139
viii
Descriptive Statistics for Descriptive Analytics 140
Measures of Centrality Tendency (Also Called Measures of Location
or Centrality) 140
Arithmetic Mean 140
Median 141
Mode 141
Measures of Dispersion (Also Called Measures of Spread or
Decentrality) 142
Range 142
Variance 142
Standard Deviation 143
Mean Absolute Deviation 143
Quartiles and Interquartile Range 143
Box-and-Whiskers Plot 143
Shape of a Distribution 145
0 APPLICATION CASE 3.3 Town of Cary Uses Analytics to Analyze Data
from Sensors, Assess Demand, and Detect Problems 150
3.6 Regression Modeling for Inferential Statistics 151
How Do We Develop the Linear Regression Model? 152
How Do We Know If the Model Is Good Enough? 153
What Are the Most Important Assumptions in Linear Regression? 154
Logistic Regression 155
Time-Series Forecasting 156
0 APPLICATION CASE 3.4 Predicting NCAA Bowl Game Outcomes 157
3.7 Business Reporting 163
0 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA 165
3.8 Data Visualization 166
Brief History of Data Visualization 167
0 APPLICATION CASE 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online 169
3.9 Different Types of Charts and Graphs 171
Basic Charts and Graphs 171
Specialized Charts and Graphs 172
Which Chart or Graph Should You Use? 174
3.10 Emergence of Visual Analytics 176
Visual Analytics 178
High-Powered Visual Analytics Environments 180
3.11 Information Dashboards 182
0 APPLICATION CASE 3.7 Dallas Cowboys Score Big with Tableau and Teknion 184
Dashboard Design 184
0 APPLICATION CASE 3.8 Visual Analytics Helps Energy Supplier Make Better Connections 185
Contents ix
What to Look for in a Dashboard 186
Best Practices in Dashboard Design 187
Benchmark Key Performance Indicators with Industry Standards 187
Wrap the Dashboard Metrics with Contextual Metadata 187
Validate the Dashboard Design by a Usability Specialist 187
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 188
Enrich the Dashboard with Business-User Comments 188
Present Information in Three Different Levels 188
Pick the Right Visual Construct Using Dashboard Design Principles 188
Provide for Guided Analytics 188
Chapter Highlights 188 • Key Terms 189
Questions for Discussion 190 • Exercises 190 References 192
Predictive Analytics/Machine Learning 193
Chapter 4 Data Mining Process, Methods, and Algorithms 194
4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee
and Fight Crime 195
4.2 Data Mining Concepts 198
0 APPLICATION CASE 4.1 Visa Is Enhancing the Customer Experience while Reducing Fraud with Predictive Analytics and Data Mining 199
Definitions, Characteristics, and Benefits 201
How Data Mining Works 202
0 APPLICATION CASE 4.2 American Honda Uses Advanced Analytics to Improve Warranty Claims 203
Data Mining Versus Statistics 208
4.3 Data Mining Applications 208
0 APPLICATION CASE 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding 210
4.4 Data Mining Process 211
Step 1: Business Understanding 212
Step 2: Data Understanding 212
Step 3: Data Preparation 213
Step 4: Model Building 214
0 APPLICATION CASE 4.4 Data Mining Helps in
Cancer Research 214
Step 5: Testing and Evaluation 217 Contents
Step 6: Deployment 217
Other Data Mining Standardized Processes and Methodologies 217
4.5 Data Mining Methods 220
Classification 220
Estimating the True Accuracy of Classification Models 221
Estimating the Relative Importance of Predictor Variables 224
PART II
x
Cluster Analysis for Data Mining 228
0 APPLICATION CASE 4.5 Influence Health Uses Advanced
Predictive Analytics to Focus on the Factors That Really Influence
People’s Healthcare Decisions 229
Association Rule Mining 232
4.6 Data Mining Software Tools 236
0 APPLICATION CASE 4.6 Data Mining goes to Hollywood: Predicting Financial Success of Movies 239
4.7 Data Mining Privacy Issues, Myths, and Blunders 242
0 APPLICATION CASE 4.7 Predicting Customer Buying Patterns—The Target Story 243
Data Mining Myths and Blunders 244
Chapter Highlights 246 • Key Terms 247
Questions for Discussion 247 • Exercises 248 References 250
Chapter 5 Machine-Learning Techniques for Predictive
Analytics 251
5.1 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedures
252
5.2 Basic Concepts of Neural Networks 255
Biological versus Artificial Neural Networks 256
0 APPLICATION CASE 5.1 Neural Networks are Helping to
Save Lives in the Mining Industry 258
5.3 Neural Network Architectures 259
Kohonen’s Self-Organizing Feature Maps 259
Hopfield Networks 260
0 APPLICATION CASE 5.2 Predictive Modeling Is Powering the Power Generators 261
5.4 Support Vector Machines 263
0 APPLICATION CASE 5.3 Identifying Injury Severity Risk Factors in Vehicle Crashes with Predictive Analytics 264
Mathematical Formulation of SVM 269
Primal Form 269
Dual Form 269
Soft Margin 270
Nonlinear Classification 270
Kernel Trick 271
5.5 Process-Based Approach to the Use of SVM 271
Support Vector Machines versus Artificial Neural Networks 273
5.6 Nearest Neighbor Method for Prediction 274
Similarity Measure: The Distance Metric 275
Parameter Selection 275
0 APPLICATION CASE 5.4 Efficient Image Recognition and Categorization with knn 277
Contents xi
5.7 Naïve Bayes Method for Classification 278
Bayes Theorem 279
Naïve Bayes Classifier 279
Process of Developing a Naïve Bayes Classifier 280
Testing Phase 281 0 APPLICATION CASE 5.5 Predicting Disease Progress in Crohn’s Disease Patients: A
Comparison of Analytics Methods 282
5.8 Bayesian Networks 287 How Does BN Work? 287
How Can BN Be Constructed? 288
5.9 Ensemble Modeling 293
Motivation—Why Do We Need to Use Ensembles? 293
Different Types of Ensembles 295
Bagging 296
Boosting 298
Variants of Bagging and Boosting 299
Stacking 300
Information Fusion 300
Summary—Ensembles are not Perfect! 301
0 APPLICATION CASE 5.6 To Imprison or Not to Imprison: A Predictive Analytics-Based Decision Support System for Drug Courts 304
Chapter Highlights 306 • Key Terms 308
Questions for Discussion 308 • Exercises 309
Internet Exercises 312 • References 313
Chapter 6 Deep Learning and Cognitive Computing 315
6.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence 316
6.2 Introduction to Deep Learning 320
0 APPLICATION CASE 6.1 Finding the Next Football Star with Artificial Intelligence 323
6.3 Basics of “Shallow” Neural Networks 325
0 APPLICATION CASE 6.2 Gaming Companies Use Data Analytics to Score Points with Players 328
0 APPLICATION CASE 6.3 Artificial Intelligence Helps Protect Animals from Extinction 333
xii Contents
6.4 Process of Developing Neural Network–Based Systems 334
Learning Process in ANN 335
Backpropagation for ANN Training 336
6.5 Illuminating the Black Box of ANN 340
0 APPLICATION CASE 6.4 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents 341
6.6 Deep Neural Networks 343
Feedforward Multilayer Perceptron (MLP)-Type Deep Networks 343
Impact of Random Weights in Deep MLP 344
More Hidden Layers versus More Neurons? 345 0 APPLICATION CASE 6.5 Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions
346
6.7 Convolutional Neural Networks 349
Convolution Function 349
Pooling 352
Image Processing Using Convolutional Networks 353 0 APPLICATION CASE 6.6 From Image Recognition to Face
Recognition 356
Text Processing Using Convolutional Networks 357
6.8 Recurrent Networks and Long Short-Term Memory
Networks 360
0 APPLICATION CASE 6.7 Deliver Innovation by Understanding Customer Sentiments 363
LSTM Networks Applications 365
6.9 Computer Frameworks for Implementation of Deep Learning 368 Torch 368
Caffe 368
TensorFlow 369
Theano 369
Keras: An Application Programming Interface 370
6.10 Cognitive Computing 370
How Does Cognitive Computing Work? 371
How Does Cognitive Computing Differ from AI? 372
Cognitive Search 374
IBM Watson: Analytics at Its Best 375 0 APPLICATION CASE 6.8 IBM Watson Competes against the
Best at Jeopardy! 376
How Does Watson Do It? 377
What Is the Future for Watson? 377
Chapter Highlights 381 • Key Terms 383
Questions for Discussion 383 • Exercises 384
References 385
Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics 388
7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real- Time Sales 389
Contents xiii
7.2 Text Analytics and Text Mining Overview 392
0 APPLICATION CASE 7.1 Netflix: Using Big Data to Drive Big Engagement: Unlocking the Power of Analytics to Drive
Content and Consumer Insight 395
7.3 Natural Language Processing (NLP) 397
0 APPLICATION CASE 7.2 AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World 399
7.4 Text Mining Applications 402
Marketing Applications 403
Security Applications 403
Biomedical Applications 404
0 APPLICATION CASE 7.3 Mining for Lies 404
Academic Applications 407
0 APPLICATION CASE 7.4 The Magic Behind the Magic: Instant Access to Information Helps the Orlando Magic Up their Game and the Fan’s Experience 408
7.5 Text Mining Process 410
Task 1: Establish the Corpus 410
Task 2: Create the Term–Document Matrix 411
Task 3: Extract the Knowledge 413
0 APPLICATION CASE 7.5 Research Literature Survey with Text Mining 415
7.6 Sentiment Analysis 418
0 APPLICATION CASE 7.6 Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledon 419
Sentiment Analysis Applications 422
Sentiment Analysis Process 424
Methods for Polarity Identification 426
Using a Lexicon 426
Using a Collection of Training Documents 427
Identifying Semantic Orientation of Sentences and Phrases 428
Identifying Semantic Orientation of Documents 428
7.7 Web Mining Overview 429
Web Content and Web Structure Mining 431
7.8 Search Engines 433
Anatomy of a Search Engine 434
1. Development Cycle 434
2. Response Cycle 435
Search Engine Optimization 436
Methods for Search Engine Optimization 437
0 APPLICATION CASE 7.7 Delivering Individualized Content and Driving Digital Engagement: How Barbour Collected More Than 49,000
New Leads in One Month with Teradata Interactive 439
7.9 Web Usage Mining (Web Analytics) 441
Web Analytics Technologies 441
Web Analytics Metrics 442
xiv Contents
Web Site Usability 442
Traffic Sources 443
Visitor Profiles 444
Conversion Statistics 444
7.10 Social Analytics 446
Social Network Analysis 446
Social Network Analysis Metrics 447
0 APPLICATION CASE 7.8 Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy 447
Connections 450
Distributions 450
Segmentation 451
Social Media Analytics 451
How Do People Use Social Media? 452
Measuring the Social Media Impact 453
Best Practices in Social Media Analytics 453
Chapter Highlights 455 • Key Terms 456
Questions for Discussion 456 • Exercises 456 References 457
Prescriptive Analytics and Big Data 459
Chapter 8 Prescriptive Analytics:
Optimization and Simulation 460
8.1 Opening Vignette: School District of Philadelphia Uses
Prescriptive Analytics to Find Optimal Solution for
Awarding Bus Route Contracts 461
8.2 Model-Based Decision Making 462
0 APPLICATION CASE 8.1 Canadian Football League Optimizes Game Schedule 463
Prescriptive Analytics Model Examples 465
Identification of the Problem and Environmental Analysis 465
0 APPLICATION CASE 8.2 Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions 466
Model Categories 467
8.3 Structure of Mathematical Models for Decision
Support 469
The Components of Decision Support Mathematical Models 469
The Structure of Mathematical Models 470
8.4 Certainty, Uncertainty, and Risk 471
Decision Making under Certainty 471
Decision Making under Uncertainty 472
Decision Making under Risk (Risk Analysis) 472
0 APPLICATION CASE 8.3 American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes 472
8.5 Decision Modeling with Spreadsheets 473
PART III
Contents xv
0 APPLICATION CASE 8.4 Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children with Families 474
0 APPLICATION CASE 8.5 Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes 475
8.6 Mathematical Programming Optimization 477
0 APPLICATION CASE 8.6 Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians 478
Linear Programming Model 479
Modeling in LP: An Example 480
Implementation 484
8.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 486
Multiple Goals 486
Sensitivity Analysis 487
What-If Analysis 488
Goal Seeking 489
8.8 Decision Analysis with Decision Tables and Decision Trees 490
Decision Tables 490
Decision Trees 492
8.9 Introduction to Simulation 493
Major Characteristics of Simulation 493
0 APPLICATION CASE 8.7 Steel Tubing Manufacturer Uses a Simulation-Based Production Scheduling System 493
Advantages of Simulation 494
Disadvantages of Simulation 495
The Methodology of Simulation 495
Simulation Types 496
Monte Carlo Simulation 497
Discrete Event Simulation 498 0 APPLICATION CASE 8.8 Cosan Improves Its Renewable Energy Supply Chain Using Simulation
498 8.10 Visual Interactive Simulation 500 Conventional Simulation Inadequacies 500
Visual Interactive Simulation 500
Visual Interactive Models and DSS 500
Simulation Software 501
0 APPLICATION CASE 8.9 Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment 501
Chapter Highlights 505 • Key Terms 505
Questions for Discussion 505 • Exercises 506 References 508
Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and
Tools 509
9.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data
Methods 510
9.2 Definition of Big Data 513
The “V”s That Define Big Data 514
0 APPLICATION CASE 9.1 Alternative Data for Market Analysis or Forecasts 517
xvi Contents
9.3 Fundamentals of Big Data Analytics 519
Business Problems Addressed by Big Data Analytics 521 0 APPLICATION CASE 9.2 Overstock.com Combines Multiple Datasets to
Understand Customer Journeys 522
9.4 Big Data Technologies 523 MapReduce 523
Why Use MapReduce? 523
Hadoop 524
How Does Hadoop Work? 525
Hadoop Technical Components 525
Hadoop: The Pros and Cons 527
NoSQL 528 0 APPLICATION CASE 9.3 eBay’s Big Data Solution 529
0 APPLICATION CASE 9.4 Understanding Quality and Reliability of Healthcare Support Information on Twitter 531
9.5 Big Data and Data Warehousing 532
Use Cases for Hadoop 533
Use Cases for Data Warehousing 534
The Gray Areas (Any One of the Two Would Do the Job) 535
Coexistence of Hadoop and Data Warehouse 536
9.6 In-Memory Analytics and Apache Spark™ 537
0 APPLICATION CASE 9.5 Using Natural Language Processing to analyze customer feedback in TripAdvisor reviews 538 Architecture of Apache SparkTM 538
Getting Started with Apache SparkTM 539
9.7 Big Data and Stream Analytics 543
Stream Analytics versus Perpetual Analytics 544
Critical Event Processing 545
Data Stream Mining 546
Applications of Stream Analytics 546
e-Commerce 546
Telecommunications 546
0 APPLICATION CASE 9.6 Salesforce Is Using Streaming Data to
Enhance Customer Value 547
Law Enforcement and Cybersecurity 547
Power Industry 548
Financial Services 548
Health Sciences 548
Government 548
9.8 Big Data Vendors and Platforms 549
Infrastructure Services Providers 550
Analytics Solution Providers 550
Business Intelligence Providers Incorporating Big Data 551
0 APPLICATION CASE 9.7 Using Social Media for Nowcasting Flu Activity 551
0 APPLICATION CASE 9.8 Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse
554
9.9 Cloud Computing and Business Analytics 557
Data as a Service (DaaS) 558
Software as a Service (SaaS) 559
Platform as a Service (PaaS) 559
Infrastructure as a Service (IaaS) 559
Essential Technologies for Cloud Computing 560
0 APPLICATION CASE 9.9 Major West Coast Utility Uses Cloud-Mobile
Technology to Provide Real-Time Incident Reporting 561
Cloud Deployment Models 563
Chapter 9 • Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 17
Major Cloud Platform Providers in Analytics 563
Analytics as a Service (AaaS) 564
Representative Analytics as a Service Offerings 564
Illustrative Analytics Applications Employing the Cloud Infrastructure 565
Using Azure IOT, Stream Analytics, and Machine
Learning to Improve Mobile
Health Care Services 565
Gulf Air Uses Big Data to Get Deeper Customer Insight 566
Chime Enhances Customer Experience Using Snowflake 566
9.10 Location-Based Analytics for Organizations 567
Geospatial Analytics 567 0 APPLICATION CASE 9.10 Great Clips Employs Spatial
Analytics to Shave Time in Location Decisions 570
0 APPLICATION CASE 9.11 Starbucks Exploits GIS and Analytics to Grow Worldwide 570
Real-Time Location Intelligence 572
Analytics Applications for Consumers 573
Chapter Highlights 574 • Key Terms 575
Questions for Discussion 575 • Exercises 575
References 576
Contents
Robotics, Social
Networks, AI and IoT
579
Chapter 10 Robotics: Industrial and
Consumer Applications 580
10.1 Opening Vignette: Robots Provide
Emotional
Support to Patients and
Children 581
10.2 Overview of Robotics 584
10.3 History of Robotics 584
10.4 Illustrative
Applications of Robotics 586
Changing Precision Technology 586
Adidas 586
BMW Employs Collaborative Robots 587
Tega 587
San Francisco Burger Eatery 588
Spyce 588
Mahindra & Mahindra Ltd. 589
Robots in the Defense Industry 589
Pepper 590
Da Vinci Surgical System 592
Snoo – A Robotic Crib 593
MEDi 593
Care-E Robot 593
AGROBOT 594
10.5 Components of Robots 595
10.6 Various Categories of Robots 596
10.7 Autonomous Cars:
Robots in Motion 597
Autonomous Vehicle Development 598
Issues with Self-Driving Cars 599
10.8 Impact of Robots on
Current and Future Jobs 600
10.9 Legal Implications of
Robots and Artificial Intelligence 603
Tort Liability 603
Patents 603
Property 604
Taxation 604
Practice of Law 604
Constitutional Law 605
Professional Certification 605
PART IV
18 Part III • Prescriptive Analytics and Big Data
Law Enforcement 605
Chapter
Highlight
s 606 •
Key
Terms
606
Questions
for
Discussio
n 606 •
Exercises
607
References 607
Chapter 11 Group Decision Making, Collaborative
Systems, and AI Support 610
11.1 Opening Vignette: Hendrick Motorsports
Excels with Collaborative Teams 611
11.2 Making Decisions in Groups: Characteristics, Process, Benefits,
and Dysfunctions 613
Characteristics of Group Work 613
Types of Decisions Made by Groups 614
Group Decision-Making Process 614
Benefits and Limitations of Group Work 615
11.3 Supporting Group Work and Team
Collaboration with Computerized
Systems 616
Overview of Group Support Systems (GSS) 617
Time/Place Framework 617
Group Collaboration for Decision Support 618
11.4 Electronic Support for Group
Communication and
Collaboration 619
Groupware for Group Collaboration 619
Synchronous versus Asynchronous Products 619
Virtual Meeting Systems 620
Collaborative Networks and Hubs 622
Collaborative Hubs 622
Social Collaboration 622
Sample of Popular Collaboration Software 623
11.5 Direct Computerized Support for Group
Decision
Making 623
Group Decision Support Systems (GDSS) 624
Characteristics of GDSS 625
Supporting the Entire Decision-Making Process 625
Brainstorming for Idea Generation and Problem Solving 627
Group Support Systems 628
11.6 Collective Intelligence and Collaborative Intelligence 629
Definitions and Benefits 629
Computerized Support to Collective Intelligence 629
0 APPLICATION CASE 11.1 Collaborative Modeling for Optimal
Water Management: The
Oregon State University
Project 630
How Collective Intelligence May Change Work and Life 631
Collaborative Intelligence 632
How to Create Business Value from
Collaboration: The IBM
Study 632 Contents
11.7 Crowdsourcing as a Method for Decision
Support 633
The Essentials of Crowdsourcing 633
Crowdsourcing for Problem-Solving and Decision Support 634
Implementing Crowdsourcing for Problem Solving 635
0 APPLICATION
CASE 11.2 How
InnoCentive
Helped GSK
Solve a
Chapter 9 • Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 19
Difficult
Problem 636
11.8 Artificial
Intelligence and Swarm AI Support of Team
Collaboration and Group
Decision Making 636
AI Support of Group Decision Making 637
AI Support of Team Collaboration 637
Swarm Intelligence and Swarm AI 639
0 APPLICATION
CASE 11.3 XPRIZE Optimizes
Visioneering 639
11.9 Human–Machine Collaboration and Teams
of Robots 640
Human–Machine Collaboration in Cognitive Jobs 641
Robots as Coworkers: Opportunities and Challenges 641
Teams of collaborating Robots 642
Chapte
r
Highlig
hts 644
• Key
Terms
645
Questio
ns for
Discuss
ion 645
•
Exercis
es 645
Referen
ces 646
Chapter 12 Knowledge Systems:
Expert Systems, Recommenders,
Chatbots, Virtual
Personal
Assistants, and
Robo Advisors 648
12.1 Opening Vignette: Sephora Excels with
Chatbots 649
12.2 Expert Systems and
Recommenders 650
Basic Concepts of Expert Systems (ES) 650
Characteristics and Benefits of ES 652
Typical Areas for ES Applications 653
Structure and Process of ES 653
0 APPLICATION CASE 12.1 ES Aid in Identification of Chemical,
Biological, and Radiological Agents
655
Why the Classical Type of ES Is Disappearing 655
0 APPLICATION CASE 12.2 VisiRule 656
Recommendation Systems 657
0 APPLICATION
CASE 12.3 Netflix
Recommender
: A Critical
Success Factor
658
12.3 Concepts, Drivers,
and Benefits of Chatbots 660
What Is a Chatbot? 660
Chatbot Evolution 660
Components of Chatbots and the Process of Their Use 662
Drivers and Benefits 663
Representative Chatbots from Around the World 663
12.4 Enterprise Chatbots 664
20 Part III • Prescriptive Analytics and Big Data
The Interest of Enterprises in Chatbots 664
Enterprise Chatbots: Marketing and Customer Experience 665
0 APPLICATION CASE 12.4 WeChat’s Super Chatbot 666
0 APPLICATION CASE 12.5 How Vera
Gold Mark Uses Chatbots to Increase
Sales 667
Enterprise Chatbots: Financial Services 668
Enterprise Chatbots: Service Industries 668
Chatbot Platforms 669
0 APPLICATION CASE 12.6 Transavia
Airlines Uses Bots for
Communication and Customer Care
Delivery 669
Knowledge for Enterprise Chatbots 671
12.5 Virtual Personal Assistants 672
Assistant for Information Search 672
If You Were Mark Zuckerberg, Facebook CEO 672
Amazon’s Alexa and Echo 672
Apple’s Siri 675
Google Assistant 675
Other Personal Assistants 675
Competition Among Large Tech Companies 675
Knowledge for Virtual Personal Assistants 675
12.6 Chatbots as Professional Advisors (Robo
Advisors) 676
Robo Financial Advisors 676
Evolution of Financial Robo Advisors 676
Robo Advisors 2.0: Adding the Human Touch 676
0 APPLICATION CASE 12.7 Betterment,
the Pioneer of Financial Robo
Advisors 677
Managing Mutual Funds Using AI 678
Other Professional Advisors 678
IBM Watson 680
12.7 Implementation Issues 680
Technology Issues 680
Disadvantages and Limitations of Bots 681
Quality of Chatbots 681
Setting Up Alexa’s Smart Home System 682
Constructing Bots 682
Chapter Highlights 683 • Key Terms 683
Questions for Discussion 684 •
Exercises 684 References 685
Chapter 13 The Internet of Things as a Platform for
Intelligent Applications 687
13.1 Opening Vignette: CNH Industrial Uses the Internet of Things to Excel 688
13.2 Essentials of IoT 689
Definitions and Characteristics 690 Contents
The IoT Ecosystem 691
Structure of IoT Systems 691
13.3 Major Benefits and Drivers of IoT 694
Major Benefits of IoT 694
Major Drivers of IoT 695
Opportunities 695
13.4 How IoT Works 696
IoT and Decision Support 696
13.5 Sensors and Their Role in IoT 697
Brief Introduction to Sensor Technology 697
0 APPLICATION CASE 13.1 Using Sensors, IoT, and AI for
Environmental
Control at the
Athens, Greece,
International Airport
697
How Sensors Work with IoT 698
0 APPLICATION CASE 13.2 Rockwell Automation
Chapter 9 • Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 21
Monitor
s
Expensiv
e Oil
and Gas
Explorat
ion
Assets
to
Predict
Failures
698
Sensor Applications and Radio-Frequency Identification (RFID) Sensors 699
13.6 Selected IoT Applications 701
A Large-scale IoT in Action 701
Examples of Other Existing Applications 701
13.7 Smart Homes and Appliances 703
Typical Components of Smart Homes 703
Smart Appliances 704
A Smart Home Is Where the Bot Is 706
Barriers to Smart Home Adoption 707
13.8 Smart Cities and Factories 707
0 APPLICATION
CASE 13.3
Amsterdam on
the Road to
Become a
Smart City 708
Smart Buildings: From Automated to Cognitive Buildings 709
Smart Components in Smart Cities and Smart Factories 709
0 APPLICATION
CASE 13.4 How IBM Is
Making Cities
Smarter
Worldwide 711
Improving Transportation in the Smart City 712
Combining Analytics and IoT in Smart City Initiatives 713
Bill Gates’ Futuristic Smart City 713
Technology Support for Smart Cities 713
13.9 Autonomous (Self- Driving) Vehicles 714
The Developments of Smart Vehicles 714
0 APPLICATION
CASE 13.5 Waymo and Autonomous
Vehicles 715
Flying Cars 717
Implementation Issues in Autonomous Vehicles 717
13.10 Implementing IoT and Managerial
Considerations 717
Major Implementation Issues 718
Strategy for Turning Industrial IoT into Competitive Advantage 719
The Future of the IoT 720
Chapter Highlights 721 • Key Terms 72
(accessed October 2018).
579
C H A P T E R
10
Robotics: Industrial and Consumer
Applications
hapter 2 briefly introduced robotics, an early and practical application of concepts developed in AI.
In this chapter, we present a number of applications of robots in industrial as well as personal settings.
Besides learning about the already deployed and emerging applications, we identify the general C
IV
LEARNING OBJECTIVES
■
■ Discuss the general history of automation and
robots
■
■ Discuss the applications of robots in various
industries
■
■ Differentiate between industrial and consumer
applications of robots
■
■ Identify common components of robots
■
■ Discuss impacts of robots on future jobs
■
■ Identify legal issues related to robotics
Chapter 10 • Robotics: Industrial and Consumer Applications 23
components of a robot. In the spirit of managerial considerations, we also discuss the impact of robotics on
jobs as well as related legal issues. Some of the coverage is broad and impacts all other artificial intelligence
(AI), so it may seem to overlap a bit with Chapter 14. But the focus in this chapter is on physical robots, not
just software-driven applications of AI.
This chapter has the following sections:
10.1 O pening Vignette: Robots Provide Emotional Support to Patients and Children 581
10.2 Overview of Robotics 584
10.3 History of Robotics 584
10.4 Illustrative Applications of Robotics 586
10.5 Components of Robots 595
10.6 Various Categories of Robots 596
10.7 Autonomous Cars: Robots in Motion 597
10.8 Impact of Robots on Current and Future Jobs 600
10.9 Legal Implications of Robots and Artificial Intelligence 603
580
10.1 OPENING VIGNETTE: Robots Provide Emotional Support
to Patients and Children As discussed in this chapter, robots have impacted industrial manufacturing and other physical activities. Now, with the research and
evolution of AI, robotics can straddle the social world. For example, hospitals today make an effort to give social and emotional support
to patients and their families. This support is especially sensitive when offering treatment to children. Children in a hospital are in an
unfamiliar environment with medical instruments attached to them, and in many cases, doctors may recommend movement restrictions.
This restriction leads to stress, anxiety, and depression in children and consequently in their family members. Hospitals try to provide
childcare support specialist or companion pet therapies to reduce the trauma. These therapies prepare children and their parents for
future treatment and provide them with temporary emotional support with their interactions. Due to the small number of such
specialists, there is a gap between demand and supply for childcare specialists. Also, it is not possible to provide pet therapy at many
centers due to the fear of allergies, dust, and bites that may cause the patient’s condition to be aggravated. To fill these gaps, the use of
social robots is being explored to resolve depression and anxiety among children. A study (Jeong et al., 2015) found that the physical
presence of a robot is more effective concerning emotional response as compared to a virtual machine interaction in a pediatric hospital
center.
Researchers have known for a long time (e.g., Goris et al., 2010) that more than 60 percent of human communication is not verbal
but rather occurs through facial expressions. Thus, a social robot has to be able to provide emotional communication like a child
specialist. One popular robot that is providing such support is Huggable. With the help of AI, Huggable is equipped to understand
facial expressions, temperament, g estures, and human cleverness. It is like a staff member added to the team of specialists who provide
children some general emotional health assistance.
Huggable looks like a teddy bear having a ringed arrangement. A furry soft body provides a childish look to it and hence is perceived
as a friend by the children. With its mechanical arms, Huggable can perform specific actions quickly. Rather than sporting high-tech
devices, a Huggable robot is composed of an Android device whose microphone, speaker, and camera are in its internal sensors, and a
mobile phone that acts as the central nervous system. The Android device enables the communication between the internal sensors and
teleoperation interface. Its segmental arm components enable an easy replacement of sensors and hence increase its reusability. These
haptic sensors along with AI enable it to process the physical touch and use it expressively.
Sensors incorporated in a Huggable transmit physical touch and pressure data to the teleoperation device or external device via an
IOIO board. The Android device receives the data from the external sensors and transmits them to the motors that are attached to the
body of the robot. These motors enable the movement of the robot. The capacitors are placed at various parts of the robot, known as
pressure points. These pressure points enable the robot to understand the pain of a child who is unable to express it verbally but may be
able to touch the robot to convey the pain. The Android device interprets the physical touch and pressure sensor data in a meaningful
way and responds effectively. The Android phone enables communication between the other devices while keeping the design
minimalistic. The computing power of the robot and the Android device is good enough to allow realtime communication with a child.
Figure 10.1 exhibits a schematic of the Huggable robot.
Huggable has been used with children undergoing treatment at Boston Children’s Hospital. Reportedly, Aurora, a 10-year-old who
had leukemia, was being treated at Dana-Farber/Boston Children’s Cancer and Blood Disorder Center. According to Aurora’s parents,
“There were many activities to do at the hospital but the Huggable being there
Chapter 10 • Robotics: Industrial and Consumer Applications 25
FIGURE 10.1 A General Schematic of a Huggable Robot.
is great for kids.” Beatrice, another child who visits the hospital frequently due to her chronic
condition, misses her classes and friends and is unable to do anything that a typical child of her age
would do. She was nervous and disliked the process of treatment, but during her interplay with the
Huggable, she was more willing to take medicine as if it were the most natural activity to do. She
recommended the robot to be a bit faster so that the next time she could play peek-a-boo correctly.
During these interactions with Huggable, children were seen hugging it, holding its hand,
tickling it, giving it high-fives, and treating it as someone they need for support. Children were polite
with it and used expressions such as “no, thank you” and “one second, please.” In the end, when
bidding it goodbye, one child hugged the Huggable, and another wished to play with it longer.
Another benefit of such emotional support robots is in the prevention of infections. Patients
may have contagious diseases, but the robots are sterilized after each use to prevent infection from
spreading. Thus, Huggable not only provides support to children but also can be a useful tool for
reducing the spread of infectious diseases.
A recently reported study by researchers at MIT’s Media Lab highlighted the differences
between social robots such as Huggable and other virtual interaction technologies. A group of 54
children who were in a hospital were given three distinct social interactions: a cute regular teddy
bear, a virtual persona of Huggable on a tablet, and a social robot. The bear offered a physical model
but not social dialogue. A virtual version of Huggable on the tablet provided linguistic engagement,
conversed with the humans in the same way, and possessed the same features as the robot but was
a 3D virtual version of the Huggable robot. Both the virtual character and robot were operated by
a teleoperator, and hence they perceived the interaction and responded in the same manner.
Children were given one of the three interactions to play with based in groups according to age and
gender. Necessary information was provided to the children by the care specialist, and the virtual
character and robot were handled separately by these specialists just outside the room. IBM
Watson’s tone Analyzer was used to attempt to identify five human emotions and five personality
traits. Interactions with each of these three types of virtual agents by children were videotaped and
analyzed by the researchers. The results of this experiment were quite interesting. These results
showed that the children gazed more at the virtual character and the robot as compared to the bear.
Touches between the children and the virtual agents were the highest with the Huggable robot
followed by the virtual character Huggable and the bear. Also, the children took care of the
Huggable robot and did not push or pull it. Interestingly, a few children responded to the virtual
character on the tablet violently even when it made ouch sounds. The poor teddy bear was thrown
and kicked around playfully. These results show that the children connected with the robotic
Huggable more than the other two options.
SOCIAL ROBOT FOR OLDER ADULTS: PARO
26 Part IV • Robotics, Social Networks, AI and IoT
Major countries in the world will soon have population rates of people aged 65 and older exceed that
of the younger population by 2050. The emotional support older adults need cannot be ignored where
geographical separation and the technological divide have made it difficult for them to connect with
their families. Paro, a social robot, is designed to interact with humans and is marketed as a robot used
for older adults at nursing homes. Paro mainly acts as pet therapy; it can also be immensely useful
where pet therapy becomes inadvisable in hospitals due to the risk of infections.
Paro interprets the human touch, and it can also capture limited speech, express a limited set of
vocal utterances, and move its head. Paro is not a mobile robot and resembles a seal. It was tested at
two nursing homes (Broekens et al., 2009) with 23 patients. The results showed that social robots like
Paro increase the social interaction. Paro not only brought smiles to patients’ faces but also some vivid,
happy experiences to occupants. Even though Paro did not provide a complete response that humans
do, many patients found the responses meaningful and connected emotionally to it. These robots can
help break the monotonous routine of older adults and add some joy to their lives. It provides them
with a feeling of being wanted and self-esteem, lowering stress and anxiety levels.
u QUESTIONS FOR THE OPENING VIGNETTE
1. What characteristics would you expect to have in a robot that provides emotional support to
patients?
2. Can you think of other applications where robots such as the Huggable can play a helpful role?
3. Visit the website https://www.universal-robots.com/case-stories/aurolab/ to learn about
collaborative robots. How could such robots be useful in other settings?
WHAT WE CAN LEARN FROM THIS VIGNETTE
As we have seen in various chapters throughout this book, AI is opening many interesting and unique
applications. The stories about the Huggable and Paro introduce us to the idea of using robots for one
of the most difficult aspects of work – to provide emotional support to patients, both children and
adults. Combinations of technologies such as machine learning, voice synthesis, voice recognition,
natural language processing, machine vision, automation, micromachines, and so on make it possible
to combine these technologies to satisfy many needs. The applications can come entirely in virtual
forms such as IBM Watson, which won the Jeopardy! game implementing industrial automation,
producing self-driving cars, and even providing emotional support as noted in this opening vignette.
We will see many examples of similar applications in this chapter.
Sources: J. Broekens, M. Heerink, & H. Rosendal. (2009). “Assistive Social Robots in Elderly Care: A Review.” Gerontechnology, 8,
pp. 94–103. doi: 10.4017/gt.2009.08.02.002.00; S. Fallon. (2015). “A Blue Robotic Bear to Make Sick Kids Feel Less Blue.”
https://www.wired.com/2015/03/blue-robotic-bear-make-sick-kidsfeel-less-blue/ (accessed August 2018). Also see
the YouTube video at https://youtu.be/UaRCCA2rRR0 (accessed August 2018); K. Goris et al. (2010, September).
“Mechanical Design of the Huggable Robot Probo.” Robotics & Multibody Mechanics Research Group. Brussels, Belgium:
Vrije Universiteit Brussel; S. Jeong et al. (2015). “A Social Robot to Mitigate Stress, Anxiety, and Pain in Hospital Pediatr ic
Care.” Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts; S. Jeong &
D. Logan. (2018, April 21–26). “Huggable: The Impact of Embodiment on Promoting Socio-emotional Interactions for Young
Pediatric Surgeons.” MIT Media Lab, Cambridge, MA, CHI 2018, Montréal, Quebec, Canada.
10.2 OVERVIEW OF ROBOTICS
Every robotics scientist has her or his own view about the definition of robot. But a c ommon notion
of robot is a machine or a physical device or software that with the c ooperation of AI can accomplish
a responsibility autonomously. A robot can sense and affect the environment. Applications of robotics
in our day-to-day lives have been increasing. This evolution and use of technologies are called the fourth
industrial revolution. Applications of robotics in manufacturing, health, and information technology (IT)
fields in the last decade have led to rapid development in changing the future of industries. Robots are
moving from just performing preselected repetitive tasks ( automation) and being unable to react to
Chapter 10 • Robotics: Industrial and Consumer Applications 27
unforeseen circumstances (Ayres and Miller, 1981) to performing specialized tasks in healthcare,
manufacturing, sports, financial services – virtually every industry. This capability of adaptation to new
situations leads to autonomy, a sea change from previous generations of robots. Chapter 2 introduced
a definition of robots and provided some applications in selected industries. In this chapter, we will
supplement that introduction with various applications and take a slightly deeper dive into the topic.
Although our imagination of a robot may be based on the R2D2 or C3-PO from the Star Wars
movies, we have experienced robots in many other ways. Factories have been using robots for a long
time (see Section 10.3) for manufacturing. On the consumer side, an early application was Roomba, a
robot that can clean floors on its own. Perhaps the best example of robots that we will all experience
soon if not already is an autonomous (self-driving) car. Tech Republic called the self-driving car the first
robot we will all learn to trust. We will dig a bit deeper into self-driving vehicles in Section 10.7. With
the growth in machine learning, especially image recognition systems, applications of robots are
increasing in virtually every industry. Robots can cut sausages into the right size pieces for pizza and
can automatically determine that the right number and type of peperoni pieces have been placed on a
pizza before it is baked. Surgeries conducted by and with the assistance of robots are growing at a
rapid pace. Section 10.4 provides many illustrative applications of robots. Then Section 10.7 discusses
self-driving cars as another category of robots.
u SECTION 10.2 REVIEW QUESTIONS
1. Define robot.
2. What is the difference between automation and autonomy?
3. Give examples of robots in use. Find recent applications online and share with the class.
10.3 HISTORY OF ROBOTICS
Wikipedia includes an interesting history of robotics. Humans have been fascinated with the idea of
machines serving us for a long time. The first idea of robotics was conceptualized in 320 BC when
Aristotle, a Greek philosopher, stated, “If every tool, when ordered, or even of its own accord, could
do the work that befits it, then there would be no need either of apprentices for the master workers
or of slaves for the lords.” In 1495, Leonardo Da Vinci drafted strategies and images for a robot that
looked like a human. Between 1700 and 1900, various automatons were created, including an excellent
automation structure built by Jacques De Vaucanson, who made one clockwork duck that could flap
its wings, quack, and appear to eat and digest food.
Throughout the industrial revolution, robotics was triggered by the advances in steam power
and electricity. As consumer demand increased, engineers strove to devise new methods to increase
production by automation and create machines that can perform the tasks that were dangerous for a
human to do. In 1893, “Steam Man,” a prototype for a humanoid robot, was proposed by Canadian
professor George Moore. It was composed of steel and powered by a steam engine. It could walk
autonomously at nearly nine miles per hour and could even pull relatively light loads. In 1898, Nikola
Tesla exhibited a submarine prototype. These events led to the integration of robotics in
manufacturing, space, defense, aerospace, medicine, education, and entertainment industries.
In 1913, the world’s first moving conveyor belt assembly line was started by Henry Ford. With
the aid of a conveyor belt, a car could be assembled in 93 minutes. Later in 1920, the term robot was
coined by Karel Capek in his play Rossum’s Universal Robots.
Then a toy robot, Lilliput, was manufactured in Japan.
By the 1950s, innovators were creating machines that could handle dangerous, repetitive tasks
for defense and industrial manufacturing. Since the robots were primarily designed for heavy-duty
industries, they were required to pull, lift, move, and push the same way humans did. Thus, many
robots were designed like a human arm. Examples include a spray-painting gadget for a position-
28 Part IV • Robotics, Social Networks, AI and IoT
controlling apparatus by W. L. V. Pollard in 1938. DeVilbiss Company acquired this robot and later
became a leading supplier of the robotic arms in the United States.
In the mid-1950s, the first commercial robotic arm, Planetbot, was developed, and General
Motors later used it in a manufacturing plant for the production of radiators. A total of eight Planetbots
were sold. According to the company, it could perform nearly 25 movements and could be reset in
minutes to perform another set of operations. However, Planetbot did not achieve the desired results
due to the unusual behavior of the hydraulic fuel inside it.
George Devol and Joe Engelberger designed Unimate to automate the manufacturing of TV
picture tubes. It weighed close to 4,000 pounds and was controlled by preprogrammed commands fed
on a magnetic drum. Later this was used by General Motors Corporation for production to sequence
and stack hot die-cast metal components. This arm with specific upgrades became one of the famous
features in assembly lines. A total of 8,500 machines was sold, and half of them went to the automotive
industries. Later Unimate was modified to perform spot welding, die casting, and machine tool
stacking.
In the 1960s, Ralph Mosher and his team created two remotely operated robotic arms,
Handyman and Man-mate. A Handyman was a two-arm electro-hydraulic robot, and the design of the
Man-mate’s arm was based on the human spine. The arms gave the robots the flexibility for artifact
examination procedures. The fingers were designed in a way that they could grasp objects via a single
command.
New mobile robots came into the picture. The first one, Shakey, was developed in 1963. It could
move freely, avoiding obstacles in its path. A radio antenna was attached to its head. It had a vision
sensor atop a central processing unit. Shakey was attached to two wheels, and its two sensors could
sense obstacles. Using logic-based problem solving, it could recognize the shape of objects, move
them, or go around them.
The space race started by Russia’s Sputnik and embraced by the United States led to many
technology advances leading to growth of robotics. In 1976, during NASA’s mission to Mars, a Viking
lander was created for the atmospheric conditions of Mars. Its arms opened out and created a tube to
gather samples from the Mars surface. There were some technical issues during the mission, but the
scientists were able to fix them remotely.
In 1986, the first LEGO-based educational products were put on the market by Honda. In 1994,
Dante II, an eight-legged walking robot built by Carnegie Mellon University, collected the volcanic gas
sample from Mount Spur.
Robotics expanded exponentially as more research and money were invested. Robotic
applications and research spread to Japan, Korea, and European nations. It is estimated that by 2019,
there will be close to 2.6 million significant robots. Robots have applications in the fields of social
support, defense, toys and entertainment, healthcare, food, and rescue. Many robots are now moving
into next stages, going from deep-sea to interplanetary and extrasolar research. And as noted, self-
driving cars will bring robots to the masses. We review several robot applications in the following
sections.
u SECTION 10.3 REVIEW QUESTIONS
1. Identify some of the key milestones in the history of manufacturing that have led to the current
interest in robotics.
2. How would Shakey’s capabilities compare to today’s robots?
3. How have robots helped with space missions?
10.4 ILLUSTRATIVE APPLICATIONS OF ROBOTICS
This section highlights examples of robot applications in various industries. Each of these is presented
as a mini-application case, with the discussion questions presented at the end of the section.
Chapter 10 • Robotics: Industrial and Consumer Applications 29
Changing Precision Technology
A mobile production company in China, Changing Precision Technology switched to the use of
robotic arms to produce parts for mobile phones. The company previously employed 650 workers to
operate the factory. Now, robots perform most of its operations, and the company has reduced its
workforce to 60, decreasing the human workforce by 90 percent. In the future, the company intends
to drop its employee count to about 20. With the robots in place, the company not only has achieved
an increase in production of 250 percent but also cut the defect levels from 25 percent to a mere 5
percent.
Compiled from C. Forrest. (2015). “Chinese Factory Replaces 90% of Humans with Robots, Production Soars.” TechRepublic.
https://www.techrepublic.com/article/chinese-factory-replaces-90-of-humans-with-robotsproduction-soars/
(accessed September 2018); J. Javelosa & K. Houser. (2017). “Production Soars for Chinese Factory Who Replaced 90% of
Employees with Robots.” Future Society. https://futurism.com/2-productionsoars-for-chinese-factory-who-replaced-
90-of-employees-with-robots/ (accessed September 2018).
Adidas
Adidas is a worldwide leading sportswear manufacturer. Keeping trends, innovation, and
customization in mind, Adidas has started to automate factories such as Speedfactory in Ansbach,
Germany, and Atlanta, Georgia. A conventional supply chain from the raw materials to final product
takes around two months, but with automation, it takes just a few days or weeks. The implementation
of robotics there was different from that of other manufacturing industries because the raw materials
used in shoes manufactured by Adidas are soft textile materials. Adidas is working with the company
Oechsler to implement the robotics in its supply chain. Adidas uses technologies such as additive
manufacturing, robotic arms, and computerized knitting. At the Speedfactory, the robot that makes a
part of a sneaker attaches a scannable QR code to the part. During quality check, if any part of the
product turns out to be faulty, the robot that created it is thus traceable and repaired. Adidas has
optimized this process, which offers the company the option to roll out a few thousands of customized
shoes in the market and see how it performs and optimize the process accordingly. In the next few
years, the company plans to roll out around 1 million pairs of the custom styles annually. In the long
term, this strategy supports moving from manufacturing large stocks of inventory to creating the
products on demand.
Compiled from “Adidas’s High-Tech Factory Brings Production Back to Germany.” (2017, January 14). The Economist.
https://www.economist.com/business/2017/01/14/adidass-high-tech-factory-brings-production-back-togermany
(accessed September 2018); D. Green. (2018). “Adidas Just Opened a Futuristic New Factory – and It Will Dramatically Change
How Shoes Are Sold.” Business Insider. http://www.businessinsider.com/adidas-hightech-speedfactory-begins-
production-2018-4 (accessed September 2018).
BMW Employs Collaborative Robots
The increased use of AI and automation in industries has resulted in the development of robots. Yet,
human cognitive capabilities are irreplaceable. The combination of robots and humans has been
achieved using collaborative robots at a BMW manufacturing unit. By doing so, the company has
maximized the efficiency of its production unit and modernized the work environment.
BMW’s Spartanburg, South Carolina, plant has employed 60 collaborative robots that work side
by side with its human workforce. These robots, for example, furnish the interior of BMW car doors
with sound and moisture insulation. This sealing protects the electronic equipment that is fixed on the
door and the vehicle as a whole from moisture. Previously, human workers performed this intensive
task of fixing the foil with the adhesive beads by using a manual roller. With the use of cobots, a robot’s
arms perform this task with precision. Cobots run on low speed and stop immediately as soon as the
sensors detect any obstacle in their way to maintain the safety of assembly-line workers.
At BMW’s Dingolfing factory located in Germany, a lightweight cobot is ceiling mounted in the
axle transmission assembly area to pick up bevel gears. These gears can weigh up to 5.5 kilos. The
cobot fits the bevel gears accurately, avoiding damage to the gear wheels.
30 Part IV • Robotics, Social Networks, AI and IoT
Compiled from M. Allinson. (2017, March 4). “BMW Shows Off Its Smart Factory Technologies at Its Plants Worldwide.”
BMW Press Release. Robotics and Automation. https://roboticsandautomationnews.com/2017/03/04/ bmw-shows-off-
its-smart-factory-technologies-at-its-plants-worldwide/11696/ (accessed September 2018); “Innovative Human-Robot
Cooperation in BMW Group Production.” (2013, October 9). https://www.press.
bmwgroup.com/global/article/detail/T0209722EN/innovative-human-robot-cooperation-in-bmw-
groupproduction?language=en (accessed September 2018).
Tega
Tega is a social bot intended to provide extended support to preschoolers by engaging them via
storytelling and offering help with vocabulary. Like Huggable, Tega is an Android-based robot and
resembles an animation character. It has an external camera and onboard speakers and is designed to
run for up to six hours before needing a recharge. Tega uses Android capabilities for expressive eyes,
computation abilities, and physical movements. Children’s response is fed to the Tega as a reward
signal into a reinforcement learning algorithm. Tega uses a social controller, sensor processing, and
motor control for moving its body and tilting and rotating left or right.
Tega is designed not only to tell stories but also to hold a conversation about the stories. With
the help of an app on a tablet, Tega interacts with a child as a peer and teammate, not as an educator.
Children communicate with the tablet, and Tega provides the feedback and reactions by watching the
children’s emotional states. Tega also offers help with vocabulary and understands a child’s physical
and emotional responses, enabling it to build a relationship with the child. The tests have shown that
Tega can positively impact a child’s interest in education, free thinking, and mental development. For
more information, watch the video at https://www.youtube.com/ watch?v=16in922JTsw.
Compiled from E. Ackerman. (2016). IEEE Spectrum. http://spectrum.ieee.org/automaton/robotics/homerobots/tega-
mit-latest-friendly-squishable-social-robot (March 5, 2017); J. K. Westlund et al. (2016). “Tega: A Social Robot.” Video
Presentation. Proceedings of the Eleventh ACM/IEEE International Conference on Human Robot Interaction; H. W. Park et al. (2017).
“Growing Growth Mindset with a Social Robot Peer.” Proceedings of the Twelfth ACM/IEEE International Conference on Human Robot
Interaction; Personal Robots Group. (2016). https://www.youtube.com/watch?v=sF0tRCqvyT0 (accessed September 2018);
Personal Robots Group, MIT Media Lab. (2016). AAAS. https://www.eurekalert.org/pub_releases/2016-03/nsf-
rlc031116.php (accessed September 2018).
San Francisco Burger Eatery
Flipping burgers is considered a low-pay, mundane task that provides many people with employment
at a low salary. Such jobs are likely to disappear over time because of robots. One such implementation
of robotics in the food industry is at a burger restaurant in San Francisco. The burger-making machine
is not a traditional robot sporting arms and legs that can move around and work as a human. Instead,
it is a complete burger prep device that can work from prepping a burger for cooking and bringing
together a full meal. It blends the robotic power in bringing the right taste with the help of a
Michelinstar chef’s recipes and being friendly on the pocket. The restaurant has put in place two 14-
foot-long machines that can make around 120 burgers per hour. Each machine has 350 sensors, 20
computers, and close to 7,000 parts.
Buns, onions, tomatoes, pickles, seasoning, and sauces are filled in transparent tubes over a
conveyor belt. Once an order is placed via a mobile device, it takes close to five minutes to prepare
the order. First, air pressure pushes a burger brioche roll from the transparent tube on the conveyor
belt. Different components of the robot work one after the other to prepare the order, from slicing
the roll in two halves, applying butter on the bun, shredding vegetables, and dropping the sauces. Also,
a light specialized grip is placed on the patty to keep it intact and to bake it per the recipe. With the
use of thermal sensors and an algorithm, the cooking time and temperature of the patty are determined,
and once cooked, the patty is placed on the bun by a robotic arm. Workers receive a notification via
an Apple watch when there is an issue with the machine regarding a malfunction on an order or the
need for refills on supplies.
Compiled from “A Robot Cooks Burgers at Startup Restaurant Creator.” (2018). TechCrunch. https://techcrunch.
com/video/a-robot-cooks-burgers-at-startup-restaurant-creator/ (accessed September 2018); L. Zimberoff. (2018, June
Chapter 10 • Robotics: Industrial and Consumer Applications 31
21). “A Burger Joint Where Robots Make Your Food.” https://www.wsj.com/articles/a-burgerjoint-where-robots-make-
your-food-1529599213 (accessed September 2018).
Spyce
Using robots to make affordable foods is demonstrated by a fast-food restaurant operating in Boston
that serves grain dishes and salad bowls. Spyce is a budget-friendly restaurant founded by MIT
engineering graduates. Michael Farid created the robots that can cook. This restaurant employs few
people with good pay and employs robots to do much of the fast-food work.
Orders are placed at a kiosk with touch screens. Once the order is confirmed, the mechanized
systems start preparing the food. Ingredients are placed in refrigerated bins that are passed via
transparent tubes and are collected using a mobile device that delivers the ingredients to the requested
pot. A metal plate attached to the side of the robotic pot heats the food. A temperature of about 450
degrees Fahrenheit is maintained, and the food is tumbled for nearly two minutes and cooked. This
resembles clothes being washed in a machine. Once the meal is ready, the robotic pot tilts and transfers
the food to a bowl. After each cooking round, the robotic pot washes itself with a high-pressure hot
water stream and then returns to its initial position, ready to cook the next meal. The customer name
is also added to the bowl. The meal is then served by a human after any final changes. Spyce is also
trying to put in place a robot that can cook pancakes.
Compiled from B. Coxworth. (2018, May 29). “Restaurant Keeps Its Prices Down – With a Robotic Kitchen.” New Atlas.
https://newatlas.com/spyce-restaurant-robotic-kitchen/54818/ (accessed September 2018); J. Engel. (2018, May 3).
“Spyce, MIT-Born Robotic Kitchen Startup, Launches Restaurant: Video.” Xconomy.
https://www.xconomy.com/boston/2018/05/03/spyce-mit-born-robotic-kitchen-startup-launchesrestaurant-
video/ (accessed September 2018).
Mahindra & Mahindra Ltd.
As the population increases, the agricultural industry is expanding to keep up with demand. To keep
increasing the food supply at a reasonable cost and to maintain quality, the Indian multinational firm
Mahindra & Mahindra Ltd. is seeking to improve the process of harvesting tabletop grapes. The
company is establishing a research and development center at Virginia Polytechnic Institute and
University. It will work with other Mahindra centers situated in Finland, India, and Japan.
The grapes can be used for juice, wine, and tabletop grapes. The quality that must be maintained
is vastly different for each of these. The ripeness and presentation of tabletop grapes differ from the
other two uses; hence, quality control is critical. Deciding which grapes are ready to pick is a labor-
intensive approach, and one must ensure the maturity, consistency, and quality of grapes. Making this
decision visually requires expert training, which is not easily scalable. Using robotic harvesting instead
of human pickers is being explored. Robots can achieve these goals using sensors that will keep the
quality in view while speeding the process.
Compiled from L. Rosencrance. (2018, May 31). “Tabletop Grapes to Get Picked by Robots in India, with Help from Virginia
Tech.” RoboticsBusinessReview. https://www.roboticsbusinessreview.com/agriculture/ tabletop-grapes-picked-
robots-india-virginia-tech/ (accessed September 2018); “Tabletop Grapes to Get Picked by Robots in India.”
Agtechnews.com. http://agtechnews.com/Ag-Robotics-Technology/ Tabletop-Grapes-to-Get-Picked-by-Robots-
in-India.html (accessed September 2018).
Robots in the Defense Industry
For obvious reasons, the military has invested in robotic applications for a long time. Robots can
replace humans in places where risk of loss of human life is too great. Robots can also reach areas
where humans may not be able to go due to extreme conditions – heat, water, and so forth. Besides
the recent growth of drones in military applications, several specific robots have been developed over
a long time. Some are highlighted in the next sections.
32 Part IV • Robotics, Social Networks, AI and IoT
MAARS MAARS (Modular Advanced Armed Robotic System) is an upgraded version of special
weapons observation reconnaissance detection system (SWORDS) robots that were used by the U.S.
military during the Iraq war. It is designed for reconnaissance, surveillance, and target acquisition and
can have a 360@degree view. Depending on the circumstances, MAARS can drape much firepower
into its tiny frame. A variety of ammunition such as tear gas, nonlethal lasers, and grenade launcher
can be wrapped in it. MAARS is an army robot that can fight autonomously thus reducing risk to
soldiers' lives while also protecting itself. This robot has seven types of sensors to track the heat
signature of an enemy during the day and night. It uses night vision cameras to monitor enemy
activities during the night. On command, MAARS fires at opponents. Its other uses include moving
heavy loads from one place to another. It provides a range of options from nonlethal force such as
warning of an attack. It can also form a two-sided communication system. The robot can also use less
lethal weapons such as laughing gas, pepper spray, and smoke and start clusters to disperse crowds.
The robot can be controlled from about one kilometer and is designed to increase or decrease speed,
climb stairs, and walk on nonpaved roads using wheels rather than tracks.
Compiled from T. Dupont. (2015, October 15). “The MAARS Military Robot.” Prezi. https://prezi.com/ fsrlswo0qklp/the-
maars-military-robot/ (accessed September 2018); Modular Advanced Armed Robotic System. (n.d.). Wikipedia.
https://en.wikipedia.org/wiki/Modular_Advanced_Armed_Robotic_System (accessed September 2018); “Shipboard
Autonomous Firefighting Robot – SAFFiR.” (2015, February 4). YouTube.
https://www.youtube.com/watch?time_continue=252&v=K4OtS534oYU (accessed September 2018).
SAFFIR (SHIPBOARD AUTONOMOUS FIREFIGHTING ROBOT) Fire on a ship is one of the greatest risks to
shipboard life. Shipboard fires have a different and crucial set of problems. Because of the confined
space, there are challenges regarding smoke, gas, and limited ability to escape. Even though procedures
like fire drills, onboard alarms, fire extinguishers, and other measures provide ways of dealing with fire
on the sea, modern technology is in place to tackle this threat in a better way. A U.S. Navy team at the
Office of Naval Research has developed SAFFiR. It is a 5 foot 10 inch tall robot. It is not designed to
be completely autonomous. It has a humanoid robotic structure so that it can pass through confined
aisles and other nooks and corners of a ship and climb ladders. The robot has been designed to work
with the obstacles in the passageways in a ship. SAFFiR can use protective fire gear such as fire-
protective coats, suppressants, and sensors that are designed for humans. Lightweight and low-friction
linear actuators improve its efficiency and control. It is equipped with several sensors: regular camera,
gas, and infrared camera for night vision and in black smoke. Its body is designed not only to be fire
resistant but also to throw extinguishing grenades. It can work for around half an hour without needing
a charge. SAFFiR can also balance itself on an uneven surface.
Compiled from K. Drummond. (2012, March 8). “Navy’s Newest Robot Is a Mechanized Firefighter.” wired. com.
https://www.wired.com/2012/03/firefight-robot/ (accessed September 2018); P. Shadbolt. (2015, February 15). “U.S.
Navy Unveils Robotic Firefighter.” CNN. https://www.cnn.com/2015/02/12/tech/mcisaffir-robot/index.html
(accessed September 2018); T. White. (2015, February 4). “Making Sailors ‘SAFFiR’ – Navy Unveils Firefighting Robot
Prototype at Naval Tech EXPO.” America’s Navy. https://www.navy.mil/ submit/display.asp?story_id=85459 (accessed
September 2018).
Pepper
Pepper is a semihumanoid robot manufactured by SoftBank Robotics that can understand human
emotions. A screen is located on its chest. It can identify frowning, tone of voice, smiling, and user
actions such as the angle of a person’s head and crossed fingers. This way Pepper can determine if a
person’s mood is good or bad. Pepper can walk autonomously, recognize individuals, and can even lift
their mood through its conversation.
Pepper has a height of 120 cms (about 4 feet). It has three directional wheels attached, enabling
it to move all around the place. It can tilt its head and move its arms and fingers and is equipped with
two high-definition cameras to understand the environment. Because of its anticollision functionalities,
Pepper reduces unexpected collisions and can recognize humans as well as obstacles nearby. It can
Chapter 10 • Robotics: Industrial and Consumer Applications 33
also remember human faces and accepts smartphone and card payments. Pepper supports commands
in Japanese, English, and Chinese.
Pepper is deployed in service industries as well as homes. It has several advantages for effectively
communicating with customers but has also been criticized at places for incompetence or security
issues. The following examples provide information on its applications and drawbacks:
• Interacting with robots while shopping is changing the face of AI in commercial settings. Nestlé
Japan, a leading coffee manufacturer, has employed Pepper to sell Nescafé machines to enhance
customer experience. Pepper can explain the range of products Nestlé has to offer and recognize
human responses using facial recognition and sounds. Using a series of questions and responses
to them, the robot identifies a consumer’s need and can recommend the appropriate product.
• Some hotels such as Courtyard by Marriott and Mandarin Oriental are employing Pepper to
increase customer satisfaction and efficiency. The hotels use Pepper to increase customer
engagement, guide guests toward activities that are taking place, and promote their reward
programs. Another goal is to collect customer data and fine-tune the communication according
to customer preferences. Pepper was deployed steps away from the entry at Disneyland theme
park hotels, and it immediately increased customer interactions. Hotels use Pepper to converse
with guests while they are checking in or out or to guide them to the spa, gym, and other
amenities. It can also inform guests about campaigns and promotions and help staff members
avoid the mundane task of enrolling guests in a loyalty program. Customer reactions are largely
quite positive in regard to this.
• Central Electric Cooperative (CEC), an electric distribution cooperative located in Stillwater,
Oklahoma, has installed Pepper to monitor outages. CEC serves more than 20,000 customers
in seven counties in Oklahoma. Pepper is connected to the operations center to read information
about live outages, and by connecting them to geographic information system (GIS) maps it can
also inform operations about the live locations of service trucks. At CEC, Pepper is also used
for conferences where attendees can know more about the company and its services. Pepper
answers a range of questions regarding energy consumption. In the future, the company plans
to invest more in robots to meet its requirements. See Figure 10.2 that shows Pepper
participating as a team member during a prospective employee interview to provide input about
CEC’s programs and so on.
• Fabio, a Pepper robot, was installed as a retail assistant at an upmarket food and wine store in
England and Scotland. A week after implementing it, the store pulled the service because it was
confusing customers, and they preferred the service from personal staff rather than Fabio. It
provided generic answers on queries such as the shelf location of items. However, it failed to
understand completely what the customer was requesting due to background noise. Fabio was
provided another chance by placing it in a specific area that attracted only a few customers. Then
they also complained about Fabio’s inability to move around the supermarket and direct them
to a specific section. Surprisingly, the staff at the market became accustomed to Fabio rather
than considering it as a competitor.
34 Part IV • Robotics, Social Networks, AI and IoT
FIGURE 10.2 Pepper Robot as a Participant in a Group Meeting. Source: Central Electric Cooperative.
• Pepper has several security concerns that were pointed out by Scandinavian research-ers.
According to them, it is easy to have unauthenticated root-level access to the bot. They also
found the robot to be prone to brute force attack. Pepper’s functions can be programmed using
various application programming interfaces (APIs) through languages such as Python, Java, and
C + +. This feature can cause it to provide access to all its sensors, making it not secure. An
attacker can establish a connection and then use Pepper’s mic, camera, and other features to spy
on people and their conversations. This is an ongoing issue for many robots and smart speakers.
Compiled from “Pepper Humanoid robot helps out at hotels in two of the nation’s most-visited destinations (2017)”. SoftBank
Robotics. https://usblog.softbankrobotics.com/pepper-heads-to-hospitality-humanoidrobot-helps-out-at-hotels-in-
two-of-the-nations-most-visited-destinations (accessed November 2018); R. Chirgwin. (2018, May 29). “Softbank’s ‘Pepper’
Robot Is a Security Joke.” The Register. https://www.theregister.
co.uk/2018/05/29/softbank_pepper_robot_multiple_basic_security_flaws/ (accessed September 2018); A. France.
(2014, December 1). “Nestlé Employs Fleet of Robots to Sell Coffee Machines in Japan.” The Guardian.
https://www.theguardian.com/technology/2014/dec/01/nestle-robots-coffee-machines-japan-georgeclooney-
pepper-android-softbank (accessed September 2018); Jiji. (2017, November 21). “SoftBank Upgrades Humanoid Robot
Pepper.” The Japan Times. https://www.japantimes.co.jp/news/2017/11/21/business/tech/ softbank-upgrades-
humanoid-robot-pepper/#.W6B3qPZFzIV (accessed September 2018); C. Prasad. (2018, January 22). “Fabio, the Pepper
Robot, Fired for ‘Incompetence’ at Edinburgh Store.” IBN Times. https://www. ibtimes.com/fabio-pepper-robot-fired-
incompetence-edinburgh-store-2643653 (accessed September 2018).
Da Vinci Surgical System
Over the last decade, the use of robotics has emerged in surgeries. One of the most famous robotic
systems used in surgery is the Da Vinci system that has performed thousands of surgeries. According
to surgeons, Da Vinci is the most ubiquitous robot used in more units than any other robot. It is
designed to perform numerous nominally invasive operations and can perform simple as well as
complex and delicate surgeries. The critical components of Da Vinci are the surgeon console, patient
side cart, endowrist instruments, and vision system.
The surgeon console is where the surgeon operates the machine. It provides a highdefinition,
3D image of the inside of the patient’s body. The console has master controls that a surgeon can grasp
by the robotic fingers and operate on the patient. The movements are accurate and in real time, and
the surgeon is entirely in control and can prevent the robotic fingers from moving by themselves. The
Chapter 10 • Robotics: Industrial and Consumer Applications 35
patient side cart is the location where the patient resides during the operation. It has either three or
four arms attached that the surgeon controls using master controls, and each arm has certain fixed
pivot points around which the arms move. The third component is the endowrist instruments, which
are available while performing surgery. They have a total of seven degrees of freedom, and each
instrument is designed for a specific purpose. Levers can be released quickly for a change of
instruments. The last component is a vision system, which has a high-definition, 3D endoscope and
image-processing device that provides real-life images of the patient’s anatomy. A viewing monitor
also helps the surgeon by providing a broad perspective during the process.
Patients who have surgery that used the Da Vinci system heal faster than those performed by
traditional methods because the cuts by robotic arms are quite small and precise. A surgeon must
undergo online and hands-on training and must perform at least five surgeries in front of a surgeon
who is certified to use the Da Vinci system. This technology does increase the cost of the surgery, but
its ability to ease pain while increasing precision makes it the future of such procedures.
Compiled from “Da Vinci Robotic Prostatectomy – A Modern Surgery Choice!” (2018). Robotic Oncology.
https://www.roboticoncology.com/da-vinci-robotic-prostatectomy/ (accessed September 2018); “The da Vinci®
Surgical System.” (2015, September). Da Vinci Surgery. http://www.davincisurgery.com/da-vincisurgery/da-vinci-
surgical-system/ (accessed September 2018).
Snoo – A Robotic Crib
Snoo, a robotic, Wi-Fi-enabled crib was developed by Yves Behar, pediatrician Dr. Harvey Karp, and
MIT–trained engineers. According to its designers, Snoo mimics Dr. Karp’s famous sleep strategy
called the five S’s, which implies swaddled, side or stomach position, shush, swing, and suck. Snoo is
an electrified crib that puts babies to sleep automatically. It recreates sensations experienced by the
child during the last trimester of a pregnancy. Infants are at maximum ease when they hear white noise,
feel movements, and are wrapped, which Snoo provides at par. Once a baby is securely attached to
the bassinet, Snoo senses whether it is fussy, keeps track of its movements, and, if found, moves the
crib in a womblike motion until the baby calms down. An app can be installed on Snoo’s smartphone
to control its speed and white noise. Also, Snoo can be turned off after eight minutes or can continue
rocking through the night. The company advertises it as the safest bed ever made with a built-in
swaddling strap that ensures that the child does not move from his or her back. Snoo prevents parents
from getting up several times in the night to do this themselves; hence, it gives them a sound sleep.
Compiled from S. M. Kelly. (2017, August 10). “A Robotic Crib Rocked My Baby to Sleep for Months.” CNN Tech.
https://money.cnn.com/2017/08/10/technology/gadgets/snoo-review/index.html (accessed September 2018); L. Ro. (2016, October 18). “World’s First Smart Crib SNOO Will Help Put Babies to Sleep.” Curbed. https://
www.curbed.com/2016/10/18/13322582/snoo-smart-crib-yves-behar-dr-harvey-karp-happiest-baby (accessed
September 2018).
MEDi
MEDi, short for Machine and Engineering Designing Intelligence, is available at six hospitals in
Canada and one in the United States. MEDi helps reduce stress in children from painful surgeries,
tests, and injections. It is two feet tall and weighs around 11 pounds. It looks like a toy. Dr. Tanya
Beran proposed using MEDi after working in hospitals where she heard children exclaiming with joy
at the sight of the robot. She suggests that since there is not enough pain management expertise
available in such situations, technology can provide a helping hand. The robot can speak 19 languages
and can easily be integrated into various cultures. Aldebaran built this robot, which calls itself NAO.
It can cost $8,000 and more. Beran bought MEDi to life by adding software that could operate in
hospital settings with kids. MEDi strikes up conversations with the kids during a variety of procedures.
It was first programmed for flu vaccines and since has been used in other tests. MEDi can even tell
story to a child. The robot helps not only children but also nurses by lowering children’s stress and
relaxing them. Parents have said that when children leave the hospital, they did not speak about needles
and pain but in fact left with happy memories.
36 Part IV • Robotics, Social Networks, AI and IoT
Compiled from A. Bereznak. (2015, January 7). “This Robot Can Comfort Children Through Chemotherapy.” Yahoo Finance.
https://finance.yahoo.com/news/this-robot-can-comfort-children-through-107365533404.html (accessed September
2018); R. McHugh & J. Rascon. (2015, May 23). “Meet MEDi, the Robot Taking Pain Out of Kids’ Hospital Visits.” NBC News.
https://www.nbcnews.com/news/us-news/meet-medi-robot-takingpain-out-kids-hospital-visits-n363191 (accessed
September 2018).
Care-E Robot
Airports are growing in size and the number of people who go to them, and this has increased air
traffic, flight cancellations, and gate switches, causing travelers to run to different boarding gates. KLM
Royal Dutch Airlines is trying a new way to ease this process from problems related to security,
boarding gates, and hectic travel with the use of the blue bot “Care-E Robot.” This service is scheduled
to launch at international airports in New York and San Francisco. This robot could be found at
security checkpoints and take travelers and their carry-on luggage wherever they need to go. Through
its nonverbal sounds and signals, Care-E directs travelers to scan their boarding passes and, once
scanned, is at their service when they are busy strolling the shops or using restrooms. Care-E also
avoids collision using eight sensors with its “peripheral collision avoidance.” One of its best features
is to relate boarding gate changes to travelers and provide them transportation to the newly assigned
gate.
Care-E Robot can carry luggage weighing up to 80 pounds. It runs at a speed of 3 mph, which
might be a little too slow for someone running late to catch a flight. However, early travelers who want
to explore the airport can use Care-E on a free trial for two days. Implementations of robots like these
have not yielded the desired results due to frequent changes in airport policies regarding batteries, but
the market for such a robot is quite optimistic about its future.
Compiled from M. Kelly. (2018, July 16). “This Adorable Robot Wants to Make Air Travel Less Stressful.” The Verge.
https://www.theverge.com/2018/7/16/17576334/klm-royal-dutch-airlines-robot-travel-airport (accessed September
2018); S. O’Kane. (2018, May 17). “Raden is the Second Startup to Bite the Dust After Airlines Ban Some Smart Luggage .”
Circuit Breaker. https://www.theverge.com/circuitbreaker/2018/5/17/17364922/radensmart-luggage-airline-ban-
bluesmart (accessed September 2018).
AGROBOT
The combination of sweetness loaded with multiple health benefits makes strawberries one of the
world’s most popular and consumed fruits. Close to 5 million tons of strawberries are harvested every
year, an upward trend in the United States, Turkey, and Spain as top harvesters. AGROBOT, a
company engaged in the business of agricultural robots, has developed a robot that can harvest
strawberries at any place. Robots using 24 robotic manipulators built on a mobile platform work to
identify superior quality strawberries.
Strawberries require a high degree of care because they are delicate compared to other fruits.
Fruits such as apples, bananas, and mangoes ripen after being picked whereas strawberries are picked
at their full maturity. Hence, harvesting strawberries has been an entirely manual process until recently.
AGROBOT was developed in Spain; this robot performs automated processes except selecting the
strawberries and packing them. To protect strawberries from being squeezed during picking, the robot
cuts them with two razor-sharp blades and catches them in baskets lined with rubber rolls. Once full,
the baskets are placed on a conveyor belt and passed to the packing station. Human operators can
directly select and pack the berries.
AGROBOT is operated by one man, and a maximum of two people can ride on it. Robotic
arms control the coordination between blades and basket. The robot has four main components:
inductive sensors, ultrasonic sensors, a collision control system, and a camera system. Camera-based
sensors view each fruit and analyze it for ripeness according to its form and color; once a berry is ripe,
the robot cuts it from its branches with precise movements. Each arm is fortified with two inductive
sensors to stop at the end positions. The collision control system must be capable of responding to
Chapter 10 • Robotics: Industrial and Consumer Applications 37
dust, temperature change, vibration, and shock; hence, an ultrasonic sensor is attached to the robot to
prevent the arms from touching the ground. Each wheel is equipped with ultrasonic sensors to
determine the distance between the strawberry and the robot’s current position. These sensors also
help in keeping the robot on track and preventing damage to the fruit. Signals received from the
sensors are continuously transmitted to an automatic steering system to regulate the position of wheels.
Compiled from “Berry Picking at Its Best with Sensor Technology.” Pepperl+Fuchs.
https://www.pepperlfuchs.com/usa/en/27566.htm (accessed September 2018); R. Bogue. (2016). “Robots Poised to
Revolutionise Agriculture.” Industrial Robot: An International Journal, 43(5), pp. 45–456; “Robots in Agriculture.” (2015, July 6).
Intorobotics. https://www.intorobotics.com/35-robots-in-agriculture/ (accessed September 2018).
u SECTION 10.4 REVIEW QUESTIONS
1. Identify applications of robots in agriculture.
2. How could a social support robot such as Pepper or MEDi be useful in healthcare?
3. Based on the illustrative applications of robots in this section, build a matrix where the rows are
the robots’ capabilities and the columns are industries. What similarities and differences do you
observe across these robots?
10.5 COMPONENTS OF ROBOTS
Depending on their purpose, robots are made of different components. However, all robots have
some common ones, and others are tweaked according to a robot’s purpose. Figure 10.3 identifies the
components. Common components of the robots are described next.
38 Part IV • Robotics, Social Networks, AI and IoT
POWER CONTROLLER A power controller is the driving force of a robot. Most robots run on batteries,
but a few are powered by a direct current (DC) electrical supply. Other factors (i.e., usage, sufficient
power to drive all parts) must be kept in mind while designing robots.
SENSORS Sensors are used to direct a robot in its surrounding. Force sensors, ultrasound sensors,
distance sensors, laser scanners, and so on help robots to make decisions according to their
environment. Sensors are used for robots to identify speech, vision, temperature, position, distance,
touch, force, sound, and time. Vision sensors or cameras are used to build a picture of the environment
and for the robot to learn about it and to differentiate between which items to choose and which to
ignore. In collaborative robots, sensors are also used to prevent them from bumping into humans or
other robots. This way, humans and robots can work next to each other without the fear that the robot
might unintentionally harm the human. Sensors collect information and send it to the central
processing unit (CPU) electronically.
EFFECTORS OR ROVER OR MANIPULATOR An effector is nothing but a body of a robot. It can also describe
the devices that affect the environment, such as hands, legs, arms, bodies, and fingers. The CPU
Chapter 10 • Robotics: Industrial and Consumer Applications 39
controls the actions of effectors. An essential function of them is to move the robot and other objects
from one place to another, and their characteristics depend on the role that has been outlined.
Industrial robots have end effectors that contribute to the robot’s work as a hand. Depending on the
type of robot, end effectors can be magnets, welding torches, or vacuums.
NAVIGATION OR ACTUATOR SYSTEM Actuators are devices that define how a robot travels. With the help
of an actuator, electrical energy converts into mechanical energy, enabling the robot to move back,
forward, left, right and to lift, drop, and perform its job. The actuator can be a hydraulic cylinder or
an electric motor. The actuator system is the way that all of the robot’s components are embedded
into one.
CONTROLLER/CPU This is the brain of the robot and has the AI embedded in it. The CPU allows a robot
to perform its function by connecting all systems into one. It also provides commands for the robot
to learn from the surrounding movement of the body or any of its actions.
u SECTION 10.5 REVIEW QUESTIONS
1. What are the common components of a robot?
2. What is the function of sensors in a robot?
3. How many different types of sensors might exist in a robot?
4. What is the function of a manipulator?
10.6 VARIOUS CATEGORIES OF ROBOTS
Robots perform a variety of functions. Depending on these, robots can be categorized into the
following categories.
PRESET ROBOTS Preset robots are preprogrammed. They have been designed to perform the same task
over time and can work 24 hours a day, 7 days a week without any breaks. Preset robots do not alter
their behavior. Therefore, these robots have an incredibly low error rate and are suitable for wearisome
work. They are frequently used in manufacturing sectors such as the mobile industry, vehicle
manufacturing, material handling, and welding to save time and money. Preset robots deliver jobs in
environments where it is hazardous for humans to work. Robots move heavy objects, perform
assembly tasks, paint, inspect parts, and handle chemicals. A preset robot articulates according to the
operation it performs. It can perform a significant role in the medical field because the tasks it performs
must have high efficiency at a level comparable to human beings.
COLLABORATIVE ROBOTS OR COBOTS Cobots are the robots that can collaborate with human workers,
assisting them to achieve their goals. The use of cobots is trending in the market, and there is an
excellent outlook for collaborative robots. According to the survey by MarketsandMarkets, the cobots
market in 2020 will be worth around $3.3 billion. There are various functions of collaborative robots.
Depending on the usage, the collaborative robots are used. Collaborative robots have various
applications in manufacturing as well as the medical industry.
STAND-ALONE ROBOTS Stand-alone robots are the robots that have a built-in AI system and work
independently without much interference from humans. These robots perform tasks depending on
the environment and adapt to changes in it. With the use of AI, a stand-alone robot learns to modify
its behavior and excel in performing its assignment. Autonomous robots have household, military,
education, and healthcare applications. They can walk like a human being, avoid obstacles, and provide
social-emotional support. Some of these robots are used for domestic purposes as stand-alone vacuum
cleaners, such as iRobot Roomba. Stand-alone robots are also used in hospitals to deliver medications,
40 Part IV • Robotics, Social Networks, AI and IoT
keep track of patients who are yet to receive them, and send this information to the nurses working
on that shift and other shifts without chance of any error.
REMOTE-CONTROLLED ROBOTS Even though robots can perform stand-alone tasks, they do not have
human brains; hence, many tasks require human supervision. These robots can be controlled via Wi-
Fi, Internet, or satellite. Humans direct remote-controlled robots to perform complicated or dangerous
tasks. The military uses these robots to detonate bombs or to act as soldiers around the clock on the
battlefield. In the space program research field, their scope of use is extensive. Remote-controlled
cobots are also used to perform marginally invasive surgeries.
SUPPLEMENTARY ROBOTS Supplementary robots enhance the existing capabilities or replace capabilities
that a human has lost or does not have. This type of robot can be directly attached to a human’s body.
It connects to a user’s body and communicates with the robot’s operator directly or when the operator
grips the body. The robot can be controlled by a human body, and in some cases, even by thinking of
a specific action. Its applications include serving as a robotic prosthetic arm or providing precision for
the surgeons. Extensive research on building prosthetic limbs is being conducted.
u SECTION 10.6 REVIEW QUESTIONS
1. Identify some key categories of robots.
2. Define and illustrate the capabilities of a cobot.
3. Distinguish between a preset robot and a stand-alone robot. Give examples of each.
10.7 AUTONOMOUS CARS: ROBOTS IN MOTION
A robot that may eventually touch most people’s lives is an autonomous (self-driving) car. Like many
other technologies, self-driving cars have been at peak hype recently, but people also recognize their
technical, behavioral, and regulatory challenges. Nevertheless, technology and processes are evolving
to make the self-driving car a reality in the future, at least in specific settings if not all over the world.
Early versions of self-driving cars were enabled by the radio antenna developed in 1925. In 1989,
researchers at Carnegie Mellon used neural networks to control an autonomous vehicle. Since then,
many technologies have come together to accelerate development of self-driving cars. These include:
• Mobile phones: With the help of low-powered computer processors and other accessories such
as cameras, mobile phones have become ubiquitous. Many technologies developed for phones,
such as location awareness and computer vision, are finding applications in cars.
• Wireless Internet: Connectivity has become much more feasible with the rise of 4G networks
and Wi-Fi. Going forward, growth in 5G will perhaps be important for self-driving cars to allow
their processors to communicate with each other in real time.
• Computer centers in cars: A number of new technologies are available in today’s cars, such as
rearview cameras and front and back sensors that help vehicles detect objects in the environment
and alert the driver to them or even take necessary actions automatically. For example, adaptive
cruise control automatically adjusts the speed of a car based upon the speed of the vehicle in
front.
• Maps: Navigation maps on mobile phones or navigation systems in cars have made a driver’s
job easy with regard to navigation. These maps enable an autonomous vehicle to follow a specific
path.
• Deep learning: With advances in deep learning, the ability to recognize an object is a key enabler
of self-driving cars. For example, being able to distinguish a person from an object such as a
tree, or whether the object is moving or stationary is critical in taking actions in a moving vehicle.
Chapter 10 • Robotics: Industrial and Consumer Applications 41
Autonomous Vehicle Development
The heart of an autonomous vehicle system is a laser rangefinder (or light detection and ranging – lidar
device), which is on the vehicle’s roof. The lidar generates a 3D image of the car’s surroundings and
then combines it with high-resolution world maps to produce different data models for taking action
to avoid obstacles and follow traffic rules. In addition, many other cameras are mounted. For example,
a camera positioned near a rearview mirror detects traffic lights and takes videos. Before making any
navigation decisions, the vehicle filters all data collected from the sensor and camera and builds a map
of its surroundings and then precisely locates itself in that map using GPS. This process is called
mapping and localization.
The vehicle also consists of other sensors such as the four radar devices that are on the front
and back bumpers. These devices allow the vehicle to see far distances so that they can make decisions
beforehand and deal with fast-moving traffic. A wheel encoder determines the vehicle’s location and
maintains records of its movements. Algorithms such as neural networks, rule-based decision making,
and a hybrid approach are used to determine the vehicle’s speed, direction, and position, and the
collected data are used to direct the vehicle on the road to avoid obstacles.
Autonomous vehicles must rely on detailed maps of roads. Thus, before sending driverless cars
on roads, engineers drive a route several times and collect data about its surroundings. When driverless
vehicles are in operation, engineers compare the data acquired by them to the historical data.
There is an entire town built for the sole purpose of testing autonomous vehicles. It is located
in Michigan. This city has no single resident, and self-driving vehicles roam the streets without the
risks in the real world. This city, called Mcity, is truly a city for robotic vehicles. Mcity includes
intersections, traffic signals, buildings, construction work, and moving obstacles such as humans and
bicycles similar to those in real cities. Autonomous vehicles are not only tested in this closed
environment but are being used in the real world as well.
Google’s Waymo unit is one of the early pioneers of self-driving vehicles. They have been tested
on California roads, but before they start to drive next to human-driven cars, companies have to test
them thoroughly because one negative incident can impede their acceptance. For example, in the
spring of 2018, a self-driving vehicle being tested by Uber killed a pedestrian in Tempe, Arizona. This
led to the suspension of all public testing of autonomous vehicles by Uber. The technology is still in
development, but it has come far enough that limited testing on public roads is safe. We might be
surprised in the near future by the fact that the person in the driver’s seat of a vehicle next to you in
traffic might not actually be driving it at all.
In 2016, the U.S. Department of Transportation (DOT) began to embrace driverless vehicles to
speed their development. In September 2016, DOT announced the first-ever guidelines for
autonomous driving. A groundbreaking announcement by the National Highway Traffic Safety
Administration (NHTSA) a month later allowed for the AI system controlling Google’s self-driving
vehicle to be considered a driver in response to the company’s proposal to the NHTSA in November
2015.
Some states currently have specific laws that ban autonomous driving. For example, as of this
writing, the state of New York does not allow any hands-free driving. Without clear regulations, testing
self-driving vehicles is a challenge. Although a few states such as Arizona, California, Nevada, Florida,
and Michigan currently allow autonomous vehicles on the road, California is the only one with
licensing regulations at this point.
Google might be the most well known for autonomous vehicles, but it is not the only one. A
handful of the most powerful companies, such as Uber and Tesla, are in the same race as well. Every
major car company is working either with technology companies or its own technology to develop
autonomous vehicles or at least to participate in this revolution.
Issues with Self-Driving Cars
42 Part IV • Robotics, Social Networks, AI and IoT
Autonomous cars have been connected to a number of issues.
• Challenges with technology: There have been several challenges with the technology used in
self-driven cars. Several software and mechanical hurdles are still to be overcome in order to roll
out a fully autonomous car. For example, Google is still trying to update its software on an
almost daily basis for its self-driven car. Several other companies are still trying to figure out the
amount of authority to be transferred when a human driver takes control from an automatic
vehicle.
• Environmental challenges: Technology and mechanical capabilities cannot yet address many
environmental factors affected by self-driving cars. For example, there are still concerns
regarding their performance in bad weather. Likewise, several systems have not been tested in
extreme conditions such as snow and hail. There are several tricky navigating situations on the
road, such as when an animal jumps onto it.
• Regulatory challenges: All companies planning to become involved with selfdriving cars need
to address regulatory hurdles. There are still many unanswered questions about the regulation
of autonomous driving. Several questions about liability include these: What will a license
involve? Will new drivers be required to get traditional licenses even if they are not drivers? What
about young people, or older people with disabilities? What will be required to operate these
new vehicles? Governments need to work quickly to catch up with the booming technology.
Considering that public safety is on the line, auto regulations should be some of the strictest
regulations in the modern world.
• Public trust issues: Most people do not yet believe that an autonomous car can keep them
safe. Trust and consumer acceptance are the crucial factors. For example, if there is a situation
when an autonomous car is being forced to choose between the life of a passenger versus that
of a pedestrian, what should be done? Consumers may refuse the whole idea of driverless cars.
No technology can be perfect, but the question is which company will be able to best convince
its customers to entrust their lives to them.
Advances similar to those for self-driving cars are being explored in other autonomous vehicles.
For example, several companies have already launched trials of s elf-driving trucks. Autonomous
trucks, if ever fully deployed, will have a massive disruptive effect on jobs in the transportation
industry. Similarly, self-driving tractors are being tested. Finally, autonomous drones and aircrafts are
also being developed. These developments will have a huge impact on future jobs while creating other
new jobs in the process.
Self-driving vehicles have become part of this world of technology in spite of related technical
and regulatory barriers. Autonomous vehicles are yet to achieve the knowledge capabilities of human
drivers, but as the technology improves, more-reliable driving vehicles will become a reality. Like many
technologies, the short-term impact may be cloudy, but the long-term impact is yet to be determined.
u SECTION 10.7 REVIEW QUESTIONS
1. What are some of the key technology advancements that have enabled the growth of self-driving
cars?
2. Give examples of regulatory issues in self-driving cars.
3. Conduct online research to identify the latest developments in autonomous car deployment. Give
examples of positive and negative developments.
4. Which type of self-driving vehicles are likely to have the most disruptive effect on jobs, and why?
Chapter 10 • Robotics: Industrial and Consumer Applications 43
10.8 IMPACT OF ROBOTS ON CURRENT AND FUTURE JOBS
Robotics has been a boon to the manufacturing industry. Besides automation that is possible with
robotics, new technologies such as image recognition systems are automating jobs that used to require
humans for inspection and quality control.
Various industry experts report that by 2025, up to 25 percent of current jobs will be replaced
by robots or AI. Davenport and Kirby’s book Humans Need Apply: Winners and Losers in the Age of Smart
Machines (2016) focuses on this topic. Of course, many other researchers, journalists, consultants, and
futurists have given their own predictions. In this section, we review some related issues. These issues
are relevant to AI in general and robotics in particular. Thus, Chapter 14 will also cover these issues,
but we want to study these in the context of robotics in this chapter.
As a group activity, watch the following video: https://www.youtube.com/watch?
v=GHc63Xgc0-8. Also watch https://www.youtube.com/watch?v=ggN8wCWSIx4 for a
different view. What are your takeaways from these videos? What is the most likely scenario in your
view? How can you prepare for the day when indeed humans may not need to apply for many jobs?
IBM Watson’s ability to digest vast amounts of data in the medical research literature and
provide the latest information to a physician has been written about in the literature. Similar job
enhancement opportunities in many other areas have been seen. Consider this: AI-powered
technologies such as narrative science and automated insights that can ingest structured data include
visualizations generated by software such as Tableau and develop an initial draft of a story to narrate
what the results convey. Of course, that would appear to threaten the job of a journalist or even a data
scientist. In reality, this can also enhance that job by presenting an initial draft of a story. Then the
storyteller can focus on more advanced and strategic issues related to that data and visualization.
The power of consistency and comprehensiveness can also be helpful in the completion of jobs.
For example, as noted by Meister (2017), chatbots can likely provide much of the initial human
resource (HR) information to new employees. Chatbots can also be helpful in providing such
information to remote employees. A chatbot is more likely than a human to provide complete and
consistent information each time. Of course, this implies that workers whose main job is to recite such
information to each new employee or serve as the first source of information may not be needed.
Hernandez (2018) identified seven job categories into which robotics in particular and AI in
general will expand. She also quoted several other studies. According to a McKinsey & Co study, AI
could result in 20950 million new jobs in the next 10915 years. McKinsey also predicts that 759375
million people may need to change jobs/occupations in the same time period because of robotics and
AI. According to Hernandez, the following seven jobs are likely to increase:
1. AI development: This is an obvious growing area. As more companies develop products and
services based on AI, the need for such developers will continue to increase. As an example,
iRobot Co, which produces robotic vacuum cleaners, is shifting its hiring from hardware to
software engineers as it works to develop its next generation of products that are more adaptive
and AI based. Newer robot vacuums are going to be able to “see” a wall. They can also alert the
owner to how long the cleanup took and the area swept.
2. Customer–robot interactions: As more companies deploy robots in these organizations,
acceptance of such robots by both employees and customers is uncertain. A new job category
has emerged to study the interactions between a robot and its coworkers and customers and to
retrain the robot or take this information into account in designing the next generation. Clearly,
the study of such interactions may enable the use of analytics/data science as well.
3. Robot managers: Although robots might do the bulk of their work in a specific situation,
humans will still need to observe them and ensure that the work is progressing as expected.
Further, if any unusual conditions arise, a human worker has to be alerted and respond to the
situation. This would be true in many settings where the robots are performing the bulk of tasks
in areas such as manufacturing. Hernandez (2018) gives an example of Cobalt robots, which work
44 Part IV • Robotics, Social Networks, AI and IoT
as security guards. These robots alert a human whenever an intruder is detected or they notice
anything unusual. Of course, a human robot manager is typically able to supervise many more
robots than human workers because the primary role of the manager is to supervise them and
respond to unusual situations.
4. Data labelers: Robots or AI algorithms learn from examples. And the more examples they are
given, the better their learning can be (see Chapter 5 for a longer description of this issue). For
example, image recognition systems in virtually every setting (see Chapter 5 on deep learning for
examples) require as many examples as possible to improve those systems’ recognition capability.
This is crucial for not just facial recognition but also image applications to detect cancer from X-
ray images, weather features from radar images, and so on. It requires that humans view the
example images and label them as representing a specific person, feature, or class. This work is
tedious and requires humans. Many companies have hired hundreds
of human labelers to view the images and tag them appropriately. As such image applications
grow, the need for labelers will also increase. These workers are also needed for continuous
improvement of the robot or AI algorithms by recording false positives or newer examples.
5. Robot pilots and artists: Robots in general and drones in particular are being used to provide
action shots using overhead cameras or angles that would be difficult if not impossible for
humans to do. Drones could also be dressed as birds or flowers and provide a unique overview
as well as enhance a setting. Similarly, other robots might be dressed in unique outfits to create a
cultural ambiance. Such designer/makeup artists are being hired by many companies that provide
services for events such as concerts, weddings, and so forth. In addition, drone piloting has
become a highly specialized skill for entertainment, commercial, and military applications. These
jobs will increase as the applications evolve.
6. Test drivers and quality inspectors. Autonomous cars are already becoming reality. With each
such automation of vehicles, at least for the foreseeable future, there is a growing need for safety
drivers who monitor each vehicle’s performance and take appropriate actions in unusual
situations. Their jobs would not entail the use of remote controls as drone pilots employ but
continuous watch of the vehicle’s operations and response to emergent situations. Similar jobs
also exist in other robotic applications as the robots are trained and tested to work in specific
settings.
7. AI lab scientists. This brings us to the very first category of new jobs we i dentified—AI coders.
While their job is to develop the algorithms for robots or AI programs, a similar category of
highly specialized users is also emerging—folks who are trained and employed in using these
hardware and software systems for special applications. For example, physicians have to undergo
additional training to be certified in the use of robots in their surgeries, cardiology and urology
practices, and so on. Another category of such specialists involves scientists who customize these
robots and AI algorithms for their domain. For example, quite a few companies are using AI
tools to identify new drug molecules to develop and test new treatment options for diseases. AI
could speed such development. These scientists not only develop their domain expertise but also
data scientists’ knowledge or at least the ability to work with data scientists to create their new
applications.
Although the preceding list identifies several categories of jobs that are likely to develop or
increase, millions of jobs are likely to be eliminated. For example, automation is already impacting the
number of jobs in logistics. When autonomous trucks become a reality, at least some of the well-paying
jobs in transportation will likely be gone. There might be disagreements on when the massive change
may occur, but the long-term impact on jobs is certain to occur. The major issue this time is that many
of the knowledge economy “white-collar” jobs are the ones that are more likely to be automated. And
this change is unprecedented in history. Many social scientists, economists, and leading thinkers are
worried about the upheaval that this next wave of robotic automation will cause, and they are
considering various solutions. For example, the concept of universal basic income (UBI) has been
Chapter 10 • Robotics: Industrial and Consumer Applications 45
proposed. UBI proponents argue that giving every citizen a minimal basic income will ensure that no
one goes hungry despite the massive loss of jobs that is likely to occur. Others, for example, Lee (2018),
have argued that providing UBI may not satisfy human beings’ need for meaningful achievements and
contributions in life. Lee proposes a social investment stipend (SIS), which would recognize
individuals’ contributions to society for providing support and care, community service, or education.
The stipend would be paid in recognition of an individual’s service in one of these categories. Lee’s
book focuses on this issue and is one of the many ideas being proposed on how to plan for and address
the upcoming disruption from automation. Our goal in this section is to simply alert you to these
issues.
u SECTION 10.8 REVIEW QUESTIONS
1. Which jobs are most at risk of disappearing as the result of the new robotics revolution?
2. Identify at least three new categories of jobs that are likely to result in a significant number of new
employees.
3. Are the tasks undertaken by data labelers just for one time or longer lasting?
4. Research the concepts of UBI and SIS.
10.9 LEGAL IMPLICATIONS OF ROBOTS AND ARTIFICIAL INTELLIGENCE
As we noted in the previous section, the impact of AI in general and robotics in particular is far and
wide and can be studied both specifically in the context of robots and more broadly for AI. Legal
implications of robotics and AI are discussed in this chapter and in Chapter 14. Many legal issues are
yet to be untangled as we embrace and employ AI technologies, robots, and self-driving cars. This
section highlights some of the key dimensions of legal impacts related to AI. The following material
has been contributed by Professor Michael Schuster, assistant professor of Legal Studies at the Spears
School of Business at Oklahoma State University. He is a noted expert in legal matters related to AI.
He has also published extensively in this area.
Tort Liability
Self-driving cars and other systems controlled by AI represent a Pandora’s box of potential tort liability
(where a wrongful act creates on obligation to pay damages to another). Imagine that a motorcyclist is
injured when a self-driving car veers into his lane and they collide. This was the alleged event that led
to Nilsson v. General Motors (N.D. California, 2018)—a case with the potential to address difficult
questions of AI-created tort liability. The suit settled, and thus did not clarify who should pay when
someone is injured by an AI-controlled system. Potential candidates for liability include programmers
of an AI system, manufacturers of a product incorporating AI, and owners of a product at the time it
harmed another. Medical malpractice litigation may similarly be altered by new technologies. As
doctors defer some decision making to AI, lawsuits for injurious medical care move from professional
liability cases (against the doctor) to product liability (against manufacturers of AI systems). An early
example of this phenomenon is the lawsuits over allegedly botched surgeries using the Da Vinci
Surgical Systems robot.
Patents
The introduction of AI systems capable of independent or human-assisted invention raises a variety
of questions about patenting these creations. Patents have traditionally been granted for novel
inventions that would not have been an obvious improvement of a known technology as viewed
through the eyes of an average party working in the relevant field. Accordingly, this standard has
traditionally asked whether a new technology would have been obvious to a human, but as AI becomes
ubiquitous, the scope of what is obvious expands. If the average person in an industry has access to
an AI system capable of inventing new things, many improvements on known technologies can
46 Part IV • Robotics, Social Networks, AI and IoT
become obvious. Since these improvements would then be obvious, they would no longer be
patentable. Inventing AI will thus make it harder for a human to get a patent as such technology
becomes more commonplace. Moving beyond human inventions, a host of issues arise regarding AI
that can independently invent. If a person does not contribute to the invention (but rather merely
identifies a goal to be achieved or provides background data), he or she does not satisfy the statutory
threshold to be an inventor (and thus, does not qualify for patent ownership). See Schuster (2018). If
AI creates an invention without a human inventor, who owns the patent or should a patent be granted
at all? Some assert the U.S. Constitution’s mandate that patents can be granted only to “inventors”
necessarily requires a human actor, and thus, Congress cannot constitutionally allow the patenting of
AI-created technologies. Additional commentators present a variety of policy positions arguing why
parties such as the computer’s owner, the AI’s creator, or others should own patents for computer-
created inventions. These issues are yet to be resolved.
Property
A basic tenet of U.S. law is a strong protection of property rights. These values extend to corporate
entities that can own both real estate and movable property to the exclusion of all others while
shareholders retain some allotted portion of the corporation itself. Analysts are presently addressing
to what extent property rights should extend to autonomous AI. If a robot were to engage in work for
hire, might it be able to make purchases of goods or realty to further its interests? Could an eccentric
octogenarian millionaire leave his entire fortune to a loyal robot housekeeper? Topics of this nature
will raise a variety of new legal queries, including intestate passage of AI-owned property at its “death”
and the standard for death in a nonbiological entity.
Taxation
Robotics and AI will replace a significant number of jobs presently undertaken by humans.
Commentators are divided on whether new technologies will create a number of new jobs equal to
those replaced by automation. Should the scope of new jobs fall shy of those lost, it is feasible that
tax-based issues will be created. A particular concern deals with federal payroll taxes whereby workers
and employers pay taxes premised on wages made by the employee. If the aggregate number of workers
and net pay are reduced by job automation, the payroll tax base will be reduced. Given that these taxes
are important to the sustainability of various government-run safety net programs (e.g., Social
Security), payroll tax shortfalls could have significant societal ramifications. In 2017, Bill Gates
(Microsoft cofounder) set forth a proposal to tax robots that are used to automate existing human
jobs. This new tax would theoretically supplement extant payroll taxes to ensure continued funding
for government programs. Commentators are split on the advisability (or need) for such a tax. A
common criticism is that taxing robots discourages technological advancement, which is contrary to
the accepted policy of encouraging such endeavors. A satisfactory resolution to this debate is yet to be
reached.
Practice of Law
Beyond what the law is or should be, AI will have substantial effects within discrete segments of the
practice of law. A prime example of this influence is in the area of document review—the part of a
lawsuit where litigants evaluate documents provided by their opponents for relevance to the case.
Costs associated with this process can be substantial given that some cases entail review of millions of
pages by attorneys who bill hundreds of dollars per hour. Corporate clients looking to reduce costs—
and law firms seeking a competitive advantage—have adopted (or intend to adopt) AI-based document
review systems to minimize the number of billed hours. Similarly, some firms have adopted industry-
specific technologies to create competitive advantage. At least one major law firm instituted the use of
an AI-driven system to analyze strengths and weaknesses of its clients’ patent portfolios.
Chapter 10 • Robotics: Industrial and Consumer Applications 47
Constitutional Law
As the state of AI advances, it continues to move toward “human-level” intelligence. But as it becomes
more “personlike,” questions arise regarding whether AI should be afforded rights commonly granted
to humans. The U.S. Constitution’s First Amendment provides for freedoms of speech, assembly, and
religion, but should rights of this nature extend to AI? For instance, one might argue that these rights
preclude the government from dictating what a robot can say (violating its right to free speech). At
first blush, this proposition seems far-fetched, but perhaps it is not. On the issue, it is notable that the
Supreme Court of the United States recently extended some free speech rights and religious liberties
to corporate entities. Accordingly, there is some domestic precedent for affording constitutional
protections to nonhuman actors. Further, in 2017, Saudi Arabia granted citizenship to “Sophia,” a
humanoid robot created by Hong Kong’s Hanson Robotics in 2015. How this issue will resolve itself
(domestically and globally) remains to be seen.
Professional Certification
There are many activities for which humans must receive certification issued by a government or
professional organization prior to undertaking that act (e.g., the practice of law or medicine). As the
state of AI progresses, AI will increasingly be capable of performing these state-regulated endeavors
independent of human engagement. With this in mind, standards must be developed to determine
whether an AI technology is capable of providing satisfactory service in regulated professional fields.
If an autonomous robot is capable of passing a state’s bar exam, should it be able to give legal advice
without human supervision? To the extent that many professional groups require annual training to
maintain competence, how will these policies apply to AI technologies? Is there value in requiring that
a computer undertaking legal functions “attend” continuing legal education classes? These issues will
be settled as AI begins to carry out work currently done exclusively by human professionals like
doctors and lawyers.
Law Enforcement
In addition to policy choices detailing what the law is or should be, AI may influence enforcement of
the law. Rapid growth in technology will soon afford police forces access to large amounts of near
real-time data and computing capacity to determine where crimes are being committed. Recognized
infractions may run the gamut from common public transgressions (e.g., running a red light) to more
private acts, such as underreporting income on a tax return. The capacity to recognize such criminal
acts on a large scale raises a variety of enforcement questions. Discretion in prosecuting infractions
has long been a part of law enforcement. Should this power of choice regarding issues associated with
stereotype-based prosecution decisions be delegated to AI systems? Moreover, machine-based
enforcement programs have consistently been met with questions of their constitutionality (e.g., using
cameras to identify drivers not stopping for red lights). While these arguments have thus far proven
unsuccessful, they will likely be relitigated as the practice expands. Beyond enforcement questions,
some have raised the possibility of implementing AI in the judiciary. For instance, it has been proposed
that data-based sentencing may more successfully achieve targeted goals (e.g., successful education
while incarcerated or avoidance of recidivism) than arguably idiosyncratic members of the judiciary.
Such a mechanism will, of course, raise potential transparency issues and arguments relating to granting
too much power to AI systems.
Regardless of the issues that the last two sections have raised, robotics technologies and
applications are evolving rapidly. As managers, you have to continue to think about how to manage
these technologies while being fully aware of immediate behavioral and legal issues in implementing
the technologies.
u SECTION 10.9 REVIEW QUESTIONS
1. Identify some of the key legal issues for robotics and AI.
48 Part IV • Robotics, Social Networks, AI and IoT
2. Liability for harm (tort liability) is an obvious early question for any technology. What are some
of the key challenges in identifying such liability?
3. Recent news about illegal intervention in elections has led to the discussion about who is
responsible for damage control. When chatbots and automated social media systems have the
ability to propagate “fake news,” who should be required to monitor them and prevent such
action?
4. What are some of the law enforcement issues in employing AI?
Chapter Highlights
• Industrial automation brought the first wave of robots, but
now the robots are becoming autonomous and finding
applications in many areas.
• Robotic applications span industries such as agri-culture,
healthcare, and customer service.
• Social robots are emerging as well to provide care and
emotional support to children, patients, and older adults.
• All robots include some common components: power unit,
sensors, manipulator/effector, logic unit/CPU, and
location sensor/GPS.
• Collaborative robots are evolving quickly, leading to a
category called cobots.
• Autonomous cars are probably the first category of
robots to touch most consumers.
• Self-driving cars are challenging the limits of AI
innovation and legal doctrines
• Millions of jobs are at risk of being lost due to the use
of robots and AI, but some new job categories will
emerge.
• Robots and AI are also creating new challenges in many
legal dimensions.
Questions for Discussion
1. Based upon the current state of the art of robotics applications,
which industries are most likely to embrace robotics? Why? 2. Watch the following two videos: https://www.
youtube.com/watch?v=GHc63Xgc0-8 and https://
www.youtube.com/watch?v=ggN8wCWSIx4 for a different
view on impact of AI on future jobs. What are your takeaways from
these videos? What is the more likely scenario in your view? How can
you prepare for the day when humans indeed may not need to apply
for many jobs? 3. There have been many books and opinion pieces written about the
impact of AI on jobs and ideas for societal responses to address the
issues. Two ideas were mentioned in the chapter – UBI and SIS. What are the pros and cons of these
ideas? How would these be implemented?
4. There has been much focus on job protection through tariffs and
trade negotiation recently. Discuss how and why this focus may or
may not address the job changes coming due to robotics and AI
technologies.
5. Laws rely on incentive structures to encourage prosocial behavior.
For example, criminal law encourages compliance by punishing those
who break the law. Patent law incentivizes creation of new
technologies by offering inventors a period of limited monopoly
during which they can exclusively use their invention. To what extent
do these (and other) incentives make sense when applied to AI? How
can incentive structures be created to encourage AI devices to behave
in prosocial manners? 6. To what extent do extralegal considerations come into play with
regard to the above issues? Are there moral (or religious) dimensions
to be considered when determining whether AI should be given
rights similar to those of a person? Would AI-assisted law
enforcement or court action erode faith in the criminal justice system
and judiciary? 7. Adopting policies that maximize the value of AI encourages future
development of these technologies.
Such a course, however, is not without drawbacks. For instance,
determining that a “robot tax” is not a preferred policy choice would
increase the incentive to adopt a robot workforce and improve any
relevant technologies. Elevating the state of robotics is a laudable goal,
but in this instance, it would come at the anticipated cost of reduced
public funds. How should trade-offs such as these be evaluated? Where
should encouragement of technological progress (especially regarding
AI) fall in the hierarchy of government priorities?
Key Terms
automation autonomous
car
autonomy
effector
patent robot
sensor social
robot
tort liability
universal basic income (UBI)
Chapter 10 • Robotics: Industrial and Consumer Applications 49
Exercises
1. Identify applications other than those discussed in this
chapter where Pepper is being used for commercial and
personal purposes.
2. Go through specifications of MAARS at https://www.
qinetiq-na.com/wp-content/uploads/brochure_
maars.pdf. What are the functions of MAARS?
3. Conduct online research to find at least one new robotics
application in agriculture. Prepare a brief summary of your
research: the problem addressed, technology summary, results
achieved if any, and lessons learned.
4. Conduct online research to find at least one new robotics
application in healthcare. Prepare a brief summary of your
research: the problem addressed, technology summary, results
achieved if any, and lessons learned. 5. Conduct online research to find at least one new robotics
application in customer service. Prepare a brief s ummary of
your research: the problem addressed, technology summary,
results achieved if any, and lessons learned.
6. Conduct online research to find at least one new robotics
application in an industry of your choice. Prepare a brief
summary of your research: the problem addressed,
technology summary, results achieved if any, and lessons
learned. 7. Conduct research to identify the most recent developments
in self-driving cars. 8. Conduct research to learn and summarize any new
investments and partnerships in self-driving cars.
9. Conduct research to identify any recent examples of legal
issues regarding self-driving cars. 10. C onduct research to identify any other new types of jobs that
would be enabled by AI and robotics beyond what was
covered in the chapter. 11. C onduct research to report on the latest projections for job
losses due to robotics and AI.
12. Identify case stories for each of the legal dimensions identified
by Schuster (2018) in Section 10.9.
References
“A Brief History of Robotics since 1950.” Encyclopedia.com.
http://www.encyclopedia.com/science/encyclopedias-
almanacs-transcripts-and-maps/brief-history-robotics- 1950
(accessed September 2018). Ackerman, E. (2016). IEEE Spectrum. http://spectrum
.ieee.org/automaton/robotics/home-robots/tega- mit-latest-
friendly-squishable-social-robot (March 5, 2017). “Adidas’s High-Tech Factory Brings Production Back to Germany.”
(2017, January 14). The Economist. https://www.
economist.com/business/2017/01/14/adidass-hightech-
factory-brings-production-back-to-germany (accessed September
2018).
Allinson, M. (2017, March 4). “BMW Shows Off Its Smart Factory
Technologies at Its Plants Worldwide.” Robotics and Automation.
https://roboticsandautomation news.com/2017/03/04/bmw-
shows-off-its-smartfactory-technologies-at-its-plants-
worldwide/11696/ (accessed September 2018). Aoki, S., et al. (1999). “Automatic Construction Method of Tree-
Structural Image Conversion Method ACTIT.” Journal of the Institute of
Image Information and Television Engine, 53(6), pp. 888–894 (in Japanese). “A Robot Cooks Burgers at Startup Restaurant Creator.” (2018).
Techcrunch. https://techcrunch.com/video/arobot-cooks-
burgers-at-startup-restaurant-creator/ (accessed September 2018).
Ayres, R., & S. Miller. (1981, November). “The Impacts of Industrial
Robots.” Report CMU-RI-TR-81-7. Pittsburgh, PA: The Robotics Institute at Carnegie Mellon University.
Bereznak, A. (2015, January 7). “This Robot Can Comfort Children
Through Chemotherapy.” Yahoo Finance.
https://finance.yahoo.com/news/this-robot-cancomfort-
children-through-107365533404.html (accessed September 2018).
“Berry Picking at Its Best with Sensor Technology.” (2018).
Pepperl+Fuchs. https://www.pepperl-fuchs.com/usa/
en/27566.htm (accessed September 2018).
Bogue, R. (2016). “Robots Poised to Revolutionise Agriculture.” Industrial
Robot: An International Journal, 43(5), pp. 450–456 Broekens, J., M. Heerink, & H. Rosendal. (2009). “Assistive Social Robots
in Elderly Care: A Review.” Gerontechnology, 8, pp. 94–103 doi:
10.4017/gt.2009.08.02.002.00. Carlsson, B. (1998) “The Evolution of Manufacturing Technology and Its
impact on Industrial Structure: An International Study.” IUI Working
Paper 203. Internation Joseph A. Schumpeter Society Conference on
Evolution of Technology and Market in an International Context. The
Research Institute of Industrial Economics (IUI), Stockholm, May
24–28, 1988. “Case Study Pepper, Courtyard Marriott.” SoftBank Robotics.
https://www.softbankrobotics.com/us/solutions/ pepper-
marriott (accessed September 2018).
Chirgwin, R. (2018, May 29). “Softbank’s ‘Pepper’ Robot Is a Security
Joke.” The Register. https://www.theregister.
co.uk/2018/05/29/softbank_pepper_robot_multiple_basic_sec
urity_flaws/ (accessed September 2018). Coxworth, B. (2018, May 29). “Restaurant Keeps Its Prices Down – With
a Robotic Kitchen.” New Atlas. https://newatlas.com/spyce-
restaurant-robotickitchen/54818/ (accessed September 2018). “Da Vinci Robotic Prostatectomy – A Modern Surgery Choice!” Robotic
Oncology. https://www.roboticoncology.com/ da-vinci-
robotic-prostatectomy/ (accessed September 2018).
50 Part IV • Robotics, Social Networks, AI and IoT
Drummond, K. (2012, March 8). “Navy’s Newest Robot Is a Mechanized
Firefighter.” wired.com. https://www.
wired.com/2012/03/firefight-robot/ (accessed September 2018). Dupont, T. (2015, October 15). “The MAARS Military Robot.” Prezi.
https://prezi.com/fsrlswo0qklp/the-maars-military- robot/
(accessed September 2018). Engel, J. (2018, May 3). “Spyce, MIT-Born Robotic Kitchen Startup,
Launches Restaurant: Video.” Xconomy. https://
www.xconomy.com/boston/2018/05/03/spyce-mitborn-
robotic-kitchen-startup-launches-restaurantvideo/ (accessed
September 2018).
Fallon, S. (2015). “A Blue Robotic Bear to Make Sick Kids Feel Less
Blue.” https://www.wired.com/2015/03/bluerobotic-bear-
make-sick-kids-feel-less-blue/ (accessed August 2018).
Forrest, C. (2015).“Chinese Factory Replaces 90% of Humans with
Robots, Production Soars.” TechRepublic. https://
www.techrepublic.com/article/chinese-factory- replaces-90-of-
humans-with-robots-productionsoars/ (accessed September
2018). France, A. (2014, December 1). “Nestlé Employs Fleet of Robots to Sell
Coffee Machines in Japan.” The Guardian. https://
www.theguardian.com/technology/2014/dec/01/ nestle-
robots-coffee-machines-japan-george-clooneypepper-android-
softbank (accessed September 2018).
Gandhi, A. (2013, February 23). “Basics of Robotics.” Slideshare.
https://www.slideshare.net/AmeyaGandhi/basics-ofrobotics
(accessed September 2018). Goris, K., et al. (2010, September). “Mechanical Design of the Huggable
Robot Probo.” Robotics & Multibody M echanics Research Group.
Brussels, Belgium: Vrije Universiteit Brussels.
Green, D. (2018). “Adidas Just Opened a Futuristic New Factory – and
It Will Dramatically Change How Shoes Are Sold.” Business Insider.
http://www.businessinsider.com/ adidas-high-tech-
speedfactory-begins-production- 2018-4 (accessed September
2018). Hernandez, D. (2018). “Seven Jobs Robots Will Create – or Expand.” The
Wall Street Journal. https://www. wsj.com/articles/seven-jobs-
robots-will-createor- expand-1525054021 (accessed September
2018). History of Robots. (n.d.). Wikipedia. https://en.wikipedia.
org/wiki/History_of_robots (accessed September 2018). “Huggable Robot Befriends Girl in Hospital.” YouTube video.
https://youtu.be/UaRCCA2rRR0 (accessed August 2018).
“Innovative Human-Robot Cooperation in BMW Group Production.”
(2013, October 9). BMW Press Release.
https://www.press.bmwgroup.com/global/article/
detail/T0209722EN/innovative-human-robot- cooperation-in-
bmw-group-production?language=en (accessed S eptember 2018). Javelosa, J., & K. Houser. (2017). “Production Soars for Chinese Factory
Who Replaced 90% of Employees with Robots.” Future Society.
https://futurism.com/2-production-soarsfor-chinese-factory-
who-replaced-90-of-employeeswith-robots/ (accessed September
2018). Jeong, S., et al. (2015). “A Social Robot to Mitigate Stress, Anxiety, and
Pain in Hospital Pediatric Care.” Proceedings of the Tenth Annual
ACM/IEEE International Conference on Human-Robot Interaction Extended
Abstracts.
Jeong, S., et al. (2015). “Designing a Socially Assistive Robot for Pediatric
Care.” Proceedings of the Fourteenth International Conference on Interaction
Design and Children. ACM. Jeong, S., & D. Logan. (2018, April 21–26).“Huggable: The Impact of
Embodiment on Promoting Socio-emotional Interactions for Young
Pediatric Surgeons.” MIT Media Lab, Cambridge, MA, CHI 2018,
Montréal, QC, Canada. Jiji. (2017, November 21). “SoftBank Upgrades Humanoid R obot
Pepper.” The Japan Times. https://www.japantimes.
co.jp/news/2017/11/21/business/tech/softbank- upgrades-
humanoid-robot-pepper/#.W6B3qPZFzIV (accessed September
2018). Joshua, J. (2013, February 24). “The 3 Types of Robots.” Prezi.
https://prezi.com/iifjw387ebum/the-3-types-of-robots/
(accessed September 2018). Kelly, M. (2018, July 16). “This Adorable Robot Wants to Make Air Travel
Less Stressful.” The Verge. https://www.theverge.
com/2018/7/16/17576334/klm-royal-dutch-airlinesrobot-travel-
airport (accessed September 2018). Kelly, S. M. (2017, August 10). “A
Robotic Crib Rocked My Baby to Sleep for Months.” CNN Tech.
https://money. cnn.com/2017/08/10/technology/gadgets/snoo-
review/index.html (accessed September 2018).
Lee, K. F. (2018). “The Human Promise of the AI Revolution.” The Wall
Street Journal. https://www.wsj.com/articles/ the-human-
promise-of-the-ai-revolution-1536935115 (accessed September
2018).
Mayank. (2012, June 18). “Basic Parts of a Robot.” maxEmbedded.com.
http://maxembedded.com/2012/06/ basic-parts-of-a-robot/
(accessed September 2018).
McHugh, R., & J. Rascon. (2015, May 23). “Meet MEDi, the Robot
Taking Pain Out of Kids’ Hospital Visits.” NBC News. https://
www.nbcnews.com/news/us-news/meet-medi- robot-taking-
pain-out-kids-hospital-visits-n363191 (accessed September 2018).
Meister, J. (2017), “The Future Of Work: How Artificial Intelligence Will
Transform The Employee Experience,” https://www.
forbes.com/sites/jeannemeister/2017/11/09/the- future-of-
work-how-artificial-intelligence-will- transform-the-employee-
experience/ (accessed November 2018). Modular Advanced Armed Robotic System. (n.d.). Wikipedia.
https://en.wikipedia.org/wiki/Modular_Advanced_
Armed_Robotic_System (accessed September 2018).
Nagato, T., H. Shibuya, H. Okamoto, & T. Koezuka. (2017, July).
“Machine Learning Technology Applied to Production Lines: Image
Recognition System.” Fujitsu Scientific & Technical Journal, 53(4).
O’Kane, S. (2018, May 17). “Raden Is the Second Startup to Bite the Dust
After Airlines Ban Some Smart Luggage.” Circuit Breaker.
https://www.theverge.com/circuitbreaker/2018/5/17/
17364922/raden-smart-luggage-airline-ban-bluesmart (accessed
September 2018).
Chapter 10 • Robotics: Industrial and Consumer Applications 51
Park, H. W., et al. (2017). “Growing Growth Mindset with a Social Robot
Peer.” Proceedings of the Twelfth ACM/IEEE International Conference on
Human Robot Interaction. Personal Robots Group. (2016). https://www.youtube.
com/watch?v=sF0tRCqvyT0 (accessed March 5, 2017). Personal Robots Group, MIT Media Lab. (2017). “Growing Growth
Mindset with a Social Robot Peer.” Proceedings of the Twelfth
ACM/IEEE International Conference on Human Robot Interaction.
Prasad, C. (2018, January 22). “Fabio, the Pepper Robot, Fired for
‘Incompetence’ at Edinburgh Store.” IBN Times.
https://www.ibtimes.com/fabio-pepper-robot-
firedincompetence-edinburgh-store-2643653 (accessed September
2018). Ro, L. (2016, October 18). “World’s First Smart Crib SNOO Will Help
Put Babies to Sleep.” Curbed. https://www.
curbed.com/2016/10/18/13322582/snoo-smart-cribyves-behar-
dr-harvey-karp-happiest-baby (accessed September 2018). “Robotics Facts.” Idaho Public Television. http://idahoptv.
org/sciencetrek/topics/robots/facts.cfm (accessed September
2018). “Robots in Agriculture.” (2015, July 6). Intorobotics. https://
www.intorobotics.com/35-robots-in-agriculture/ (accessed
September 2018). “Robotics: Types of Robots.” ElectronicsTeacher.com.
http://www.electronicsteacher.com/robotics/typeof-
robots.php (accessed September 2018). Rosencrance, L. (2018 May 31). “Tabletop Grapes to Get Picked by
Robots in India, with Help from Virginia Tech.” Robotics Business
Review.
https://www.roboticsbusinessreview.com/agriculture/tableto
p-grapespicked-robots-india-virginia-tech/ (accessed September
2018).
Schuster, W. M. (2018). “Artificial Intelligence and Patent Ownership.”
Washington & Lee L. Rev., 75. Shadbolt, P. (2015, February 15). “U.S. Navy Unveils Robotic
Firefighter.” CNN. https://www.cnn.com/2015/02/12/
tech/mci-saffir-robot/index.html (accessed September 2018). “Shipboard Autonomous Firefighting Robot – SAFFiR.” (2015,
February 4). YouTube. https://www.youtube.com/
watch?time_continue=252&v=K4OtS534oYU (accessed
September 2018). Simon, M. (2018, May 17). “The Wired Guide to Robots.” Wired.
https://www.wired.com/story/wired-guide-torobots/ (accessed
September 2018). “Tabletop Grapes to Get Picked by Robots in India.” Agtechnews.com.
http://agtechnews.com/Ag-RoboticsTechnology/Tabletop-
Grapes-to-Get-Picked-by-Robotsin-India.html (accessed
September 2018). “The da Vinci® Surgical System.” (2015, September). Da Vinci Surgery.
http://www.davincisurgery.com/da-vinci-surgery/ da-vinci-
surgical-system/ (accessed September 2018). “Types of Robots.” (2018). RoverRanch. https://prime.
jsc.nasa.gov/ROV/types.html (accessed September 2018). Westlund, J. K., J. M. Lee, J. Plummer, L. Faridia, F. Gray, J. Berlin, M.
Quintus-Bosz, H. Harmann, R. Hess, M. Dyer, S. dos Santos, K.
Adalgeirsson, S. Gordon, G. Spaulding, S. Martinez, M. Das, M.
Archie, M. Jeong, & C. Breazeal, C. (2016). “Tega: A Social Robot.”
Video Presentation. Proceedings of the Eleventh ACM/IEEE International
Conference on Human Robot Interaction.
White, T. (2015, February 4). “Making Sailors ‘SAFFiR’ – Navy Unveils Firefighting Robot Prototype at Naval Tech EXPO.”
America’s Navy. https://www.navy.mil/
submit/display.asp?story_id=85459 (accessed September 2018). Zimberoff, L. (2018, June 21). “A Burger Joint Where Robots Make Your
Food.” https://www.wsj.com/articles/ a-burger-joint-where-
robots-make-your-food- 1529599213 (accessed September 2018).
C H A P T E R
11
Group Decision Making,
Collaborative Systems,
and AI Support
LEARNING OBJECTIVES
■■ Understand the basic concepts and processes of ■■ Describe collective intelligence and its role in group work,
communication, and collaboration decision making ■■ Describe how computer systems facilitate team ■■ Define crowdsourcing and explain how it supports communication
and collaboration in an enterprise decision making and problem solving ■■ Explain the concepts and importance of the time/ ■■ Describe the role of AI in supporting collaboration, place
framework group work, and decision making ■■ Explain the underlying principles and capabilities ■■ Describe human–machine collaboration of groupware,
such as group support systems (GSS) ■■ Explain how teams of robots work
■■ Understand how the Web enables collaborative computing and
group support of virtual meetings
n this chapter, we present several topics related to group decision support and collaboration.
People work together, and groups (or teams) make many of the complex decisions in
organizations. The increase in organizational decision-making complex-
ity drives the need for meetings and group work. Supporting group work in which team
members may be in different locations and working at different times emphasizes the important
aspects of communications, computer-mediated collaboration, and workplace methodologies.
Group support is a critical aspect of decision support systems (DSS). Effective computer-supported
group support systems have evolved to increase gains and decrease losses in task performance and
underlying processes. New tools and methodology are used to support teamwork. These include
collective intelligence, crowdsourcing, and different types of AI. Finally, human–machine and
machine–machine collaboration
I
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 53
610
are increasing the power of collaboration and problem solving. All these are presented in the following sections:
11.1 Opening Vignette: Hendrick Motorsport Excels with Collaboration Teams 611
11.2 Making Decisions in Groups: Characteristics, Processes, Benefits, and Dysfunctions 613
11.3 Supporting Group Work and Team Collaboration with Computerized Systems 616
11.4 Electronic Support to Group Communication and Collaboration 619
11.5 Direct Computerized Support for Group Decision Making 623
11.6 Collective Intelligence and Collaborative Intelligence 629
11.7 Crowdsourcing as a Method for Decision Support 633
11.8 Artificial Intelligence and Swarm AI Support of Team Collaboration and Group Decision Making 636
11.9 Human–Machine Collaboration and Teams of Robots 640
11.1 OPENING VIGNETTE: Hendrick Motorsports Excels
with Collaborative Teams Hendrick Motorsports (HMS) is a leading car racing company (with more than 500 employees) that competes in the Monster
Energy NASCAR Cup Series. HMS’s major objective is to win as many races as possible each year. The company enters four
race cars and their teams. HMS also builds its race cars. This includes building or rebuilding 550 car engines every year. In this
kind of business, teamwork is critical because many different people with different skills and knowledge and several
professional teams contribute to the success of the company.
THE OPERATIONS
HMS is engaged in car races all over the United States during the racing season (38 weeks a year). The company moves to a
different racetrack every week. During the off-season time (14 weeks), the company analyzes the data obtained, and lessons
learned during the latest racing seasons, and prepares for the following season. The company’s headquarters contains 19
buildings scattered over 100 acres.
THE PROBLEMS DURING THE RACING SEASON
The company needs to make quick decisions during races—some in real time, sometimes in a split second. Different team
members need to participate, and they are in different locations. Communication and collaboration are critical.
Car racing is based on teamwork, drivers, engineers, planners, mechanics, and others who participate. Members must
communicate and collaborate to make decisions.
The environment is too noisy to talk during a race. However, team members need to share data, graphs, and images, and
chat quickly. Several decisions need to be made in real time that will help win races (e.g., how much fuel to add in the next few
seconds to a car in the middle of the race). Team members must communicate and share data, including visual. It takes about
45–50 seconds for a car to complete a 2.5-mile lap at Daytona 500. During the race, top engineers need to communicate
constantly with the fuelers. Lastminute data are common during the racing session.
Any knowledge acquired in each lap can be used to improve the next one. In races, fueling decisions are critical. There
are many other decisions to be made during the racing season. For example, after each race, the company needs to move a
large crew with equipment and supplies from one location to the next (38 different venues). Moves need to be fast, efficient,
and economical. Again, teamwork, as well as coordination, is needed.
OFF-SEASON PROBLEMS
There are 14 weeks to prepare for the next season. In addition, there is a considerable amount of data to analyze, simulate,
discuss, and manipulate. For this, people need not only communication and collaboration tools but also analytics of different
types.
54 Part IV • Robotics, Social Networks, AI and IoT
THE SOLUTION
HMS decided to use Microsoft Teams, which is a chat-based platform, for team workspace in Microsoft Office 365. This
platform is used as a communication hub for team members at the race tracks and at any other location in the organization.
Microsoft Teams stores data in different formats in its Teams workspace. Therefore, car crews, engineers, and mechanics
can make split-second decisions that may help win races. This also enables computational analysis in a central place.
Microsoft Teams includes several subprograms and is easily connected to other software in Office 365. Office 365
provides several other tools that increase collaboration (e.g., SharePoint). For example, in the HSM solution, there is a working
link to Excel as well as to SharePoint. Also, One Note of Teams is used to share meeting notes. Before Teams, the company
used Slack (Section 11.4), but Slack did not provide enough security and functions.
Members need to share and discuss the massive amount of data accumulated during the racing season. Note that several
employees have multiple skills and tasks. The solution included the creation of a collaboration hub for concurrent projects. Note
that each different project may require different talents and data, depending on the project’s type. Also, the s olution involves
other information technology (IT) tools. For example, HMS uses Power BI dashboard to communicate data visually. Some
data can be processed as Excel-based spreadsheets.
Microsoft Teams is also available as a mobile app. Each team’s data file is available on the track at home and even under
a car. So, the software package is able to respond to important situations right away.
The Results
The major results were improved productivity, smoother communication, easier collaboration, and reduction of the need for
the time consumed in face-to-face meetings. People can chat online, seeing their partners without leaving their physical
workplace. The company admits that without Teams, it would not have been able to accomplish its success. Today, Teams
has everything the company needs at its fingertips.
u QUESTIONS FOR THE OPENING VIGNETTE
1. What were the major drivers for the use of Microsoft’s Teams?
2. List some discussions held during the racing season, and how they were supported by the technology.
3. List decisions held during the off-season, and how they were supported by the technology.
4. Discuss why Microsoft Teams was selected, and explain how it supports teamwork group decision making.
5. Trace communication and collaboration within and between groups.
6. Specify the function of Microsoft Teams workspace.
7. Watch the video at youtube.com/watch?time_continue=108&v=xnFdM9IOaTE and summarize its content.
WHAT WE CAN LEARN FROM THIS VIGNETTE
The first lesson is that many tasks today must be done by collaborating teams in order to succeed. Second, time is critical;
therefore, companies must use technology to speed operations and facilitate communication and collaboration in teamwork.
Third, it is possible to use existing software for support, but it is better to use a major vendor that has additional products that
can supplement the collaboration/communication software. Fourth, chatting can expedite communication, and visual
technology support can be useful. Fifth, team members belong to diverse units and have diverse skills. The software brings
them together. Team members should have clear goals and understand how to achieve them. Finally, collaboration can be
both within and between groups.
Sources: Compiled from Ruiz-Hopper (2016) and Microsoft (2017).
11.2 MAKING DECISIONS IN GROUPS: CHARACTERISTICS,
PROCESS, BENEFITS, AND DYSFUNCTIONS
Managers and other knowledge workers continuously make decisions, design products, develop policies and strategies, create
software systems, and so on. Frequently they do it in groups. When people work in groups (i.e., teams), they perform group
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 55
work or teamwork. Group work refers to work done by two or more people together. One aspect of group work is group
decision making.
Group decision making refers to a situation in which people make decisions together. Let’s first look at the
characteristics of group work.
Characteristics of Group Work
The following are some of the functions and characteristics of group work:
• Group members may be located in different places.
• Group members may work at different times.
• Group members may work for the same organization or different organizations.
• A group can be permanent or temporary.
• A group can be at one managerial level or span several levels.
• A group can create synergy (leading to process and task gains) or result in conflict.
• A group can generate productivity gains and/or losses.
• A group’s task may have to be accomplished very quickly.
• It may be impossible or too expensive for all team members to meet in one place at the same time, especially when the
meeting is called for emergency purposes.
• Some of the groups’ needed data, information, or knowledge may be located in several sources, some of which may be
external to the organization.
• The expertise of a group’s team members may be needed.
• Groups perform many tasks; however, groups of managers and analysts frequently concentrate on decision making or
problem solving.
• The decisions made by a group are easier to implement if supported by all (or at least most) members.
• Group work has many benefits and, unfortunately, some possible dysfunctions.
• Group behaviors are influenced by several factors and may affect group decisions.
56 Part IV • Robotics, Social Networks, AI and IoT
FIGURE 11.1 The Process of Group Decision Making.
Types of Decisions Made by Groups
Groups are usually involved in two major types of decision making:
1. Making a decision together.
2. Supporting activities or tasks related to the decision-making process. For example, the group may select criteria for
evaluating alternative solutions, prioritizing possible ones, and helping design strategy to implement them.
Group Decision-Making Process
The process of group decision making is similar to that of the general decision-making process described in Chapter 1 but it
has more steps. Steps of the group decision-making process are illustrated in Figure 11.1.
Step 1. Prepare for meetings regarding the agenda, time, place, participants, and schedule.
Step 2. Determine the topic of the meeting (e.g., problem definition).
Step 3. Select participants for the meeting.
Step 4. Select criteria for evaluating the alternatives and the selected solution.
Step 5. Generate alternative ideas (brainstorm).
Step 6. Organize the ideas generated into similar groups.
Step 7. Evaluate the ideas, discuss, and brainstorm.
Step 8. Select a short list (finalists).
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 57
Step 9. Select a recommended solution.
Step 10. Plan implementation of the solution.
Step 11. Implement the solution.
The process is shown as sequential, but as shown in Figure 11.1, some loops are possible. Also, if no solution is found, the
process may start again.
GROUP DECISION FACTS When a group is going through the steps shown in Figure 11.1, the following is usually true:
• The decisions made need to be implemented.
• Group members are typically of equal or nearly equal status.
• The outcome of a meeting depends partly on the knowledge, opinions, and judg-ments of its participants and the
support they give to the outcome.
• The outcome of a meeting depends on the composition of the group and on the decision-making process it uses.
• Group members settle differences of opinions either by the ranking person present or through negotiations or
arbitration.
• The members of a group can be in one place, meeting face-to-face, or they can be a virtual team, in which case they
are in different places meeting electronically. They can also meet at different times.
Benefits and Limitations of Group Work
Some people endure meetings (the most common form of group work) as a necessity; others find meetings to be a waste of
time. Many things can go wrong in a meeting. Participants may not clearly understand its purpose, may lack focus, or may
have hidden agendas. Many participants may be afraid to speak up, or a few may dominate the discussions. Misunderstandings
occur because of different interpretations of language, gestures, or expression. Technology Insight 11.1 provides a list of
factors that can hinder the effectiveness of a manually managed meeting. Besides being challenging, teamwork is also
expensive. A meeting of several top managers or executives can cost thousands of dollars.
Group work may have potential benefits (process gains) or drawbacks (process losses). Process gains are the benefits
of working in groups. The unfortunate dysfunctions that may occur when people work in groups are called process losses.
Examples of each are listed in Technology Insight 11.1.
TECHNOLOGY INSIGHT 11.1 Benefits and Dysfunctions of Working in Groups
The following are the possible major benefits and dysfunctions of group works.
Dysfunctions of Face-to-Face Group Process Benefits of Working in Groups (Process
Gains) (Process Losses)
• It provides learning. Groups are better than • Social pressures of conformity may result in individuals at understanding problems.
They can groupthink (i.e., people begin to think alike teach each other. and not tolerate new ideas; they yield to
conformance pressure).
• People readily take ownership of problems and • It is a time-consuming, slow process. their solutions. • Some relevant information
could be missing.
• Group members have their egos embedded in • A meeting can lack coordination, have a poor the final decision, so they are
committed it. agenda, or be poorly planned.
Benefits of Working in Groups (Process Gains)
Dysfunctions of Face-to-Face Group Process (Process Losses)
• Groups are better than individuals at catching errors. • A meeting may be dominated by time, topic, opinion of one or a few individuals, or fear of contributing
because of the possibility of conflicts.
58 Part IV • Robotics, Social Networks, AI and IoT
• A group has more information and knowledge than
any one member does. Members can combine their
knowledge to create new knowledge. More and
more creative alternatives for problem solving can
be generated, and better solutions can be derived
(e.g., through brainstorming).
• Some group members can tend to influence the
agenda while some try to rely on others to do
most of the work (free riding). The group may
ignore good solutions, have poorly defined goals,
or be composed of the wrong participants.
• A group may produce synergy during problem solving,
therefore the effectiveness and/or quality of group
work can be greater than the sum of what individual
members produce.
• Some members may be afraid to speak up. • The group may be unable to reach consensus. • The group may lack focus.
• Working in a group may stimulate the creativity of
the participants and the process. • There can be a tendency to produce poorquality
compromises.
• Working together could allow a group to have better
and more precise communication. • There is often nonproductive time (e.g., socializing,
preparing, waiting for latecomers).
• Risk propensity is balanced. Groups moderate high-risk
takers and encourage conservatives. • There can be a tendency to repeat what has
already been said (because of failure to
remember or process).
• Meeting costs can be high (e.g., travel,
participation time spent).
• There can be incomplete or inappropriate use of information.
• There can be too much information (i.e., information overload).
• There can be incomplete or incorrect task
analysis.
• There can be inappropriate or incomplete
representation in the group.
• There can be attention or concentration blockage.
u SECTION 11.2 REVIEW QUESTIONS
1. Define group work.
2. List five characteristics of group work.
3. Describe the steps of group decision making.
4. List the major activities that occur in group work.
5. List and discuss five benefits of group work.
6. List and discuss five dysfunctions of group-made decisions.
11.3 SUPPORTING GROUP WORK AND TEAM COLLABORATION
WITH COMPUTERIZED SYSTEMS
When people work in teams, especially when the members are in different locations and may work at different times, they
need to communicate, collaborate, and access a diverse set of information sources in multiple formats. This makes meetings,
especially virtual ones, complex with an increased chance for process losses. Therefore, it is important to follow certain
processes and procedures for conducting meetings.
Group work may require different levels of coordination. Sometimes a group operates at the individual work level with
members making individual efforts that require no c oordination. As with a team of sprinters representing a country
participating in a 1 00-meter dash, group productivity is simply the best of the individual results. At other times, group members
may interact in coordination. At this level, as with a team in a relay race, the work requires careful coordination between
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 59
otherwise independent individual efforts. Sometimes a team may operate at the concerted work level. As in a rowing race,
teams working at this level must make a continuous concerted effort to be successful. Different mechanisms support group
work at different levels of coordination.
Most organizations, small and large, use some computer-based communication and collaboration methods and tools to
support people working in teams or groups. From e-mails to mobile phones and Short Message Service (SMS), as well as
conferencing technologies, such tools are an indispensable part of today’s work life. We next highlight some related
technologies and applications.
Overview of Group Support Systems (GSS)
For groups to collaborate effectively, appropriate communication methods and technologies are needed. We refer to these
technologies as group support systems (GSS). The Internet and its derivatives (i.e., intranets, Internet of Things [IoT], and
extranets) are the infrastructures on which much communication and collaboration occurs. The Web supports intra- and inter-
organizational collaborative decision making.
Computers have been used for several decades to facilitate group work and decision making. Lately, collaborative tools
have received more attention due to their increased capabilities and ability to save time and money (e.g., on travel cost) and to
expedite decision making. Computerized tools can be classified according to time and place categories.
Time/Place Framework
The tools used to support collaboration, groups, and the effectiveness of collaborative computing technology depend on the
location of the group members and on the time that shared information is sent and received. DeSanctis and Gallupe (1987)
proposed a framework for classifying IT communication support technologies. In this framework, communication is divided
into four cells, which are shown with representative computerized support technologies in Figure 11.2. The four cells are
organized along two d imensions—time and place.
When information is sent and received almost simultaneously, the communication is in synchronous (real-time) mode.
Telephones, instant messaging (IM), and face-to-face meetings are examples of synchronous communication. Asynchronous
communication occurs when the receiver gets (or views) the information, such as an e-mail, at a different time than it was sent.
The senders and the receivers can be in the same place or in different places.
As shown in Figure 11.2, time and place combinations can be viewed as a four-cell matrix, or framework. The four cells
of the framework are as follows:
• Same time/same place. Participants meet face-to-face, as in a traditional meeting, or decisions are made in a specially
equipped decision room. This is still an important way to meet even when Web-based support is used because it is
sometimes critical for participants to leave their regular workplace to eliminate distractions.
• Same time/different place. Participants are in different places, but they communicate at the same time (e.g., with
videoconferencing or IM).
• Different time/same place. People work in shifts. One shift leaves information for the next shift.
• Different time/(any place) different place (any place). Participants are in different places, and they send and
receive information at different times. This occurs when team members are traveling, have conflicting schedules, or
work in different time zones.
60 Part IV • Robotics, Social Networks, AI and IoT
Same Time Different Time
Same
Place
• Instant Messaging • Chatting, decision
room • Web-based GSS • Multimedia
presentation system •
Whiteboard • Document sharing • Workspace
• GSS in a decision
room • Web-based GSS • Workflow
management system • Document sharing • E-mail, V-mail • Videoconferencing
playback
• Web-based GSS • Virtual whiteboard • Document sharing • Videoconferencing • Audio-conferencing • Computer conferencing • E-mail, V-mail • Virtual workspace
• Web-based GSS • Virtual whiteboard • Document sharing • E-mail, V-mail • Workflow
management system • Computer
conferencing with memory • videoconferencing
playback • Voice memo
Different
Place
FIGURE 11.2 The Time/Place Framework.
Groups and group work in organizations are proliferating. Consequently, groupware continues to evolve to support
effective group work, mostly for communication and collaboration (Section 11.4).
Group Collaboration for Decision Support
In addition to making decisions, groups also support decision-making subprocesses such as brainstorming. Collaboration
technology is known to be the driving force for productivity increase and boosting people and organizational performance.
Groups collaborate to make decisions in several ways. For example, groups provide assistance for the steps in Figure 11.1.
Groups can help to identify problems, to assist in choosing criteria for selecting solutions, generating solutions (e.g.,
brainstorming), evaluating alternatives, and assisting in the selection of the best solution and implementing it. The group can
be involved in one step or in several steps. In addition, it can collect the necessary data.
Many technologies can be used for collaboration; several of them are computerized and are described in several sections
in this chapter.
Studies indicate that adopting collaboration technologies increases productivity: for example, visual collaborative
solutions increase employees’ satisfaction and productivity.
COMPUTERIZED TOOLS AND PLATFORMS We divide the computerized support into two parts. In Section 11.4, we present the
major support of generic activities in communication and collaboration. Note that hundreds, maybe thousands, of commercial
products are available to support communication and collaboration. We cover only a sample here.
Section 11.5 covers direct support of decision making, both to the entire process and to the major steps in the process.
Note that some products, such as Microsoft Teams, which is cited in the opening vignette, support both generic activities and
those in the decision-making process.
u SECTION 11.3 REVIEW QUESTIONS
1. Why do companies use computers to support group work?
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 61
2. What is GSS?
3. Describe the components of the time/place framework.
4. Describe the importance of collaboration for decision making.
11.4 ELECTRONIC SUPPORT FOR GROUP COMMUNICATION
AND COLLABORATION
A large number of tools and methods are available to facilitate group work, e-collaboration, and communication. The following
sections present only some tools that support the process. Our attention here is on indirect support to decision making. In
Section 11.5, we cover direct support.
Groupware for Group Collaboration
Many computerized tools have been developed to provide group support. These tools are called groupware because their
primary objective is to support group work indirectly as described in this section. Some e-mail programs, chat rooms, IM, and
teleconferences provide indirect support.
Groupware provides a mechanism for team members to share opinions, data, information, knowledge, and other
resources. Different computing technologies support group work in different ways depending on the task and size of the
group, the security required, and other factors.
CATEGORIES OF GROUPWARE PRODUCTS AND FEATURES Many groupware products to enhance the collaboration of a small and
large number of people are available on the Internet or intranets. A prime example is Microsoft’s Teams (opening vignette).
The features of groupware products that support commutation, collaboration, and coordination are listed in Table 11.1. What
follows are brief definitions of some of those features.
Synchronous versus Asynchronous Products
The products and features described in Table 11.1 may be synchronous or asynchronous. Web conferencing and IM, as well
as voice-over IP (VoIP), are associated with the synchronous mode. Methods associated with asynchronous modes include e-
mail and online workspaces where participants can collaborate while working at different times. Google Drive
(drive.google.com) and Microsoft SharePoint (http://office.microsoft.com/en-us/ SharePoint/collaboration-
software-SharePoint-FX103479517.aspx) allow users to set up online workspaces for storing, sharing, and working
collaboratively on different types of documents. Similar products are Google Cloud Platform and Citrix Workspace Cloud.
Companies such as Dropbox.com provide an easy way to share documents. Similar systems, such as photo sharing
(e.g., Instagram, WhatsApp, Facebook), are evolving for consumer home use.
TABLE 11.1 Groupware Products and Features
General (Can Be Either Synchronous or Asynchronous)
• Built-in e-mail, messaging system
• Browser interface
• Joint Web page creation
• Active hyperlink sharing
• File sharing (graphics, video, audio, or other)
• Built-in search functions (by topic or keyword)
• Workflow tools
• Corporate portals for communication, collaboration, and search
• Shared screens
• Electronic decision rooms
• Peer-to-peer networks
62 Part IV • Robotics, Social Networks, AI and IoT
Synchronous (Same Time)
• IM
• Videoconferences, multimedia conferences
• Audioconferences
• Shared whiteboard, smart whiteboard
• Instant videos
• Brainstorming
• Polling (voting) and other decision support (activities such as consensus building, scheduling)
• Chats with people
• Chats with bots
Asynchronous (Different Times)
• Virtual workspaces
• Tweets
• Ability to receive/send e-mail, SMS
• Ability to receive notification alerts via e-mail or SMS
• Ability to collapse/expand discussion threads
• Message sorting (by date, author, or read/unread)
• Auto responders
• Chat session logs
• Electronic bulletin boards, discussion groups
• Blogs and wikis
• Collaborative planning and/or design tools
Groupware products are either stand-alone, supporting one task (such as videoconferencing), or integrated, including
several tools. In general, groupware technology products are fairly inexpensive and can easily be incorporated into existing
information systems.
Virtual Meeting Systems
The advancement of Web-based systems opens the door for improved electronically supported virtual meetings with the
virtual team members in different locations, even in different countries. Online meetings and presentation tools are provided
by tools such as webex, GoToMeeting.com, Skype.com, and many others. These systems feature Web seminars (popularly
called Webinars), screen sharing, audioconferencing, videoconferencing, polling, question–answer sessions, and so on.
Microsoft Office 365 includes a built-in virtual meeting capability. Even smartphones now have sufficient interaction
capabilities to allow live meetings through applications such as FaceTime.
COLLABORATIVE WORKFLOW Collaborative workflow refers to software products that address project-oriented and collaborative
processes. They are administered centrally yet are capable of being accessed and used by workers from different departments
and from different physical locations. The goal of collaborative workflow tools is to empower knowledge workers. The focus
of an enterprise solution for collaborative workflow is on allowing workers to communicate, negotiate, and collaborate within
an integrated environment. Some leading vendors of collaborative workflow applications are FileNet and Action Technologies.
Collaborative workflow is related to but different than collaborative workspace.
DIGITAL COLLABORATIVE WORKSPACE: PHYSICAL AND VIRTUAL A collaborative workspace is where people can work together
from any location at the same or at a different time. Originally, it was a physical conference room that teams used for
conducting meetings. It was expanded to be a shared workspace, also known as “coworking space.” Some of these are in
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 63
companies; others are offered for rent. Different computerized technologies are available to support group work in a physical
structure. For 12 benefits of collaborative workspace, see Pena (2017).
A virtual collaboration workspace is an environment equipped with digital support by which group members who are in
different locations can share information and collaborate. A simple example is Google Drive, which enables sharing
spreadsheets.
Collaborative workspace enables tech-savvy employees to access systems and tools from any device they need. People
can work together in a secure way from anywhere. The digital workspace increases team productivity and innovation. It
empowers employees and unlocks innovation. It allows workers to reach other people for collaborative work. For details and
other collaboration technologies, see de Lares Norris (2018).
Example
PricewaterhouseCoopers (PwC) built an ideation war room in its Paris office as a large, immersive collaboration facility to
support customer meetings.
MAJOR VENDORS OF VIRTUAL WORKSPACE Products by five major vendors follow:
• Google Cloud Platform is deployed on the “cloud,” so it is offered as a platform-as-aservice (PaaS). Google is also known
for its Flexible Workspace product.
• Citrix Workspace Cloud is also deployed on the “cloud.” Citrix is known for its G oToMeeting collaboration tool. Citrix
Workspace Cloud users can manage secure digital workplaces on Google Cloud.
• Microsoft Workspace is part of Office 365.
• Cisco’s Webex, a popular collaboration package including Meeting.
• Slack workspace is a very popular workspace.
ESSENTIALS OF SLACK Slack workspace is a digital space on which teammates share, communicate, and collaborate on work. It
can be in one organization, or large organizations may have multiple interconnected Slack spaces.
Each workspace includes several topical channels. These can be organized as public, private, or shared. The remaining
components of Slack are messages, searches, and notifications. There are four groups of people involved with Slack: workspace
owners, workspace administrators, members, and guests. For a Slack Guide, see get.slack.help/ hc/en-
us/articles/115004071768-What-is-Slack-.
Slack has many key features and can deliver secure virtual apps to almost any device.
Collaborative Networks and Hubs
Traditionally, collaboration has taken place among supply chain members, frequently those that were close to each other (e.g.,
a manufacturer and its distributor or a distributor and a retailer). Even if more partners were involved, the focus was on the
optimization of information and product flow between existing nodes in the traditional supply chain. Advanced methods, such
as collaborative planning, forecasting, and replenishment (CPFR), do not change this basic structure.
Traditional collaboration results in a vertically integrated supply chain. However, Web technologies can fundamentally
change the shape of the supply chain, the number of players in it, and their individual roles. In a collaborative network, partners
at any point in the network can interact with each other, bypassing what are traditional partners. Interaction may occur among
several manufacturers or distributors as well as with new players, such as software agents that act as aggregators.
Collaborative Hubs
The purpose of a collaborative hub is to be a center point for group collaboration.
Collaborative hub platforms need to enable participants’ interactions to unfold in various forms online.
64 Part IV • Robotics, Social Networks, AI and IoT
Example: Surface Hub for Business by Microsoft
This product connects individuals wherever they are and whenever they want to use a digital whiteboard and integrating
software and apps. It helps to create a collaboration workplace where multiple devices are connected wirelessly to create a
powerful work environment.
Social Collaboration
Social collaboration refers to collaboration conducted within and between socially oriented groups. It is a process of group
interactions and information/knowledge sharing while attempting to attain common goals. Social collaboration is usually done
on social media sites, and it is enabled by the Internet, IoT, and diversified social collaboration software. Social collaboration
groups and schemes can take many different shapes. For images, conduct a Google search for “images of social collaboration.”
COLLABORATION IN SOCIAL NETWORKS Business-related collaboration is most evidenced on Facebook and LinkedIn. However,
Instagram, Pinterest, and Twitter support collaboration as well.
• Facebook. Facebook’s Workspace facebook.com/workspace is used by hundreds of thousands of companies utilizing
its features, such as “groups,” to support team members. For example, 80 percent of Starbucks store managers use this
software.
• LinkedIn. LinkedIn provides several collaboration tools to its members. For example, LinkedIn Lookup provides several
tools. Also, LinkedIn is a Microsoft company and it provides some integrated tools. The creation of subgroups of interest
is a useful facilitator.
SOCIAL COLLABORATION SOFTWARE FOR TEAMS In addition to the generic collaboration software that can be used by two people
and by teams, there are software platforms specifically for forming teams and supporting their activities. A few popular
examples according to collaboration-software.financesonline.com/c/social-collaborationsoftware/ are Wrike, Ryver,
Azendoo, Zimbra social platform, Samepage, Zoho, Asana, Jive, Chatter, and Social Tables. For viewing the best social
collaboration software by category, see technologyadvice.com/social-collaboration-software/.
Sample of Popular Collaboration Software
As noted earlier, there are hundreds or maybe thousands of communication and collaboration software products. Furthermore,
their capabilities are ever changing. Given that our major interest is decision-making support, we provide only a small sample
of these tools. We use the classification and example of Time Doctor, using the 2018 list (see Digneo, 2018).
• Communication tools: Yammer (social collaboration), Slack, Skype, Google Hangouts, GoToMeeting
• Design tools: InVision, Mural, Red Pen, Logo Maker
• Documentation tools: Office Online, Google Docs, Zoho
• File-sharing tools: Google Drive, Dropbox, Box
• Project management tools: Asana, Podio, Trello, WorkflowMax, Kanban Tool,
• Software tools: GitHub, Usersnap,Workflow tools: Integrity, BP Logix
OTHER TOOLS THAT SUPPORT COLLABORATION AND/OR COMMUNICATION
Notejoy (makes collaborative notes for team).
Kahootz (brings stakeholders together to form communities of interest).
Nowbridge (offers team connectivity, ability to see participants).
Walkabout Workplace (is a 3D virtual office for remote teams).
RealtimeBoard (is a enterprise visual collaboration).
Quora (is a popular place for posting questions to the crowd).
Pinterest (provides an e-commerce workspace that allows collection of text and images on selected topics).
IBM connection closed (offers a comprehensive communication and collaboration tool set).
Skedda (schedules space for coworking)
Zinc (is a social collaboration tool)
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 65
Scribblar (is an online collaboration room for virtual brainstorming) Collokia (is a machine learning
platform for workflow) For additional tools, see Steward (2017).
u SECTION 11.4 REVIEW QUESTIONS
1. Define groupware.
2. List the major groupware tools and divide them into synchronous and asynchronous types.
3. Identify specific tools for Web conferencing and their capabilities.
4. Describe collaborative workflow.
5. What is collaborative workspace? What are its benefits?
6. Describe social collaboration.
11.5 DIRECT COMPUTERIZED SUPPORT FOR GROUP
DECISION MAKING
Decisions are made frequently at meetings, some of which are called in order to make a one-time specific decision. For
example, directors are elected at shareholder meetings, organizations allocate budgets in meetings, cities decide which
candidates to hire for their top positions, and the U.S. federal government meets periodically to set the short-term interest
rate. Some of these decisions are complex; others can be controversial, as in resource allocation by a city government. Process
dysfunctions can be significantly large in such situations; therefore, computerized support has often been suggested to mitigate
these controversies. These computer-based support systems have appeared in the literature under different names, including
group decision support systems (GDSS), group support systems (GSS), computersupported collaborative work (CSCW), and electronic meeting
systems (EMS). These systems are the subject of this section. In addition to supporting entire processes, there are tools that
support one or several activities in the group decision-making process (e.g., brainstorming).
Group Decision Support Systems (GDSS)
During the 1980s, researchers realized that computerized support to managerial decision making needed to be expanded to
groups, because major organizational decisions are made by groups, such as executive committees and special task forces. The
result was the creation of the group decision support systems methodology.
A group decision support system (GDSS) is an interactive computer-based system that facilitates the solution of
semistructured or unstructured problems by a group of decision makers. The goals of GDSS are to improve the productivity
of decision-making meetings by speeding up the decision-making process and/or to increase the quality of the resulting
decisions.
MAJOR CHARACTERISTICS AND CAPABILITIES OF A GDSS GDSS characteristics follow:
• It supports the process of group decision makers mainly by providing automation of subprocesses (e.g., brainstorming)
and using information technology tools.
• It is a specially designed information system, not merely a configuration of already existing system components. It can
be designed to address one type of problem or make a variety of group-level organizational decisions.
• It encourages generation of ideas, resolution of conflicts, and freedom of expres-sion. It contains built-in mechanisms
that discourage development of negative group behaviors, such as destructive conflict, miscommunication, and
groupthink.
The first generation of GDSS was designed to support face-to-face meetings in a decision room . Today, support is provided
mostly over the Web to virtual teams. A group can meet at the same time or at different times. GDSS is especially useful when
controversial decisions have to be made (e.g., resource allocation, determining which individuals to lay off). GDSS applications
require a facilitator for one physical place or a coordinator or leader for online virtual meetings.
GDSS can improve the decision-making process in various ways. For one, GDSS generally provides structure to the
meeting planning process, which keeps a group meeting on track, although some applications permit the group to use
66 Part IV • Robotics, Social Networks, AI and IoT
unstructured techniques and methods for idea generation. In addition, GDSS offers rapid and easy access to external and
stored information needed for decision making. It also supports parallel processing of information and idea generation by
participants and allows asynchronous computer discussion. GDSS makes possible larger group meetings that would otherwise
be unmanageable; having a larger group means that more complete information, knowledge, and skills can be represented in
the meeting. Finally, voting can be anonymous with instant results, and all information that passes through the system can be
recorded for future analysis (producing organizational memory).
Over time, it became clear that supporting teams needed to be broader than GDSS has beed supported in a decision
room. Furthermore, it became clear that what was really needed was support for virtual teams, both in different place/same
time and different place/different time situations. Also, it became clear that teams needed indirect support in most decision-
making cases (e.g., help in searching for information or in collaboration) rather than direct support for the decision-making
process. Although GDSS expanded to virtual team support, it was unable to meet all the other needs. In addition, the traditional
GDSS was designed to deal with contradictory decisions when conflicts were likely to arise. Thus, a new generation of GDSS
that supports collaboration work was needed. As we will see later, products such as Stormboard provide those needs.
Characteristics of GDSS
There are two options for deploying GDSS technology: (1) in a special-purpose decision room and (2) as Internet-based
groupware with client programs running wherever the group members are.
DECISION ROOMS The earliest GDSS was installed in expensive, customized, specialpurpose facilities called decision rooms
(or electronic meeting rooms) that had PCs and a large public screen at the front of each room. The original idea was that only
executives and high-level managers would use the expensive facility. The software in an electronic meeting room usually ran
over a local area network (LAN), and these rooms were fairly plush in their furnishings. Electronic meeting rooms were
structured in different shapes and sizes. A common design was a room equipped with 12 to 30 networked PCs, usually recessed
into the desktop (for better participant viewing). A server PC was attached to a large screen projection system and connected
to the network to display the work at individual workstations and aggregated information from the facilitator’s workstation.
Breakout rooms equipped with PCs connected to the server, in which small subgroups could consult, were sometimes located
adjacent to the decision room. The output from the subgroups was able to be displayed on the large public screen. A few
companies offered such rooms for a daily rent. Only a few upgraded rooms are still available today, usually for high rent.
INTERNET-BASED GROUPWARE Since the late 1990s, the most common approach to GSS and GDSS delivery has been to use an
Internet-based groupware that allows group members to work from any location at any time (e.g., WebEx, GoToMeeting,
Adobe Connect, IBM Connections, Microsoft Teams). This groupware often includes audio conferencing and
videoconferencing. The availability of relatively inexpensive groupware, as described in Section 11.4, combined with the power
and low cost of computers and the availability of mobile devices, makes this type of system very attractive.
Supporting the Entire Decision-Making Process
The process that was illustrated in Figure 11.1 can be supported by a variety of software products. In this section, we provide
an example of one product, Stormboard, that supports several aspects of that process.
Example: Stormboard
Stormboard stormboard.com provides support for different brainstorming and group decision-making configurations. The
following is the product’s sequence of activities:
1. Define the problem and the users’ objectives (what they are hoping to achieve).
2. Brainstorm ideas (to be discussed later).
3. Organize the ideas in groups of similar flavor, look for patterns, and select only viable ideas.
4. Collaborate, refine concepts, and evaluate (using criteria) the meeting’s objectives.
5. The software enables users to prioritize proposed ideas by focusing on the selection criteria. It lets all participants
express their thinking and directs the team to be cohesive.
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 67
6. It presents a short list of superior ideas.
7. The software suggests the best idea and recommends implementation.
8. It plans the project implementation.
9. It manages the project.
10. It periodically reviews progress.
For a video, see youtube.com/watch?v=0buRzu4rhJs.
COMPREHENSIVE GROUPWARE TOOLS INCLUDING THINKTHANK Although many capabilities that enable group decision support are
embedded in common software tools for office productivity such as Microsoft Office 365, it is instructive to learn about
specific software that illustrates some of groupware’s unique capabilities. MeetingRoom was one of the first comprehensive,
same time/same place electronic meeting packages. Its follow-up product, GroupSystems OnLine, offered similar capabilities,
and it ran in asynchronous mode (anytime/anyplace) over the Web (MeetingRoom ran only over a LAN). GroupSystems’
latest product is ThinkTank, a suite of tools that facilitate the various group decision-making activities. For example, it shortens
cycle time for brainstorming. ThinkTank improves the collaboration of face-to-face or virtual teams through customizable
processes toward the groups’ goals faster and more effectively than in previous product generations. ThinkTank offers the
following:
• It can provide efficient participation, workflow, prioritization, and decision analysis.
• Its anonymous brainstorming for ideas and comment generation is an ideal way to capture the participants’ creativity
and experience.
• The product’s enhanced Web 2.0 user interface ensures that participants do not need special training to join, so they can
focus 100 percent on solving problems and making decisions.
• With ThinkTank, all of the knowledge shared by participants is captured and saved in documents and spreadsheets,
automatically converted to the meeting minutes, and made available to all participants at the end of the session.
Examples: ThinkTank Use (thinktank.net/case-study)
The following are two examples of ThinkTank’s use.
• It enables transformational collaboration between supply chain partners. Their meet-ing was supported by collective
intelligence tools and procedures. Partners agreed on how to cut costs, speed processes, and improve efficiencies. In the
past, there had been no progress on these issues.
• The University of Nebraska and the American College of Cardiology collaborated using ThinkTank tools and procedures
to rethink how electronic health records could be reorganized to help medical consultants save time. Patients’
appointment times were shortened by 5 to 8 minutes. Other improvements also were achieved. Both patient care and
large monetary savings were achieved.
OTHER DECISION-MAKING SUPPORT The following is a list of other types of support provided by intelligent systems:
• Using knowledge systems and a product called Expert Choice Software for dealing with multiple-criteria group decision
making.
• A mediating group decision-making method for infrastructure asset management was proposed by Yoon et al. (2017).
• For a group decision-making support system in logistics and supply chain manage-ment, see Yazdani et al. (2017).
Brainstorming for Idea Generation and Problem Solving
A major activity in group decision making is idea generation. Brainstorming is a process for generating creative ideas. It
involves freewheeling group discussions and spontaneous contribution of ideas for solving problems and making strategy and
resource allocation. Contributors’ ideas are discussed by the members. An attempt is made to generate as many ideas as
possible, no matter how bizarre they look. Generated ideas are discussed and evaluated by the group. There is evidence that
groups not only generate more ideas but also better ones (McMahon et al., 2016). Manually managed brainstorming has some
of the limitations of group work described in Section 11.2. Therefore, computer support is frequently recommended.
68 Part IV • Robotics, Social Networks, AI and IoT
COMPUTER-SUPPORTED BRAINSTORMING Computer programs can support the various brainstorming activities. The support is
usually for online brainstorming, synchronously or asynchronously. Hopefully, electronic brainstorming eliminates many of
the process dysfunctions cited in Section 11.2 and helps in the generation of many new ideas. Brainstorming software can
stand alone or be a part of a general group support package. The major features of software packages follow:
• Creation of a large number of ideas.
• Large group participation.
• Real-time updates.
• Information color coding.
• Collaborative editing.
• Design of brainstorming sessions.
• Idea sharing.
• People participation.
• Idea mapping (e.g., create mind maps).
• Text, video, documents, etc. posting.
• Remote brainstorming.
• Creation of an electronic archive.
• Reduction of social loafing.
The major limitations of electronic software support are increased cognitive load, fear of using new technology, and need for
technical assistance.
COMPANIES THAT PROVIDE ONLINE BRAINSTORMING SERVICES AND SUPPORT FOR GROUP WORK Some companies and the services
and support they provide follow:
• eZ Talks Meetings. Cloud-based tool for brainstorming and idea sharing.
• Bubbl.us. Visual thinking machine that provides a graphical representation of ideas and concepts, helps in idea
generation, and shows where ideas and thoughts overlap (visually, in colors).
• Mindomo. Tool for real-time collaboration that offers integrated chat capability.
• Mural. Tool that enables collecting and sorting of ideas in rich media files. It is designed as a Pinboard that invites
participants.
• iMindQ. Cloud-based service that enables creating mind maps and basic diagrams.
For an evaluation of 28 online brainstorming tools, see blog.lucidmeetings.com/ blog/25-tools-for-online-
brainstorming-and-decision-making-in-meetings/.
ARTIFICIAL INTELLIGENCE SUPPORTS BRAINSTORMING In Chapter 12, we will introduce the use of bots. Some software allows
users to create and post a bot (or avatar) that represents people in order to communicate anonymously. Artificial intelligence
(AI) can also be used for pattern recognition and identifying ideas that are similar to each other. AI is also used in
crowdsourcing (Section 11.7), which is used extensively for idea generation and voting.
Group Support Systems
A group support system (GSS), which was discussed earlier, is any combination of hardware and software that enhances
group work. GSS is a generic term that includes all forms of communication and collaborative computing. It evolved after
information technology researchers recognized that technology could be developed to support many activities that normally
occur at face-to-face meetings when they occur in virtual meetings (e.g., idea generation, consensus building, anonymous
ranking). Also, a focus was made on collaboration rather than on minimizing conflicts.
A complete GSS is considered a specially designed information system software, but today, its special capabilities have
been embedded in standard IT productivity tools. For example, Microsoft Office 365 includes Microsoft Teams (opening
vignette). It also includes the tools for Web conferences. Also, many commercial products have been developed to support
only one or two aspects of teamwork (e.g., videoconferencing, idea generation, screen sharing, wikis).
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 69
HOW GSS IMPROVES GROUP WORK The goal of GSS is to provide support to participants in improving the productivity and
effectiveness of meetings by streamlining and speeding up the decision-making process and/or by improving the quality of
the results. GSS attempts to increase process and task gains and decrease process and task losses. Overall, GSS has been
successful in doing just that. Improvement is achieved by providing support to group members for the generation and
exchange of ideas, opinions, and preferences. Specific features such as the ability of participants in a group to work
simultaneously on a task (e.g., idea generation or voting) and anonymity produce improvements. The following are some
specific GSS support activities:
• Supporting parallel processing of information and idea generation (brainstorming).
• Enabling the participation of larger groups with more complete information, knowl-edge, and skills.
• Permitting the group to use structured or unstructured techniques and methods.
• Offering rapid, easy access to external information.
• Allowing parallel computer discussions.
• Helping participants frame the big picture.
• Providing anonymity, which allows shy people to contribute to the meeting (i.e., to get up and do what needs to be
done).
• Providing measures that help prevent aggressive individuals from controlling a meeting.
• Providing multiple ways to participate in instant anonymous voting.
• Providing structure for the planning process to keep the group on track.
• Enabling several users to interact simultaneously (i.e., conferencing).
• Recording all information presented at a meeting (i.e., providing organizational memory).
For GSS success stories, look for sample cases at vendors’ Web sites. As you will see in many of these cases, collaborative
computing led to dramatic process improvements and cost savings.
Note that only some of these capabilities are provided in a single package from one vendor.
u SECTION 11.5 REVIEW QUESTIONS
1. Define GDSS and list the limitations of the initial GSS software.
2. List the benefits of GDSS.
3. List process gains made by GDSS.
4. Define decision room.
5. Describe Web-based GSS.
6. Describe how GDSS supports brainstorming and idea generation.
11.6 COLLECTIVE INTELLIGENCE AND COLLABORATIVE
INTELLIGENCE
Groups or teams are created for several purposes. Our book concentrates on support for decision making. This section deals
with the collective intelligence and collaborative intelligence of groups.
Definitions and Benefits
Collective intelligence (CI) refers to the total intelligence of a group. It is also refers to as the wisdom of the crowd. People in a
group are using their skills and knowledge for solving problems and providing new insights and ideas. The major benefits are
the ability to solve complex problems and/or design new products and services that result from innovations. A major research
center on collective intelligence (CI) is the MIT Center for Collective Intelligence (CCI) (cci.mit.edu). A major study aspect
of CCI is how people and computers can work together so that teams can be more innovative than any individual, group, or
computer can be alone. CI appears in several disciplines ranging from sociology to political science. Our interest here is in CI
as it relates to computerized decision making. We cover CI here and in Section 11.7 where we present the topic of
crowdsourcing. In Section 11.8, we present swarm intelligence, which is also an application of CI. For the benefits of CI, see
50Minutes.com (2017).
70 Part IV • Robotics, Social Networks, AI and IoT
TYPES OF COLLECTIVE INTELLIGENCE One way to categorize CI is to divide it into three major areas of applications: cognition,
cooperation, and coordination. Each of these can be further divided. For an overview, see collective intelligence on Wikipedia. Our
interest is in applications by which the group synergy helps in problem solving and decision making. People contribute their
experience and knowledge, and the group interactions and the computerized support help in making better decisions.
Thomas W. Malone, the founder and director of CCI at MIT, considers CI as a broad umbrella. He views collective
intelligence as “groups of individuals acting collectively in ways that seem intelligent.” The CCI work, known as the Edge, is
available at the Edge video (31:45 minutes) available at edge.org/conversation/ thomas_w__malone-collective-
intelligence.
Computerized Support to Collective Intelligence
Collective intelligence can be supported by many of the tools and platforms described in Sections 11.4 and 11.5. In addition,
the Internet, intranet, and the IoT (Chapter 13) play a major role in facilitating CI by enabling people to share knowledge and
ideas.
Example 1: The Carnegie University Foundation Supports Network Collaboration
The Carnegie Foundation was looking for ways to have people work together collaboratively in order
to accelerate improvements and to share data and learning across its networks of people. The solution
is an online workspace called the Carnegie Hub, which serves as an access point to resources and
enables engagement in group work and collaboration.
The Hub uses several software products, some of which were described in Section 11.4, such as
Google Drive, creating a collaborative workspace. The major aspects of the Carnegie Collection
Intelligence project follow:
1. Content is shared in one place (the “cloud”) for everyone to view, edit, or contribute even at the
same time.
2. All data and knowledge are stored in one location on the Web. Discovery is easy.
3. Asynchronous conversations using discussion boards are easy; all notes are publicly displayed,
documented, and stored.
4. These aspects facilitate social collaboration, commitment to problem solving, and peer support.
The Carnegie University faculty is now a community of practice, using collective intelligence to
plan, create, and solve problems together. For details, see Thorn and Huang (2014).
Example 2: How Governments Tap IoT for Collective Intelligence
According to Bridgwater (2018), governments are using IoT to support decision making and policy
creation. Governments are trying to collect information and knowledge from people and increasingly do so via IoT. Bridgwater
cites the government of the United Arab Emirates that uses IoT to enhance public decision making. The IoT systems collect
ideas and aspirations of the citizens. The collective intelligence platform allows the targeting of narrowly defined groups. Real
estate plans are subjected to the opinion of residents in the vicinity of proposed developments. The country’s project of smart
cities is combined with CI (Chapter 13). In addition to IoT, there are activities in CI and networks as shown in Application
Case 11.1. Application Case 11.1 Collaborative Modeling for Optimal Water Management: The Oregon State University Project
Introduction
Water management is one of the most important
challenges for many communities. In general, the demand
for water is growing while the supply could shrink (e.g.,
due to pollution). Managing water requires the
involvement of numerous stakeholders ranging from
consumers and suppliers to local governments and
sanitation experts. The stakeholders must work together.
The objective is to have responsible water use and water
preservation. The accounting office of PwC published
report 150CO47, “Collaboration: Preserving Water
Through Partnership That Works” available at
pwc.com/hu/hu/kiadvanyok/assets/pdf/pwc_
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 71
water_collaboration.pdf. It describes the problem and
its benefits and risks. The report shares the different
stakeholders’ perspectives, identifies the success factors of
collaboration, and weighs the trade-offs for evaluating
alternative solutions for the water management issue. An
interesting framework for a solution is the collaborative
modeling developed at Oregon State University in
collaboration with Indiana UniversityPurdue University.
The Challenge
Planning and managing water conservation activities are
not simple tasks. The idea is to develop a userfriendly tool
that will enable all stakeholders to participate in these
activities. It is necessary to involve the stakeholder
communities in using scientifically developed guidelines
for designing water conservation practices. Here are some
of the requirements of the desired tool:
• The tool needs to be interactive and human guided and operated.
• It needs to be Web-based and user friendly.
• Both individuals and groups should be able to use it. • It should enable users to view and evaluate solution
designs based on both quantitative and qualitative
criteria.
The Solution: WRESTORE
Watershed Restoration Using Spatio-Temporal
Optimization (WRESTORE) is a Web-based tool that
meets the preceding requirements. It is based on AI and
analytical optimization algorithms. The algorithms process
dynamic simulation models and allow users to spatially
optimize the location of new water conservations. In
addition to using the dynamic simulation models, users are
able to include their own personal subjective views and
qualitative criteria. WRESTORE generates alternative
practices that users can discuss and evaluate.
Incorporation of human preferences to computer
solutions makes the solutions more acceptable. The AI
part of the project includes machine learning and
crowdsourcing (Section 11.7) to solicit
information from the crowd. The reason for the
participative collaboration is that water is an essential
resource and should not be only centrally controlled. The
AI technologies “democratize” water management while
harnessing the power of people and computers to solve
difficult water management problems.
The machine-learning algorithms learn from what
people are doing. Human feedback helps AI to identify
best solutions and strategies. Thus, humans and machines
are combined to solve problems together.
The Results
WRESTORE developers are experimenting with the
technology in several places and so far have achieved full
collaboration from participating stakeholders. Initial
results indicate the creation by WRESTORE of innovative
ideas for developing water resources and distribution
methods that save significant amounts of water.
Questions for Case 11.1
1. Crowdsourcing is used to find information from a
crowd. Why is it needed in this case? (see Section 11.7 if
you are not familiar with crowdsourcing).
2. How does WRESTORE act as a CI tool?
3. Debate centralized control versus participative
collaboration. Cite the pros and cons of each.
4. Why it is difficult to manage water resources?
5. How can an optimization/simulation/AI model support
group work in this case?
Sources: Compiled from Basco-Carrera et al. (2017), KTVZ.com (Channel
21, Oregon, March 21, 2018), and Babbar-Sebens et al. (2015).
How Collective Intelligence May Change Work and Life
For several decades, researchers studied the relationship of CI and work. For example, Doug Engebert, a pioneer in CI,
describes how people work together in response to a shared challenge and how they can leverage their collective memory,
perception, planning, reasoning, and so on into powerful knowledge. Since Engebert’s pioneering work, the impact of
technology is increasing organizations’ CI and building collaborative communities of knowledge. In summary, CI attempts to
augment human intelligence to solve business and social problems. This basically means that CI allows more people to
have more engagement and involvement in organizational decision making. At MIT’s CCI, research is done on how people
72 Part IV • Robotics, Social Networks, AI and IoT
and computers can work together to improve work (see also Section 11.9). MIT’s CCI focuses on the role of networks,
including the Internet, intranets, and IoT. Researchers there found that organizations’ structures tend to be flatter, and
more decisions are delegated to teams. All this results in decentralized workplaces. For further discussion on MIT’s CCI,
see MIT’s blog of April 3, 2016, at executive.mit.edu/blog/willcollective-intelligence-change-the-way-we-work/.
For a comprehensive view on how CI can change the entire world, see Mulgan (2017).
A major thrust in CI is the collaboration efforts within a group, as described next.
Collaborative Intelligence
Placing people in groups and expecting them to collaborate with the help of technology may be wishful thinking.
Management and behavioral researchers study the issue of how to make people collaborate in groups.
Called by some collaborative intelligence, Coleman (2011) stipulates that group collaboration has the following 10
components: (1) willingness to share, (2) knowing how to share, (3) being willing to collaborate, (4) knowing what to share,
(5) knowing how to build trust, (6) understanding team dynamics, (7) using correct hubs for networking, (8) mentoring and
coaching properly, (9) being open to new ideas, and (10) using computerized tools and technology. A similar list is provided
at thebalancecareers.com/ collaboration-skills-with-examples-2059686.
Computerized tools and technologies are critical enablers of communication, collaboration, and people’s
understanding of each other.
How to Create Business Value from Collaboration: The IBM Study
Groups and team members provide ideas and insights. To excel, organizations must utilize people’s knowledge, some of
which is created by collective intelligence. One way to do this is provided by a study of collective intelligence conducted by
the IBM Institute for Business Value. The study is available (free) at www-935.ibm.com/services/us/gbs/
thoughtleadership/ibv-collective-intelligence.html. There is also a free executive summary. The study presents three
major points:
1. CI can enhance organizational outcomes by correctly tapping the knowledge and experience of working groups
(including customers, partners, and employees).
2. It is crucial to target and motivate the appropriate participants.
3. CI needs to address the issue of participants’ resistance to change. All in all, IBM concludes that “Collective
intelligence is a powerful resource for creating value using the experience and insights of vast numbers of people
around the world.”
Access the untapped knowledge of your networks, IBM. (www-935.ibm.com/
services/us/gbs/thoughtleadership/ibv-collective-intelligence.html) An offshoot of CI is crowdsourcing, the
topic of the next section (11.7).
u SECTION 11.6 REVIEW QUESTIONS
1. What is collective intelligence (CI)?
2. List the major benefits of CI.
3. How is CI supported by computers?
4. How can CI change work and life?
5. How can CI impact organization structure and decision making?
6. The Carnegie case described how standard collaboration tools create a collective intelligence infrastructure. The
WRESTORE case described a modeling analytical framework that enables stakeholders to collaborate. What are the
similarities and differences between the two cases?
7. Describe collaborative intelligence.
8. How do you create business value from collective intelligence?
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 73
11.7 CROWDSOURCING AS A METHOD FOR DECISION SUPPORT
Crowdsourcing refers to outsourcing tasks to a large group of people (crowd). One of the major reasons for doing so is
the potential for the wisdom of a crowd to improve decision making and assist in solving difficult problems; see Power
(2014). Therefore, crowdsourcing can be viewed as a method of collective intelligence. This section is divided into three parts:
The essentials of crowdsourcing, crowdsourcing as a decision support mechanism, and implementing crowdsourcing for
problem solving.
The Essentials of Crowdsourcing
Crowdsourcing has several definitions because it is used for several purposes in a number of fields. For a tutorial on
crowdsourcing and examples, view the video (14:51 min.) at youtube.com/watch?v=lXhydxSSNOY. Crowdsourcing
means that an organization is outsourcing or farming out work for several reasons: Necessary skills may not be available
internally, speed of execution is needed, problems are too complex to solve, or special innovation is needed.
SOME EXAMPLES
• Since 2005, Doritos Inc. has run a “Crash the Super Bowl” contest for creating a 30-second video for the Super
Bowl. The company has given $7 million in prizes in the last 10 years for commercials composed by the public.
• Airbnb is using user-submitted videos (15 seconds each) that describe travel sites.
• Dell’s Idea Storm (ideastorm.com) enables customers to vote on features of Idea Storm the customers prefer,
including new ones. Dell is using a technically oriented crowd, such as the Linux (linux.org) community. The crowd
submits ideas and sometimes members of the community vote on them.
• Procter & Gamble’s researchers post their problems at innocentive.com and ninesigma.com, offering cash
rewards to problem solvers. It uses other crowdsourcing service providers such as yourencore.com.
• The LEGO company has a platform called LEGO Ideas through which users can submit ideas for new LEGO sets
and vote on submitted ideas by the crowd. Accepted ideas generate royalties to those who proposed them if the ideas
are commercialized.
• PepsiCo solicits ideas regarding new potato chip flavors for the company’s Lay’s brand. Over the years, the company
has received over 14 million suggestions. The estimated contribution to sales increase is 8 percent.
• Cities in Canada are creating real-time electronic city maps to inform cyclists about high-risk areas to make the streets
safer. Users can mark the maps when they experience a collision, bike theft, road hazard, and so on. For details, see
Keith (2018).
• U.S. intelligence agencies have been using ordinary people (crowds) to predict world events ranging from the results
of elections to the direction of prices.
• Hershey crowdsourced potential solutions of how to ship chocolate in warm climates. For how this was done, see
Dignan (2016). The winning prize was $25,000.
These examples illustrate some of the benefits of crowdsourcing, such as wide exposure to expertise, increased performance
and speed, and improved problem-solving and innovation capabilities. These examples also illustrate the variety of
applications.
MAJOR TYPES OF CROWDSOURCING Howe (2008), a crowdsourcing pioneer, divided the crowdsourcing applications into the
following types (or models):
1. Collective intelligence (or wisdom). People in crowds are solving problems and providing new insights and ideas
leading to product, process, or service innovations.
2. Crowd creation. People are creating various types of content and sharing it with others (for pay or free). The
created content may be used for problem
74 Part IV • Robotics, Social Networks, AI and IoT
solving, advertising, or knowledge accumulation. Content creation can also be done by
splitting large tasks into small segments (e.g., contributing content to create Wikipedia).
3. Crowd voting. People are giving their opinions and ratings on ideas, products, or services, as well as evaluating and filtering information presented to them. An example is voting in
American Idol competitions.
4. Crowd support and funding. People are contributing and supporting endeavors for social
or business causes, such as offering donations, and micro-financing new ventures.
Another way to classify crowdsourcing is by the type of work it does. Some examples with a crowdsourcing vendor for
each follow:
• Logo design—Design Bill
• Problem solving—InnoCentive, NineSigma, IdeaConnection
• Business innovation—Chardix
• Brand names—Name This
• Product and manufacturing design—Pronto ERP
• Data cleansing—Amazon Mechanical Turk
• Software testing—uTest
• Trend watching—TrendWatching
• Images—Flickr Creative Commons
For a compressive list of crowdsourcing, collective intelligence, and related companies, see boardofinnovation.com.
THE PROCESS OF CROWDSOURCING The process of crowdsourcing differs from application to application, depending on the
nature of the specific problem to be solved and the method used. However, the following steps exist in most enterprise
crowdsourcing applications, even though the details of the execution may differ. The process is illustrated in Figure 11.3.
1. Identify the problem and the task(s) to be outsourced.
2. Select the target crowd (if not an open call).
3. Broadcast the task to the crowd (or to an unidentified crowd in an open call).
4. Engage the crowd in accomplishing the task (e.g., idea generation, problem solving).
5. Collect user-generated content.
6. Have the quality of submitted material evaluated by the management that initiated the request, by experts, or by a
crowd.
7. Select the best solution (or a short list).
8. Compensate the crowd (e.g., the winning proposal).
9. Implement the solution.
Note that we show the process as sequential, but there could be loops returning to previous steps.
Crowdsourcing for Problem-Solving and Decision Support
Although there are many potential activities in crowdsourcing, major ones are supporting the managerial decision-making
process and/or providing a solution to a problem. A complicated problem that is difficult for one decision maker or a small
group to solve may be solved by a crowd, which can generate a large number of ideas for solving a
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 75
FIGURE 11.3 The Crowdsourcing Process.
problem. However, inappropriate use of crowdsourcing could generate negative results (e.g., see Grant, 2015). On how to
avoid the potential pitfalls of crowdsourcing, see Bhandari et al., 2018.
THE ROLE OF CROWDSOURCING IN DECISION MAKING Crowds can provide ideas in a collaborative or a competitive mode.
However, the crowd’s role may differ at different stages of the decision-making process. We may use a crowd to decide
how to respond to a competitor’s act or to help us decide whether a proposed design is useful. Chiu et al. (2014) adopted
Herbert Simon’s decision-making process model to outline the potential roles of a crowd. Simon’s model includes three
major phases before implementation: intelligence (information gathering and sharing for the purpose of problem solving or
opportunity exploitation, problem identification, and determination of the problem’s importance), design (generating ideas
and alternative solutions), and choice (evaluating the generated alternatives and then recommending or selecting the best
course of action). Crowdsourcing can provide different types of support to this managerial decision-making process. Most
of the applications are in the design phase (e.g., idea generation and co-creation) and in the choice phase (voting). In some
cases, support can be provided in all phases of the process.
Implementing Crowdsourcing for Problem Solving
While using an open call to the public can be done fairly easily by the problem owner, people who need to solve difficult
problems usually like to reach experts for solving problems (solvers). For a company to obtain assistance in finding such
experts, especially externally, it can use a third-party vendor. Such vendors have hundreds of thousands or even millions of
preregistered solvers. Then, the vendor can do the job as illustrated in Application Case 11.2.
Application Case 11.2 How InnoCentive Helped GSK Solve a Difficult Problem GlaxoSmithKline (GSK) is a UK-based global
pharmaceutical/healthcare company, with over 100,000
employees. The company strives on innovations.
However, despite its mega size and global presence, it has
problems that it needs outside expertise to solve.
The Problem
The company researched a potentially disruptive
technology that promised cure to difficult diseases. The
company wanted to discover which disease to use as a test
bed for the potential innovative treatments. It was
76 Part IV • Robotics, Social Networks, AI and IoT
necessary to make sure that the selection will cover a
disease where every aspect of the new treatment is
checked. Despite its large size, GSK wanted some outside
expertise to support and check the in-house research
efforts.
The Solution
GSK decided to crowdsource the problem solution to
experts, using InnoCentive Corp. (Innocentive. com).
InnoCentive is a US-based global crowdsourcing
company. The company receives challenges from client
companies like GSK. These challenges are posted for
solvers to see with the potential rewards, in InnoCentive’s
Challenge Center. Solvers that think they want to
participate follow instructions and may sign an agreement.
The solutions submitted are evaluated, and awards are
provided to the winners.
The GSK Situation
In total, 397 solvers engaged in this challenge, even the
reward was minimal ($5000). The solvers resided in several
countries. The solvers submitted 66 proposed solutions.
The entire process lasted 75 days.
The Results
The winning solution proposed a new area that was not
considered by GSK teams. The proposer was a Bulgarian
who based his idea on a Mexican publication. Several other
winning proposals contributed useful ideas. Also, the
process enabled collaboration between the GSK team and
the winning researchers. Questions for Case 11.2
1. Why did GSK decide to crowdsource?
2. Why did the company use InnoCentive?
3. Comment on the global nature of the case.
4. What lessons did you learn from this case?
5. Why do you think a small $5000 reward is sufficient?
Sources: Compiled from InnoCentive Inc. Case Study GlaxoSmithKline.
Waltham, MA., GSK Corporate Information (gsk. com) and
InnoCentive.com/our-solvers/.
CROWDSOURCING FOR MARKETING More than 1 million customers are registered at Crowd Tap, the company that
provides a platform named Suzy that enables marketers to conduct crowdsourcing studies.
u SECTION 11.7 REVIEW QUESTIONS
1. Define crowdsourcing.
2. Describe the crowdsourcing process.
3. List the major benefits of the technology.
4. List some areas for which crowdsourcing is suitable.
5. Why may you need a vendor to crowdsource the problem-solving process?
11.8 ARTIFICIAL INTELLIGENCE AND SWARM AI SUPPORT OF TEAM
COLLABORATION AND GROUP DECISION MAKING
AI, as seen in Chapter 2, is a diversified field. Its technologies can be used to support group decision making and
team collaboration.
AI Support of Group Decision Making
A major objective of AI is to automate decision making and/or to support its process. This objective holds also for
decisions made by groups. However, we cannot automate a decision made by a group. All we can do is to support
some of the steps in a group’s decision-making process.
A logical place to start is Figure 11.1. We can examine the different steps of the process and see where AI can
be used.
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 77
1. Meeting preparation. AI is used to find a convenient time for meetings to take place. AI can assist in scheduling
meetings so that all can participate.
2. Problem identification. AI technologies are used for pattern recognition that can identify areas that need attention.
AI can be used in other types of analysis to identify potential or difficult to pinpoint problems.
3. Idea generation. AI is known for its quest for creativity. Team members can increase their creativity when they
use AI for support.
4. Idea organization. Natural language processing (NLP) can be used to sort ideas and organize them for improved
evaluation.
5. Group interaction and collaboration. AI can facilitate communication and collaboration among group members.
This activity is critical in the process of arriving at a consensus. Also, Swarm AI (see the end of this section) is
designed to increase interactions among group members so their combined wisdom is elevated.
6. Predictions. AI supports predictions that are required to assess the impact of the ideas generated regarding
performance and/or impacts in the future. Machine learning, deep learning, and Swarm AI are useful tools in
this area.
7. Multinational groups. Collaboration among people located in different countries is on the rise. AI enables group
interaction of people who speak different languages, in real time.
8. Bots are useful in supporting meetings. Group members may consult Alexa and other bots. Chatbots can provide
answers to queries in real time.
9. Other advisors. IBM Watson can provide useful advice during meetings, supplementing knowledge provided by
participants and by Alexa.
Example
In 2018, Amazon.com was looking for a site for its second headquarters. A robot named Aiera from Wells Fargo
Securities used deep learning to predict that the winning site would be Boston (Yurieff, 2018a). (When this chapter
was written, the decision had not been made.)
For an academic approach on how to improve group decision making by AI, see Xia (2017).
AI Support of Team Collaboration
Organizations today are looking for ways to increase and improve collaboration with employees, business partners,
and customers. To gain insight into how AI may impact collaboration, Cisco Systems sponsored a global survey, AI
Meets Collaboration (Morar HPI, 2017), regarding the impact of AI, including the use of virtual assistants in the
work space. The major findings of this survey are:
1. Virtual assistants increase employees’ productivity, creativity, and job satisfaction.
Bots also enable employees to focus on high-value tasks.
2. Bots are accepted as part of workers’ teams.
3. Bots improve conference calls. They also can take meetings notes and schedule meetings.
4. AI can use facial recognition to sign in eligible people to meetings.
5. Personal characteristics are likely to influence how people feel about AI in the workplace.
6. Employees in general like to have AI in their teams.
7. Security is a major concern when AI, such as virtual assistants, is used in teams.
8. The major AI tools that are most useful are NLP and voice response; AI can also summarize the key topics of
meetings and understand participants’ needs. AI can be aware of organizational goals and workers’ skills and
can make suggestions accordingly.
For how virtual meetings are supported with AI by Cisco Systems in their leading products, see Technology Insight
11.2.
78 Part IV • Robotics, Social Networks, AI and IoT
TECHNOLOGY INSIGHT 11.2 How Cisco Improves Collaboration with AI
Cisco Systems is well known for its collaboration products such as Spark and Webex. The first step in introducing AI was to
acquire MindMeld’s AI platform for use in Cisco’s collaboration products. The project’s objective was to improve the
conversational interferences for any application or device so users could better understand the context of conversations.
MindMeld uses machine learning to improve the accuracy of voice and text communication. To do so, it uses NLP and five
varieties of machine learning. Cisco is also integrating IBM Watson into its enterprise collaboration solutions. As you may
recall from Chapter 6, Watson is a powerful advisor. AI collaboration tools can increase efficiency, speed idea generation, and
improve the quality of decisions made by groups. The improved Cisco’s technology will be used in conference rooms and
everywhere else. One of the major AI projects is the assistant to Spark.
Monica, a Digital Assistant to the Spark Collaboration Platform
Monica is trained to answer users’ queries by employing machine learning. Furthermore, users can use Monicait to interact
with the Spark collaboration platform using natural language commands. It is an enterprise assistant similar to Alexa and
Google Assistant (Chapter 12). Cisco’s Monica is the world’s first enterprise-ready voice assistant specifically designed to
support meetings. The bot has deep-domain conversational AI that adds cognitive capabilities to the Spark platform.
Monica can assist users in several of the steps of Figure 11.1, such as:
• Organize meetings. • Provide information to participants before and during meetings. • Navigate and control Spark’s devices. • Help organizers find a meeting room and reserve it.
• Help share screens and bring up a whiteboard.
• Take meeting notes and organize them.
In the near future, Monica will know about participants’ internal and external activities and will schedule meetings using this
information. Additional functions to support more steps of the process in Figure 11.1 will be added in the future.
For more about the assistant, see youtube.com/watch?v=8OcFSEbR_6k (5:10 minutes).
Note: Cisco Spark will become Webex Teams with more AI functionalities. In addition, Webex meetings will include v ideoconferencing for
collaboration and other supports to meetings.
Sources: Compiled from Goecke (2017), Finnegan (2018), and Goldstein (2017).
Swarm Intelligence and Swarm AI
The term swarm intelligence refers to the collective behavior of decentralized, selforganized systems, natural or
artificial (per Wikipedia). Such systems consist of things (e.g., ants, people) interacting with each other and their
environment. A swarm’s actions are not centrally controlled, but they lead to intelligent behavior. In nature, there
are many examples (e.g., ant colonies, fish schools) of such behaviors.
Natural groups were observed to amplify their group intelligence by forming swarms. Social creatures,
including people, can improve the performance of their individual members when working together as a unified
system. In contrast with animals and other species whose interactions among group members are natural, people
need technology to exhibit swarm intelligence. This concept is used in studies and implementation of AI and robotics.
The major applications are in the area of predictions.
Example
A study at Oxford University (United Kingdom) involved predicting the results of all 50 English Premier League
soccer games over five weeks. A group of independent judges scored 55 percent accuracy when working alone.
However, when predicting using an AI swarm, their prediction success increased to 72 percent (an improvement of
31 percent). Similar improvement was recorded in several other studies.
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 79
In addition to improved prediction accuracy, studies show that using swarm AI results in more ethical decisions than
that of individuals (Reese, 2016).
SWARM AI TECHNOLOGY Swarm AI (or AI swarm) provides the algorithms for the interconnections among people
creating the human swarm. These connections enable the knowledge, intuition, experience, and wisdom of
individuals to merge into single improved swarm intelligence. Results of swarm intelligence can be seen in the TED
presentation (15:58 min.) at youtube.com/watch?v=Eu-RyZt_Uas. Swarm AI is used by several thirdparty
companies (e.g., Unanimous.aI, as illustrated in Application Case 11.3.
Application Case 11.3 XPRIZE Optimizes Visioneering XPRIZE is a nonprofit organization that allocates prizes
via competitions to promote innovations that have the
potential to change the world for the better. The main
channel for designing prizes that solve humanity’s
grandest challenges is called Visioneering. It attempts to
harness the power of the global crowd to develop
solutions to important challenges. The organization’s
major event is an annual summit meeting where prizes are
designed and proposals are evaluated. The experts at
XPRIZE develop concepts and turn them into
incentivized competitions. Prizes are donated by leading
corporations.
For example, in 2018, IBM Watson donated a $5
million prize called “AI approaches and collaboration.”
The competition had 142 registered teams, and 62 were
left in round 2 in June 2018. The teams are invited to create
their own goals and solutions to a grand challenge.
The Problem
Every year, there is a meeting of 250 members of
“Visioneers Summit Ideation” where top experts
(entrepreneurs, politicians, scientists, etc.), participate to
discover and prioritize topics for the XPRIZE agenda.
Finding the top global problems can be a very
complex challenge due to a large number of variables. In
just a few days, top experts need to use their collective
wisdom to agree on the next year’s XPRIZE top
challenges. The method used to support the group’s
decision is a critical success factor.
(Continued)
80 Part IV • Robotics, Social Networks, AI and IoT
Application Case 11.3 (Continued)
The Solution
In the 2017 annual meeting for determining what challenge
to use for 2018, the organization used the swarm AI
platform (from Unanimous AI). Several small groups
(swarms) moderated by AI algorithms were created to
discover challenging topics. The mission was to explore
ideas and agree on preferred solutions. The objective was
to use the talents and brainpower of the participants.
In other words, the objective was to use the thinking
together feature of swarm AI to generate each group’s
synergy with the AI algorithms acting as moderators. This
way, smarter decisions were generated by the groups than
its individual participants. The different groups examined
six preselected topics: energy and infrastructure, learning
human potential, space and new frontiers, plant and
environment, civil society, and health and well-being. The
groups brainstormed the issues. Then, each participant
created a customized evaluation table. The tables were
combined and analyzed by algorithms.
The Swarm AI replaced traditional voting methods
by optimizing the detailed contribution of each participant.
The Results
Use of swarm AI did the following:
• Supported the generation of optimized answers and enabled fast buy-in from the participants.
• Enabled all participants to contribute.
• Provided a better voting system than in previ-ous years.
Questions for Case 11.3
1. Why is the group discussion in this case complex?
2. Why is getting a consensus when top experts are involved more difficult than when non-experts are
involved?
3. What was the contribution of swarm AI?
4. Compare simple voting to swarm AI voting.
Sources: Compiled from Unanimous AI (2018), xprize.org, and
xprize.org/about.
SWARM AI FOR PREDICTIONS Swarm AI was used by Unanimous AI for making predictions in difficult-to-assess
situations. Examples are:
• Predicting Super Bowl #52 number of points scored (used for spread waging).
• Predicting winners in the regular NFL season.
• Predicting the top four finishers of the 2017 Kentucky Derby.
• Predicting the top recipients of the Oscars in 2018.
u SECTION 11.8 REVIEW QUESTIONS
1. Relate the use of AI to the activities in Figure 11.1.
2. Discuss the different ways that AI can facilitate group collaboration.
3. How can AI support group evaluation of ideas?
4. How can AI facilitate idea generation?
5. What is the analogy of swarm AI to swarms of living species?
6. How is swarm AI used to improve group work and to initiate group predictions?
11.9 HUMAN–MACHINE COLLABORATION AND TEAMS OF ROBOTS
Since the beginning of the Industrial Revolution, people and machines have worked together. Until the late 1900s,
the collaboration was in manufacturing. But since then, due to advanced technology and changes in the nature of
work, human–machine collaboration has spread to many other areas, including performing mental and cognitive
work and collaborating on managerial and executive work. According to Nizri (2017), human and AI collaboration
will shape the future of work (see also Chapter 14).
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 81
Humans and machines can collaborate in many ways, depending on the tasks they perform. The collaboration
with robots in the manufacturing scenario is an extension of the older model in which humans and robots
collaborated with humans controlling and monitoring production and robots doing physical work that requires speed,
power, accuracy, or nonstop attention. Robots are also doing work in hazardous environments. In general, robots
complement human capabilities. An example is Amazon’s distribution centers where over 50,000 mobile robots do
a variety of tasks, mostly in hauling materials and helping to fulfill customer orders. The robotic technology enables
fully collaborative solutions. For details, watch the video at Kuka kuka.com/en-us/technologies/humanrobot-
collaboration. Kuka’s system allows the execution of complex jobs that can be done cost effectively.
Another collaborative human-robotic system is called YuMi. To see this system (from ABB Robotics) at work,
watch the 4:38 min. video at youtube.com/ watch?v=2KfXY2SvlmQ. Notice that the robot has two arms.
Human–Machine Collaboration in Cognitive Jobs
Advancement in AI enables the automation of nonmanual activities. While some intelligent systems are fully
automated (see automated decision making in Chapter 2 and chatbots in Chapter 12), there are many more examples
of human–machine collaboration in cognitive jobs (e.g., in marketing and finance). An example is in investment
decisions. A human asks the computer for advice concerning investments, and after receiving the advice, can ask
more questions, changing some of the input. The difference from the past is that today the computers (machines)
can provide much more accurate suggestions, by using machine learning and deep learning. Another collaboration
example involves medical diagnoses of complex situations. For example, IBM Watson provides medical advice, which
permits doctors and nurses to significantly improve their jobs. Actually, the entire field of machines advising humans
is reaching new heights. For more on the increasing collaborative power of AI, see Carter (2017).
TOP MANAGEMENT JOBS A major task of managers is decision making, which has become one area of human–machine
collaboration. Use of AI and analytics has improved decision making considerably, as illustrated throughout this
book. For an overview, see Wladawsky-Berger (2017).
McKinsey & Company and MIT are two major players in researching the topic of collaboration between
managers and machines. For example, Dewhurst and Wilmott (2014) report on its increased use of man-machine
collaboration, using deep learning. A Hong Kong company even appointed a decision-making algorithm to its board
of directors. Companies are using crowdsourcing advice to support complex problem solving, as illustrated in S
ection 11.7.
Robots as Coworkers: Opportunities and Challenges
Sometime in the future, walking and talking humanoid robots will socialize with humans during breaks from work.
Someday, robots will become cognitive coworkers and help people be more productive (as long as people do not
talk too much with the robots).
According to Tobe (2015), a study at a BMW factory found that human–robot collaboration could be more
productive than either humans or robots working by themselves. Also, the study found that collaboration reduced
idle time by 85 percent. This is because people and machines capitalize on the strengths of each (Marr, 2017).
The following challenges must be considered:
• Designing a human–machine team that capitalizes on the strength of each partner.
• Exchanging information between humans and robots.
• Preparing company employees in all departments for the collaboration (Marr, 2017).
• Changing business processes to accommodate human–robot collaboration (Moran, 2018).
• Ensuring the safety of robots and employees that work together.
TECHNOLOGIES THAT SUPPORT ROBOTS AS COWORKERS Yurieff (2018b) lists the following examples of facilitating or
considering robots as coworkers.
1. Virtual reality can be used as a powerful training tool (e.g., for safety).
82 Part IV • Robotics, Social Networks, AI and IoT
2. A robot is working with an ad agency in Japan to generate ideas.
3. A robot can be your boss.
4. Robots are coworkers in providing parts out of bins in assembly lines and can check quality together with
humans.
5. AI tools measure blood flow and volume of the cardiac muscles in seconds (instead of minutes when done
completely by a radiologist). This information facilitates the decisions made by radiologists.
BLENDING HUMANS AND AI TO BEST SERVE CUSTOMERS Genesys Corp. commissioned Forrester Research Company to
conduct a global study in 2017 to find how companies are using AI to improve customer service. The study, titled
“Artificial Intelligence with the Human Touch,” is available at no charge from
genesys.com/resources/artificialintelligence-with-the-human-touch. A related video is available at
youtube.com/ watch?v=NP2qqwGTNPk.
The study revealed the following:
1. “AI is already transforming enterprises by increasing worker efficiency and productivity, delivering better
customer experiences and uncovering new revenue streams” (from the Executive Summary).
2. A major objective of man–machine collaboration is to improve the satisfaction of both customers and
companies’ agents rather than reduce cost.
3. Human agents’ ability to connect emotionally with customers for the increased satisfaction of themselves and
customers is superior to that of service provided by AI.
4. By blending the strengths of humans and AI, companies achieve better customer service satisfaction of
customers (71 percent) and agents (69 percent).
Note that AI excels in the support of marketing and advertising as illustrated in Chapter 2. See also Loten (2018) for
the use of AI to support customer relationship management (CRM) and of crowdsourcing and collective intelligence
to support marketing.
COLLABORATIVE ROBOTS (CO-BOTS) Collaborative robots (co-bots) are designed to work with people, assisting in
executing various tasks. These robots are not very smart, but their low cost and high usability make them popular.
For details, see Tobe (2015).
Teams of collaborating Robots
One of the future directions in robotics is creating teams of robots that are designed to do complex work. Robot
teams are common in manufacturing where they serve each other or join a robot group in simple assembly jobs. An
interesting example is the use of a team of robots in preparation to land on Mars.
FIGURE 11.4 Team of Robots Prepares to Go to Mars. Source: C. Kang.
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 83
Example: Teams of Robots to Explore Mars
Before people land on Mars, scientists need to know more about the “Red Planet.” The idea was to use teams of
robots. The German Research Centers for Artificial Intelligence (DFKI) conducted simulation experiments in the
desert of Utah. The details of this simulation are described by Staff Writers (2016). The process is illustrated in a 4:54
min. video at youtube. com/watch?v=pvKIzldni68/ showing robots’ collaboration. For more information, see
robotik.dfki-bremen.de/en/research/projects/ft-utah.html.
DFKI is not the only entity that plans to explore the surface of Mars. NASA plans to send swarms of robot
bees with flapping wings called Marsbees that will operate in a group to explore the land and air of the Red Planet.
The reason for the flapping wings structure is to enable low-energy flights (like bumblebees). Each robot is the size
of a bee. Part of a wireless communication network, Marsbees will together create networks of sensors. Information
will be delivered to a mobile base (see Figure 11.4, showing one robot) that will be the main communication center
and a recharging station for the Marsbees. For more information, see Kang (2018).
Getting robots to work together is being researched at MIT. They use their perception system to sense the
environment, and then they communicate their findings to each other and coordinate their work. For example, a
robot can open a door for another robot. Read about how this is done and watch a video at ft.com/video/
ea2d4877-f3fb-403d-84a8-a4d2d4018c5e.
Example
Alibaba.com is using teams of robots in its smart warehouses where robots do 70 percent of the work. This is
shown in a video at youtube.com/watch?v=FBl4Y55V2Z4.
Social collaboration of robots is being investigated by watching the behavior of swarms of ants and other
species to learn how to design robots to work in teams. Watch the TED presentation at
youtube.com/watch?v=ULKyXnQ9xWA on how to design a robot collaboration.
Having robots collaborate involves several issues such as making sure they do not collide with each other. This
is a part of the safety issue regarding robotics. Finally, you can build your own team of robots with LEGO’s
Mindstorms. For details, see Hughes and Hughes (2013). u SECTION 11.9 REVIEW QUESTIONS
1. Why is there an increase in human–machine collaboration?
2. List some benefits of such collaboration.
3. Describe how collaborating robotics can be used in manufacturing.
4. Discuss the use of teams of robots.
5. What will do robots on Mars?
Chapter Highlights
• Groupware refers to software products that provide collaborative
support to groups (including conducting meetings).
• Groupware can support decision-making and problem solving
directly or indirectly by improving communication between team
members.
• People collaborate in their work (called group work). Groupware
(i.e., collaborative computing software) supports group work.
• Group members may be in the same organiza-
tion or in different organizations in the same or in
different locations and may work at the same or
different times.
• The time/place framework is a convenient way to
describe the communication and collaboration patterns
and support of group work. Different technologies can
support different time/place settings.
• Working in groups can result in many benefits, including
improved decision making, increased productivity and
speed, and cost reductions.
• Communication can be synchronous (i.e., same time) or
asynchronous (i.e., sent and received at different times).
• The Internet, intranets, and IoT support virtual meetings
and decision making through collaborative tools and
access to data analysis, information, and knowledge.
84 Part IV • Robotics, Social Networks, AI and IoT
• Groupware for direct support typically contains capabilities for
brainstorming, conferencing, scheduling group meetings;
planning; resolving conflicts; videoconferencing; sharing
electronic documents; voting; formulating policy; and analyzing
enterprise data.
• A GDSS is any combination of hardware and software that
facilitates decision-making meetings. It provides direct support in
face-to-face settings and in virtual meetings, attempting to
increase process gains, and reducing process losses of group
works.
• Collective intelligence is based on the premise that the combined
wisdom of several collaborating people is greater than that of
individuals working separately.
• Each of the several configurations of collective intelligence can
be supported differently by technology.
• Several collaboration platforms, such as Micro-soft Teams and
Slack, can facilitate collective intelligence.
• Idea generation and brainstorming are key activ-ities in group
work for decision making. Several collaboration software and AI
programs are supporting these activities.
• Crowdsourcing is a process of outsourcing work to a crowd.
Doing so can improve problem solving, idea generation, and
other innovative activities.
• Crowdsourcing can be used to make predictions by groups of
people, including crowds. Results have shown better predictions,
especially when communication is used among the predictors
than when no communication was enabled.
• One method of communication in crowdsourc-ing is based on
swarm intelligence. A technology known as swarm AI has had
significant success.
• AI can support many activities in group deci-sion making.
• Human–machine collaboration can be a major method of work
in the future.
• Machines that once supported manufacturing work are used now
also in support of cognitive, including managerial, work.
• For people and machines to work in teams, it is necessary to make
special preparations.
• Robots may work in exclusive teams. They do so in
manufacturing and possibly in other activities (e.g., explore Mars)
as they become more intelligent.
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 85
Exercises
1. Go to realtimeboard.com. How can the site support idea creation
and brainstorming? 2. Investigate how researchers are trying to develop collaborative
computer systems that portray or display nonverbal communication
factors (e.g., images). 3. For each of the software packages Skype Business and WebEx, check
the trade literature and the Web for details and explain how each
includes computerized collaborative support system capabilities. 4. Compare Simon’s four-phase decision-making model to the steps in
using GDSS.
5. A major claim in favor of wikis is that they can replace e-mail,
eliminating its disadvantages (e.g., spam). Go to socialtext.com and
review such claims. Find other supporters of switching to wikis. Then
find counter arguments and conduct a debate on the topic.
6. Search the Internet to identify sites that describe methods for making
meetings more effective and efficient. 7. Enter MIT Center for CI and review some of its recent activities.
Write a report.
8. Debate the issue of the quality of crowdsourcing results.
Start by viewing youtube.com/
watch?v=JJHAHQmiI3c.
9. Find information about Yammer (a Microsoft company).
Why is it considered a social collaboration tool? Why is it
popular? Write a report.
10. Enter Dropbox.com and find its collaboration tools. Write
a summary. 11. Read Pena (2017). Examine the 12 benefits of collaboration.
Which are related to social collaboration? 12. Compare Microsoft’s Universal Translator to Google’s
Translator. Concentrate on face-to-face conversation in real
time.
13. Write a report on the issue of whether crowdsourcing
produces superior decisions. Use Quora for help. Find
other sources.
14. Investigate the status of IBM Connections Cloud. Examine
all the collaboration and communication features. How
does the product improve productivity? Write a report.
15. Compare Microsoft Teams to Spark Teams. Write a report.
Key Terms
asynchronous group decision making brainstorming
group decision support collective intelligence system
(GDSS)
collaborative workspace group support system
crowdsourcing (GSS) decision room groupthink
groupware group
work idea
generation online
workspace process
gain process loss
swarm intelligence
synchronous (real-time)
virtual meeting virtual
team
Questions for Discussion
1. Explain why it is useful to describe group work in terms of the
time/place framework. 2. Describe the kinds of support that groupware can provide to
decision makers. 3. Explain why most groupware is deployed today over the Web. 4. Explain in what ways physical meetings can be inefficient. Explain
how technology can make meetings more effective. 5. Explain how GDSS can increase some benefits of collaboration
and decision making in groups and eliminate or reduce some losses. 6. The initial term for group support system (GSS) was group
decision support system (GDSS). Why was the
word decision dropped? Does this make sense? Why, or why not? 7. Discuss why Microsoft SharePoint is considered a workspace.
What kind of collaboration does it support? 8. Reese (2017) claims that swarm AI can be used instead of polls
for market research. Discuss the advantages of swarm AI. In
what circumstances would you prefer each method? (Read
“Polls vs. Swarms” at Unanimous AI.) 9. What is a collaborative robot? What is an uncollaborative one? 10. Discuss the ways in which social collaboration can improve
work in a digital workplace. 11. Provide an example of using analytics to improve decision
making in sport.
86 Part IV • Robotics, Social Networks, AI and IoT
16. Enter crowdtap.com and read Kurzer (2018) paper. Explain how
the platforms work. Relate the material about crowdsourcing and
collective intelligence. Write a report.
17. Go to technologyreview.com and look at the May 8, 2017, video
(17:42 min.) “Next Generation Human-
Babbar-Sebens, M., et al. “A Web-Based Software Tool for Participatory
Optimization of Conservation Practices in Watersheds.”
Environmental Modelling & Software, 69, 111–127, July 2015. Basco-Carrera, L., et al. “Collaborative Modelling for Informed Decision
Making and Inclusive Water Development.” Water Resources
Management, 31:9, July 2017. Bhandari, R., et al. “How to Avoid the Pitfalls of IT Crowdsourcing to
Boost Speed, Find Talent, and Reduce Costs.” McKinsey & Company,
June 2018.
Bridgwater, A. “Governments to Tap IoT for ‘Collective Intelligence.’”
Internet of Business, January 2, 2018. Carter, R. “The Growing Power of Artificial Intelligence in Workplace
Collaboration.” UC Today, June 28, 2017. Chiu, C-M., T. P. Liang, and E. Turban. “What Can Crowdsourcing Do
for Decision Support?” Decision Support Systems, September 2014.
Coleman, D. “10 Components of Collaborative Intelligence.” CMS Wire,
November 21, 2011. de Lares Norris, M. A. “Collaboration Technology Is the Driving Force
for Productivity and Businesses Need to Embrace It . . . Now.” IT
ProPortal, January 4, 2018. DeSanctis, G., and R. B. Gallupe. “A Foundation for the Study of Group
Decision Support Systems.” Management Science, 33:5, 1987.
Dewhurst, M., and P. Willmott. “Manager and Machine: The New
Leadership Equation.” McKinsey & Company, September 2014. Dignan, L. “A Sweet Idea: Hershey Crowdsourcing for Summer
Chocolate Shipping Concepts.” ZDNet, January 14, 2016. Digneo, C. “49
Online Collaboration Tools to Help Your Team Be More Productive.”
Time Doctor, 2018. biz30.timedoctor.com/online-collaboration-tools/
(accessed July 2018).
50Minutes.com. The Benefits of Collective Intelligence: Make the Most of Your
Team’s Skills. Brussels, Belgium: 50Minutes.com (Lemaitre
Publishing), 2017. Finnegan, M. “Cisco Shakes Up Collaboration Efforts; Morphs Spark into
Webex.” Computer World, May 2, 2018. Goecke, J. “Meet Cisco Spark Assistant, Your Virtual Assistant for
Meetings.” Cisco Blogs, November 2, 2017. Goldstein, P. “How Can AI Improve Collaboration Technology?” Biztech
Magazine, June 5, 2017.
Grant, R. P. “Why Crowdsourcing Your Decision-Making Could Land
You in Trouble.” The Guardian, March 10, 2015.
Howe, J. Crowdsourcing: Why the Power of the Crowd is Driving the Future of
Business. New York: Crown Business, 2008.
Hughes,
C., et al.
Build
Your
Own
Teams of Robots with LEGO® Mindstorms® NXT and Bluetooth®. New
York, NY: McGraw-Hill/Tab Electronic, 2013. Kang, C-K. “Marsbee—Swarm of Flapping Wing Flyers for Enhanced
Mars Exploration.” NASA.gov, March 30, 2018. Keith, E. “Here’s How a
New Crowd-Sourced Map Is Making Canadian Streets Safer for Cyclists.”
Narcity.com, June 2018.
Kurzer, R. “Meet Suzy: The New Crowd Intelligence Platform with the
Cute Name.” MarTech Today, March 27, 2018. Loten, A. “The Morning Download: AI-Enabled Sales Tools Spotlight
Data Needs.” The Wall Street Journal, March 27, 2018. Marr, B. “Are You Ready to Meet Your Intelligent Robotic Co-Worker?”
Forbes.com, September 8, 2017.
McMahon, K., et al. “Beyond Idea Generation: The Power of Groups in
Developing Ideas.” Creativity Research Journal, 28, 2016. Microsoft. “Hendrick Motorsports Uses Microsoft Teams to Win
Productivity Race.” Customers.Microsoft.com, April 27, 2017.
Moran, C. “How Should Your Company Prepare for Robot Coworkers?”
Fast Company, February 13, 2018. Morar HPI. “A Global Survey Reveals Employee Perception of Advanced
Technologies and Virtual Assistants in the Workplace.” Cisco.com,
October 2017. Mulgan, G. Big Mind: How Collective Intelligence Can Change Our World.
Princeton, NJ: Princeton University Press, 2017. Nizri, G. “Shaping the Future of Work: A Collaboration of Humans and
AI.” Forbes.com, August 17, 2017. Pena, S. “12 Benefits of a Collaborative Workspace.” Creator, June 14,
2017. wework.com/creator/start-yourbusiness/12-benefits-of-
a-collaborative-workspace/ (accessed July 2018). Power, B. “Improve Decision-Making with Help from the Crowd.”
Harvard Business Review, April 8, 2014. Reese, H. “New Research Shows That Swarm AI Makes More Ethical
Decisions Than Individuals.” Tech Republic, June 8, 2016.
Ruiz-Hopper, M. “Hendrick Motorsports Gains Competitive Advantage
on the Race Track.” Microsoft.com, September 26, 2016. Staff Writers. “Scientists Simulate a Space Mission in MarsAnalogue Utah
Desert.” Mars Daily, October 19, 2016.
Stewart, C. “The 18 Best Tools for Online Collaboration.” Creative Blog,
March 7, 2017. Thorn, C., and J. Huang. “How Carnegie Is Using Technology to Enable
Collaboration in Networks.” Carnegie Foundation Blog, September 9,
2014. Tobe, F. “Why Co-Bots Will Be a Huge Innovation and Growth Driver
for Robotics Industry.” IEEE Spectrum, December 30, 2015.
Machine Collaboration.” Write a report.
References
Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 87
Unanimous AI. “XPRIZE Uses Swarm AI Technology to Optimize
Visioneers Summit Ideation.” Unanimous AI, 2018. UAI_
case_study_xprize_0601_0601.pdf (accessed July 2018). Wladawsky-Berger, I. “Building an Effective Human-AI Decision
System.” The Wall Street Journal, December 1, 2017. Xia, L. “Improving Group Decision-Making by Artificial Intelligence.” In
C. Sierra, Editor, Proceedings of the Twenty-Sixth International Joint
Conference on Artificial Intelligence, IJCAI, 2017. Yazdani, M., et al. “A Group Decision Making Support System in
Logistics and Supply Chain Management.” Expert Systems with
Applications, 88, December 1, 2017.
Yoon, Y., et al. “Preference Clustering-Based Mediating Group Decision-
Making (PCM-GDM) Method for Infrastructure Asset Management.”
Expert Systems with Applications, 83, October 15, 2017.
Yurieff, K. “Robot Predicts Boston Will Win Amazon HQ2.” CNN Tech,
March 13, 2018a. Yurieff, K. “Robot Co-Workers? 7 Cool Technologies Changing the Way
We Work.” CNN Tech, May 4, 2018b.
C H A P T E R
12
Knowledge Systems: Expert
Systems, Recommenders,
Chatbots, Virtual Personal Assistants,
and Robo Advisors
dvancement in artificial intelligence (AI) technologies and especially natural language processing
(NLP), machine and deep learning and knowledge systems, coupled with the increased quality and
functionalities of other intelligent systems, and mobile devices and their apps, have driven the
development of chatbots (bots) for inexpensive and fast execution of many tasks related to
communication, collaboration, and information retrieval. The use of chatbots in business is
increasing rapidly, partly because of their fit with mobile systems and devices. As a matter of fact,
sending messages is probably the major activity in the mobile world.
In the last two to three years, many thousands of bots have been placed into service worldwide
by both organizations (private and public) and individuals. Many people refer to these phenomena
as the chatbot revolution. Chatbots today are much more sophisticated than those of the past. They are
extensively used, for example, in marketing; customer, government, and financial services;
healthcare; and in manufacturing. Chatbots make communication more personal than faceless
computers and excel in data gathering. Chatbots can stand alone or be parts of other knowledge
systems.
648
We divide the applications in this chapter into four categories: expert systems, chatbots for communication and
collaboration, virtual personal assistants (native products, such as Alexa), and chatbots that are used as professional advisors.
Some implementation topics of intelligent systems are described last.
A LEARNING OBJECTIVES
■
■ Describe recommendation systems
■
■ Describe expert systems
■
■ Describe chatbots
■
■ Understand the drivers and capabilities of chatbots
and their use
■
■ Describe virtual personal assistants and their
benefits
■
■ Describe the use of chatbots as advisors
■
■ Discuss the major issues related to the
implementation of chatbots
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 89
This chapter has the following sections:
12.1 Opening Vignette: Sephora Excels with Chatbots 649
12.2 Expert Systems and Recommenders 650
12.3 Concepts, Drivers, and Benefits of Chatbots 660
12.4 Enterprise Chatbots 664
12.5 Virtual Personal Assistants 672
12.6 Chatbots as Professional Advisors (Robo Advisors) 676
12.7 Implementation Issues 680
12.1 OPENING VIGNETTE: Sephora Excels with Chatbots
THE PROBLEM
Sephora is a French-based cosmetics/beauty products company doing business globally. It has its own stores and sells its
goods in cosmetic and department stores. In addition, Sephora sells online on Amazon and on its online store. The company
sells hundreds of brands, including many of its own. It operates in a very competitive market where customer care and
advertising are critical. Sephora sells some products for men, but most beauty products are targeted to women.
THE SOLUTION
Sephora’s first use of chatbots occurred through messaging services. The purpose of the first bot was to search for information
for the company’s resources such as videos, images, tips, and so on. This bot operates in a question-and-answer (Q&A) mode.
It recommends relevant content based on customers’ interests. The company aims to appeal to young customers messaging
on Kik.
Sephora researchers found that customers conversing with the Kikbot were engaged deeply in the dialog. Then the bot
encouraged them to explore new products. Sephora’s newer bot called Reservation Assistant was placed on Facebook
Messenger. It enables customers to book or reschedule makeover appointments.
Another Sephora bot delivered on Kik is Shade-Matching. It matches lips colors to photos (face and lips) uploaded by
users and recommends the best match to them. The bot also lets users try on photos of recommended colors, using Sephora
Virtual Artist that runs on Facebook Messenger. Bots are deployed as mobile apps. If users like the recommendation, they are
directed to the company’s Web store to buy the products. Users can upload photos taken with selfies so that the program can
do the matching. Over 4 million visitors tried 90 million shades in the first year of Virtual Artist’s operation.
The Q&A collection of the knowledge base was built by connecting it with store experts. Knowledge acquisition
techniques (Chapter 2) were used for this purpose. The company’s bots use NLPs that were trained to understand the typical
vocabulary of users.
THE RESULTS
The company’s customers loved the bots. In addition, Sephora learned the importance of providing assistance and guidance
to users who are motivated to return (at a reasonable cost!), happier, and more engaged.
Sephora’s bot asks users questions to find their tastes and preferences. Then it acts like a recommendation system (Section
12.2), offering products. Kik and Messenger users can purchase items without leaving the messaging service.
Finally, the company has improved the bots’ knowledge over time and plans new bots for additional tasks.
Note: Sephora was selected by Fast Company Magazine, March/April 2018, as one of the “World’s Most Innovative Companies.” Sephora is known for its digital
transformation and innovation (Rayome, 2018). Also, Sephora’s bots are considered among the top marketing chatbots (Quoc, 2017).
Sources: Compiled from Arthur (2016), Rayome (2018), and Taylor (2016), theverge.com/2017/3/16/14946086/ sephora-virtual-assistant-ios-app-
update-ar-makeup/, and sephora.com/.
u QUESTIONS FOR THE OPENING VIGNETTE
90 Part IV • Robotics, Social Networks, AI and IoT
1. List and discuss the benefits of bots to the company.
2. List and discuss the benefits of bots to customers.
3. Why were the bots deployed via Messenger and Kik?
4. What would happen to Sephora if competitors use a similar approach?
WHAT WE CAN LEARN FROM THIS VIGNETTE
In the highly competitive world of retail beauty products, customer care and marketing are critical. Using only live employees
can be very expensive. In addition, customers are shopping 24/7, and physical stores are open during limited hours and days.
In addition, there are large combinations of certain beauty products (e.g., many shades/colors) available. Sephora decided to
use chatbots on Facebook Messenger and Kik to engage its customers. Chatbots, the subject of this chapter, are available 24/7
at a lower cost and are delivered via mobile devices. Bots deliver information to customers consistently and quickly direct
customers to easy online shopping. Sephora placed its chatbots on messaging services. The logic was that people like to chat
with friends on messaging services, and they may also like to chat with businesses.
In addition to several services to customers, using chatbots helps Sephora learn about customers. This type of chatbot is
the most common type for customer care and marketing. In this chapter, we cover several other types of knowledge systems,
including the pioneering expert systems, recommenders, virtual personal assistants offered by several large technology
companies, and robo advisors.
12.2 EXPERT SYSTEMS AND RECOMMENDERS
In Chapter 2 we introduced the reader to the concept of autonomous decision systems. An expert system is a category of
autonomous decision systems and are considered the earliest applications of AI. Expert systems use started in research
institutions in the early and mid1960s (e.g., Stanford University, IBM) and was adopted commercially during the 1980s.
Basic Concepts of Expert Systems (ES)
The following are the major concepts related to ES technology.
DEFINITIONS There are several definitions of expert systems. Our working definition is that an expert system is a computer-
based system that emulates decision making and/or problem solving of human experts. These decisions and problems are in
complex areas that require expertise to solve. The basic objective is to enable nonexperts to make decisions and solve problems
that usually require expertise. This activity is usually performed in narrowly defined domains (e.g., making small loans,
providing tax advice, analyzing reasons for machine failure). Classical ES use “what-if-then” rules for their reasoning.
EXPERTS An expert is a person who has the special knowledge, judgment, experience, and skills to provide sound advice and
solve complex problems in a narrowly defined area. It is an expert’s job to provide the knowledge about how to perform a
task so that a nonexpert will be able to do the same task assisted by ES. An expert knows which facts are important and
understands and explains the dependent relationships among those facts. In diagnosing a problem with an automobile’s
electrical system, for example, an expert car mechanic knows that a broken fan belt can be the cause for the battery to discharge.
There is no standard definition of expert, but decision performance and the level of knowledge a person has are typical
criteria used to determine whether a particular person is an expert as related to ES. Typically, experts must be able to solve a
problem and achieve a performance level that is significantly better than average. An expert at one time or in one region may
not be an expert in another time or region. For example, a legal expert in New York may not be one in Beijing, China. A
medical student may be an expert compared to the general public but not in making a diagnosis or performing surgery. Note
that experts have expertise that can help solve problems and explain certain obscure phenomena only within a specific domain.
Typically, human experts are capable of doing the following:
• Recognizing and formulating a problem.
• Solving a problem quickly and correctly.
• Explaining a solution.
• Learning from experience.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 91
• Restructuring knowledge.
• Breaking rules (i.e., going outside the general norms) if necessary.
• Determining relevance and associations.
Can a machine help a nonexpert perform like an expert? Can a machine make autonomous decisions that experts make?
Let us see. But first, we need to explore what expertise is.
EXPERTISE An expertise is the extensive, task-specific knowledge that experts possess. The level of expertise determines the
success of a decision made by an expert. Expertise is often acquired through training, learning, and experience in practice. It
includes explicit knowledge, such as theories learned from a textbook or a classroom and implicit knowledge gained from
experience. The following is a list of possible knowledge types used in ES applications:
• Theories about the problem domain.
• Rules and procedures regarding the general problem domain.
• Heuristics about what to do in a given problem situation.
• Global strategies for solving of problems amenable to expert systems.
• Meta knowledge (i.e., knowledge about knowledge).
• Facts about the problem area.
These types of knowledge enable experts to make better and faster decisions than nonexperts.
Expertise often includes the following characteristics:
• It is usually associated with a high degree of intelligence, but it is not always as-sociated with the smartest person.
• It is usually associated with a vast quantity of knowledge.
• It is based on learning from past successes and mistakes.
• It is based on knowledge that is well stored, organized, and quickly retrievable from an expert who has excellent recall
of patterns from previous experiences.
Characteristics and Benefits of ES
ES were used during the period 1980 to 2010 by hundreds of companies worldwide. However, since 2011, their use has
declined rapidly, mostly due to the emergence of better knowledge systems, three types of which are described in this chapter.
It is important, however, to understand the major characteristics and benefits of expert systems since many of them evolved
evidenced newer knowledge systems.
The major objective of ES is the transfer of expertise to a machine. The expertise will be used by nonexperts. A typical
example is a diagnosis. For example, many of us can use self-diagnosis to find (and correct) problems in our computers. Even
more than that, computers can find and correct problems by themselves. One field in which such ability is practiced is
medicine, as described in the following example:
Example: Are You Crazy?
A Web-based ES was developed in Korea for people to self-check their mental health status. Anyone in the world can access
it and get a free evaluation. The knowledge for the system was collected from a survey of 3,235 Korean immigrants. The
results of the survey were analyzed and then reviewed by experts via focus group discussions. For more information, see Bae
(2013).
BENEFITS OF ES Depending on the mission and structure of ES, the following are their capabilities and potential benefits:
• Perform routine tasks (e.g., diagnosis, candidate screening, credit analysis) that require expertise much faster than
humans.
• Reduce the cost of operations.
• Improve consistency and quality of work (e.g., reduce human errors).
92 Part IV • Robotics, Social Networks, AI and IoT
• Speed up decision making and make consistent decisions.
• May motivate employees to increase productivity.
• Preserve scarce expertise of retiring employees.
• Help transfer and reuse knowledge.
• Reduce employee training cost by using self-training.
• Solve complex problems without experts and solve them faster.
• See things that even experts sometimes miss.
• Combine expertise of several experts.
• Centralize decision making (e.g., by using the “cloud”).
• Facilitate knowledge sharing.
These benefits can provide a significant competitive advantage to companies that use ES. Indeed, some companies have saved
considerable amounts of money using them.
Despite these benefits, the use of ES is on the decline. The reasons for this and the related limitations are discussed later
in this section.
Typical Areas for ES Applications
ES have been applied commercially in a number of areas, including the following:
• Finance. Finance ES include analysis of investments, credit, and financial reports; evaluation of insurance and
performance; tax planning; fraud prevention; and financial planning.
• Data processing. Data processing ES include system planning, equipment selection, equipment maintenance, vendor
evaluation, and network management.
• Marketing. Marketing ES include customer relationship management, market research and analysis, product planning,
and market planning. Also, presale advice is provided for prospects.
• Human resources. Examples of human resource ES are planning, performance evaluation, staff scheduling, pension
management, regulatory advising, and design of questionnaires.
• Manufacturing. Manufacturing ES include production planning, complex product configuration, quality
management, product design, plant site selection, and equipment maintenance and repair (including diagnosis).
• Homeland security. These ES include terrorist threat assessment and terrorist finance detection.
• Business process automation. ES have been developed for desk automation, call center management, and regulation
enforcement.
• Healthcare management. ES have been developed for bioinformatics and other healthcare management issues.
• Regulatory and compliance requirements. Regulations can be complex. ES are using a stepwise process to ensure
compliance.
• Web site design. A good Web site design requires paying attention to many variables and ensures that performance
is up to standard. ES can lead to a proper design process.
Now that you are familiar with the basic concepts of ES, it is time to look at the internal structure of ES and how their
goals are achieved.
Structure and Process of ES
As you may recall from Section 2.5 and Figure 2.5, the process of knowledge extraction and its use is divided into two distinct
parts. In ES we refer to these as the development environment and the consultation environment (see Figure 12.1). An ES builder builds
the necessary ES components and loads the knowledge base with appropriate representation of expert knowledge in the
development environment. A nonexpert uses the consultation environment to obtain advice and solve problems using
the expert knowledge embedded into the system. These two environments are usually separated.
MAJOR COMPONENTS OF ES The major components in typical expert systems include:
• Knowledge acquisition. Mostly from human experts, is usually obtained by knowledge engineers. This knowledge, which
may derive from several sources, is integrated, validated, and verified.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 93
• Knowledge base. This is a knowledge repository. The knowledge is divided into knowledge about the domain and
knowledge about problem solving and solution procedures. Also, the input data provided by the users may be stored
in the knowledge base.
• Knowledge representation. This is frequently organized as business rules (also known as production rules).
FIGURE 12.1 General Architecture of Expert Systems.
• Inference engine. Also known as the control structure or the rule interpreter, this is the “brain” of ES. It provides the reasoning
capability, namely the ability to answer users’ questions, provide recommendations for solutions, generate predictions,
and conduct other relevant tasks. The engine manipulates the rules by either forward chaining or backward chaining.
In 1990s ES started to use other inference methods.
• User interface. This component allows user inference engine interactions. In classical ES, this was done in writing or by
using menus. In today’s knowledge systems, it is done by natural languages and voice.
These major components of ES generate useful solutions in many areas. Remember that these areas need to be well
structured and in fairly narrow domains. Less common is a justifier/explanation subsystem that shows users of rule-based
systems the chains of rules used to arrive at conclusions. Also, least common is a knowledge refining subsystem that helped to
improve knowledge (e.g., rules) when new knowledge is added.
A major provider of expert systems technologies was Exsys Inc. While the company is no longer active in this business,
its Web site (Exsys.com) is. It contains tutorials and a large number of cases related to its major software product, Exsys
Corvid. Application Case 12.1 is one example.
Application Case 12.1 ES Aid in Identification of Chemical, Biological, and Radiological Agents
94 Part IV • Robotics, Social Networks, AI and IoT
Terrorist attacks using chemical, biological, or
radiological (CBR) agents are of great concern due to
their potential for leading to large loss of life. The United
States and other nations have spent billions of d ollars on
plans and protocols to defend against acts of terrorism
that could involve CBR. However, CBR covers a wide
range of input agents with many specific organisms that
could be used in multiple ways. Timely response to such
attacks requires rapid identification of the input agents
involved. This can be a difficult process involving
different methods and instruments.
The U.S. Environmental Protection Agency (EPA)
along with Dr. Lawrence H. Keith, president of Instant
Reference Sources Inc. and other consultants, have
incorporated their knowledge, experience, and expertise
as well as information in publicly available EPA
documents to develop the CBR Advisor using Exsys
Inc.’s Corvid software.
One of the most important parts of the CBR
Advisor is providing advice in logical step-bystep
procedures to determine the identity of a toxic agent
when little or no information is available, which is typical
at the beginning of a terrorist attack. The system helps
response staff proceed according to a well-established
action plan even in such a highly stressful environment.
The system’s dual screens present three levels of
information: (1) a top/executive level with brief answers,
(2) an educational level with in-depth information, and
(3) a research level with links to other documents, slide
shows, forms, and Internet sites. CBR Advisor’s content
includes:
Why the Classical Type of ES Is Disappearing
• How to classify threat warnings.
• How to conduct an initial threat evaluation.
• What immediate response actions to take.
• How to perform site characterization.
• How to evaluate the initial site and safe entry to it.
• Where and how to best collect samples.
• How to package and ship samples for analysis.
Restricted content includes CBR agents and methods
for analyzing them. The CBR Advisor can be used for
incident response and/or training. It has two different
menus, one for emergency response and another, longer
menu for training. It is a restricted software program and is
not publicly available.
Questions for Case 12.1
1. How can the CBR Advisor assist in making quick
decisions?
2. What characteristics of the CBR Advisor make it an
expert system?
3. What could be other situations in which similar expert
systems can be employed?
Expert systems are also used in high-pressure situations in
which human decision makers often need to take split-
second actions involving both subjective as well as objective
knowledge in responding to emergency situations.
Sources: www.exsys.com “Identification of Chemical, Biological and
Radiological Agents” http://www.exsyssoftware.com/CaseStudy
Selector/casestudies.html. April 2018. (Publicly available information.)
Used with permission.
The large benefits described earlier drove the implementation of many ES worldwide. However, like many other
technologies, the classical ES have been replaced by better systems. Let us first look at some of the limitations of ES that
contributed to its declining use.
1. The acquisition of knowledge from human experts has proven to be very expensive due to the shortage of good
knowledge engineers as well as the possible need to interview several experts for one application.
2. Any acquired knowledge needed to be updated frequently at a high cost.
3. The rule-based foundation was frequently not robust and not too reliable or flexible and could have too many
exceptions to the rules. Improved knowledge systems use data-driven and statistical approaches to make the
inferences with better success. In addition, case-based reasoning could work better only if a sufficient number of
similar cases were available. So, usually it cannot support ES.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 95
4. The rule-based user-interface needed to be supplemented (e.g., by voice communication, image maps). This could
make ES too cumbersome.
5. The reasoning capability of rule-based technology is limited compared to use of newer mechanisms such as those
used in machine learning.
NEW GENERATION OF EXPERT SYSTEMS Instead of using the old knowledge acquisition and
representation system, newer ES based on machine learning algorithms and other AI technologies
are deployed to create better systems. An example is provided in Application Case 12.2.
Application Case 12.2 VisiRule
VisiRule is an older ES company that remodeled its historical). In addition, rules can be created by business over time.
VisiRule (of the United Kingdom) machine learning (lower left side). provides easy-to-use diagramming tools to facilitate
The right-hand side (upper corner) illustrates the construction of ES. Diagramming allows easier the hybrid delivery
(consultation). Using interacextraction and use of knowledge in expert systems. tive questions and answers the system
can gener-
The process of building the knowledge base ate advice. In addition, rules can be used to process can be seen on
the left side of Figure 12.2. On the data remotely and update the data repository. Note left-hand side, you can see the
hybrid creation. that the dual delivery option is based on machine Using a decision tree, the domain experts can cre-
learning’s ability to discover hidden patterns in data ate additional rules directly from relevant data (e.g., that can be used
to form predictive decision models.
Domain Expert Expert Systems draws rules as deployed using decision tree using interactive VisiRule Author questionnaire
Human Expert Interactive
Hybrid Hybrid
Creation VisiRule Delivery
Machine Data- Learning Driven
Rules are used to Rules are created process data from data using remotely and
Machine Learning update database
FIGURE 12.2 The Process of Recommendation Systems.
96 Part IV • Robotics, Social Networks, AI and IoT
VisiRule also provides chatbots for improving the
interactive part of the process and supplies an interactive map.
According to the company’s Web site visirule.co.uk/, the major
benefits of the product are:
All-in-all, VisiRule provides a comprehensive AI-
based expert system.
Source: Courtesy of VisiRule Corp. UK. Used with permission.
Questions for Case 12.2
1. Which of the limitations of early ES have been solved
by the VisiRule system?
2. Compare Figures 12.2 and 12.1. What are the differences between the creation (Fig. 12.2) and the
development (Fig. 12.1) subsystems?
3. Compare Figures 12.2 and 12.1. What are the differences between the delivery (Fig. 12.2) and the
consultation (Fig. 12.1) subsystems?
4. Identify all AI technologies and list their contribution to the VisiRule system.
5. List some benefits of this ES to users.
• The charts allow creation of models that can be
immediately executed and validated.
• It is code-free; no programming is needed.
• The diagrams are drawn by human experts or
induced automatically from data.
• It contains self-assessment tools with report
generation and document production.
• The generated knowledge can be easily executed as
XML code.
• It provides explanation and justification.
• The interactive expert advice attracts new
customers.
• It can be used for training and advising employees. • Companies can easily access the corporate
knowledge repository.
• The charts to use VisiRule authoring tools are
created with ease using flowcharting and decision
trees.
Three major AI types of applications that overcome the earlier discussed limitations of RS are chatbots, virtual
personal assistants, and robo advisors, which are presented next in this chapter. Other AI technologies that perform similar
activities are presented in Chapters 4 to 9. Most notable is IBM Watson (Chapter 6); some of its advising capabilities are
similar to those of ES but are much superior.
Another similar AI technology, the recommendation system, is presented next. Its newer variations use machine
learning and IBM Watson Analytics.
Recommendation Systems
A heavily used knowledge system for recommending one-to-one targeted products or services is the recommendation
system, also known as recommender system or recommendation engine. Such a system tries to predict the importance (rating or
preference) that a user will attach to a product or service. Once the rating is known, a vendor knows users’ tastes and
preferences and can match and recommend a product or service to the user. For comprehensive coverage, see Aggarwal
(2016). For a comprehensive tutorial and case study, see analyticsvidhya.com/blood/2015/10/ recommendation-
engines/.
Recommendation systems are very common and are used in many areas. Top applications include movies, music, and
books. However, there are also systems for travel, restaurants, insurance, and online dating. The recommendations are typically
given in rank order. Online recommendations are preferred by many people over regular searches, which are less
personalized, slower, and sometimes less accurate.
BENEFITS OF RECOMMENDATION SYSTEMS Using these systems may result in substantial benefits both to buyers and sellers
(see Makadia, 2018).
Benefits to customers are:
• Personalization. They receive recommendations that are very close to fulfilling what they like
or need. This depends, of course, on the quality of the method used.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 97
• Discovery. They may receive recommendations for products that they did not even know
existed but were what they really need.
• Customer satisfaction. With repeated recommendations tends to increase.
• Reports. Some recommenders provide reports and others provide explanations about the
selected products.
• Increased dialog with sellers. Because recommendations may come with explanations, buyers
may want more interactions with the sellers.
Benefits to sellers are:
• Higher conversion rate. With personalized product recommendations, buyers tend to buy more.
• Increased cross-sell. Recommendation systems can suggest additional products. Amazon.com,
for example, shows other products that “people bought together with the product you
ordered.”
• Increased customer loyalty. As benefits to customers increase, their loyalty to the seller increases.
• Enabling of mass customization. This provides more information on potential customized
orders.
Several methods are (or were) used for building recommendation systems. Two classic methods are
collaborative filtering and content-based filtering.
COLLABORATIVE FILTERING This method builds a model that summarizes the past behavior of
shoppers, how they surf the Internet, what they were looking for, what they have purchased, and
how much they like (rate) the products. Furthermore, collaborative filtering considers what
shoppers with similar profiles bought and how they rated their purchases. From this, the method
uses AI algorithms to predict the preference of both old and new customers. Then, the computer
program makes a recommendation.
CONTENT-BASED FILTERING This technique allows vendors to identify preferences by the attributes of
the product(s) that customers have bought or intend to buy. Knowing these preferences, the vendor
recommends to customers products with similar attributes. For instance, the system may
recommend a text-mining book to a customer who has shown interest in data mining, or action
movies after a consumer has rented one in this category.
Each of these types has advantages and limitations (see example at en.wikipedia.org/
wiki/Recommender_system). Sometimes the two are combined into a unified method.
Several other filtering methods exist. Examples include rule-based filtering and a ctivity-based
filtering. Newer methods include machine learning and other AI technologies, as illustrated in
Application Case 12.3.
Application Case 12.3 Netflix Recommender: A Critical Success Factor
According to ir.netflix.com, Netflix is (Spring 2018 TV shows and movies per day, including original data) the world’s
leading Internet television network series, documentaries, and feature films. Members with more than 118 million
members in over 190 can view unlimited shows without commercials for countries enjoying more than 150 million
hours of a monthly fee.
98 Part IV • Robotics, Social Networks, AI and IoT
The Challenges
Netflix has several million titles and now produces its own
shows. The large titles inventory often creates a problem
for customers who have difficulty determining which
offerings they want to watch. An additional challenge is
that Netflix expanded its business from the United States
and Canada to 190 other countries. Netflix operates in a
very competitive environment in which large players such
as Apple, Amazon.com, and Google operate. Netflix was
looking for a way to distinguish itself from the competition
by making useful recommendations to its customers.
The Original Recommendation Engine
Netflix originally was solely a mail-order business for
DVDs. At that time, it encountered inventory problems
due to its customers’ difficulties in determining which
DVDs to rent. The solution was to develop a
recommendation engine (called Cinematch) that told
subscribers which titles they probably would like.
Cinematch used data mining tools to sift through a
database of billions of film ratings and customers’ rental
histories. Using proprietary algorithms, it recommended
rentals to customers. The recommendation was
accomplished by comparing an individual’s likes, dislikes,
and preferences against those of people with similar tastes,
using a variant of collaborative filtering. Cinematch was like
the geeky clerk at a small movie store who sets aside titles
he knows you will like and suggests them to you when you
visit the store.
To improve Cinematch’s accuracy, Netflix began a
contest in October 2016, offering $1 million to the first
person or team that will write a program that would
increase Cinematch’s prediction accuracy by at least 10
percent. The company understood that this would take
quite some time; therefore, it offered a $50,000 Progress
Prize each year in which the contest was conducted. After
more than two years of competition, the grand prize went
to Bellkor’s Pragmatic Chaos team, a combination of two
runner-up teams.
To learn how the movie recommendation
algorithms work, see quora.com/How-does-the-
Netflixmovie-recommendation-algorithm-work/.
The New Era
As time passed, Netflix moved to the streaming business
and then to Internet TV. Also, the spread of cloud technology
enabled improvement in the
recommendation system. The new system stopped making
recommendations based on what people have seen in the past.
Instead, it is using Amazon’s cloud to mimic the human
brain in order to find what people really like in their
favorite movies and shows. The system is based on AI and
its technology of deep learning. The company can now
visualize Big Data and draw insights for the
recommendations. The analysis is also used in creating the
company’s productions. Another major change dealt with
the transformation to the global arena. In the past,
recommendations had been based on information
collected in the country (or region) where users live. The
recommendations were based on what other people in the
same country enjoyed. This approach did not work well in
the global environment due to cultural, political, and social
differences. The modified system considers what people
who live in many countries view and their viewing habits
and likes.
Implementation of the new system was difficult,
especially when a new country or region was added.
Recommendations were initially made without knowing
much about the new customers. It took 70 engineers and a
year of work to modify the recommendation system. For
details, see Popper (2016).
The Results
As a result of implementing its recommender system,
Netflix has seen very fast growth in sales and membership.
The benefits include the following:
• Effective recommendations. Many Netflix
members select their movies based on
recommendations tailored to their individual tastes.
• Customer satisfaction. More than 90 per-
cent of Netflix members say they are so s atisfied
with the Netflix service that they recommend it to
family members and friends.
• Finance. The number of Netflix members has grown from 10 million in 2008 to 118 million in
2018. Its sales and profits are climbing steadily. In
spring 2018, Netflix stock sold for over $400 per
share compared with $140 a year earlier.
Sources: Based on Popper (2016), Arora (2016), and StartUp (2016).
(Continued)
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 99
Application Case 12.3 (Continued)
Questions for Case 12.3
1. Why is the recommender system useful? (Relate it to
one-to-one targeted marketing.)
2. Explain how recommendations are generated.
3. Amazon disclosed its recommendation algorithms to
the public but Netflix did not. Why?
4. Research the research activities that attempt to “mimic
the human brain.”
5. Explain the changes due to the globalization of the
company.
u SECTION 12.2 REVIEW QUESTIONS
1. Define expert systems.
2. What is the major objective of ES?
3. Describe experts.
4. What is expertise?
5. List some areas especially amenable to ES.
6. List the major components of ES and describe each briefly.
7. Why is ES usage on the decline?
8. Define recommendation systems and describe their operations and benefits.
9. How do recommendation systems relate to AI?
12.3 CONCEPTS, DRIVERS, AND BENEFITS OF CHATBOTS
The world is now infested with chatbots. According to 2017 data (Knight, 2017c), 60 percent of millennials have already
used chatbots and 53 percent of those who have not used them are interested in doing so. Millennials are not the only
generation using chatbots, although they may use them more than others. What chatbots are and what they do is the subject
of this section.
What Is a Chatbot?
Short for chat robot, a chatbot, also known as a “bot” or “robo,” is a computerized service that enables easy conversations
between humans and humanlike computerized robots or image characters, sometimes over the Internet. The conversations
can be in w riting, and more and more are by voice and images. The conversations frequently involve short questions and
answers and are executed in a natural language. More intelligent chatbots are equipped with NLPs, so the computer can
understand unstructured dialog. Interactions also can occur by taking or uploading images (e.g., as is done by Samsung
Bixby on the Samsung S8 and 8). Some companies experiment with learning chatbots, which gain more knowledge with their
accumulated experience. The ability of the computer to converse with a human is provided by a knowledge system (e.g.,
rulebased) and a natural language understanding capability. The service is often available on messaging services such as
Facebook Messenger or WeChat, and on Twitter.
Chatbot Evolution
Chatbots originated decades ago. They were simple ES that enabled machines to answer questions posted by users. The
first known such machine was Eliza (en.wikipedia. org/wiki/ELIZA). Eliza and similar machines were developed to
work in Q&A mode.
The machine evaluated each question, usually to be found in a bank of FAQs, and generated an answer matched to each
question. Obviously, if the question was not in the FAQ collection, the machine provided irrelevant answers. In addition,
because the power of the natural language understanding was limited, some questions were misunderstood and the answers
were at times at best entertaining. Therefore, many companies opted to use live chats, some with inexpensive labor,
100 Part IV • Robotics, Social Networks, AI and IoT
organized as call centers around the globe. For more about Eliza’s current generation, and how to build it, see
search.cpan.org/dist/
Chatbot-Eliza/Chatbot/Eliza.pm/. Chatbot use and reputation are rapidly increasing globally.
Example
Sophia is a chatbot created in Hong Kong and was awarded citizenship by Saudi Arabia in October 2017. Because she is
not a Muslim, she is not wearing a hijab. She can answer many questions. For details, see newsweek.com/Saudi-arabia-
robot-sophia-muslim-694152/.
TYPES OF BOTS Bots can be classified by their capabilities; three classes follow:
1. Regular bots. These are essentially conversational intelligent agents (Chapter 2). They can do simple, usually
repetitive, tasks for their owners, such as showing their bank’s debits, helping them to purchase goods online, and to
sell or buy stocks online.
2. Chatbots. In this category, we include more capable bots, for example, those that can stimulate conversations with
people. This chapter deals mainly with chatbots.
3. Intelligent bots. These have a knowledge base that is improving with experience. That is, these bots can learn, for
example, a customer’s preferences (e.g., like Alexa and some robo advisors).
A major limitation of the older types of bots was that updating their knowledge base was both slow and expensive.
They were developed for specific narrow domains and/or specific users. It took many years to improve the supporting
technology. NLP has become better and better. Knowledge bases are updated today in the “cloud” in a central location;
the knowledge is shared by many users so the cost per user is reduced.
The stored knowledge is matched with questions asked by users. The answers by the machines have improved
dramatically. Since 2000, we have seen more and more capable AI machines for Q&A dialogs. Around 2010, conversational
AI machines were named chatbots and later were developed into virtual personal assistants, championed by Amazon’s
Alexa.
DRIVERS OF CHATBOTS The major drivers are:
• Developers are creating powerful tools to build chatbots quickly and inexpensively with useful functionalities.
• The quality of chatbots is improving, so conversations are getting more useful to users.
• Demand for chatbots is growing due to their potential cost reduction and improved customer service and marketing
services, which are provided 24/7.
• Use of chatbots allows rapid growth without the need to hire and train many cus-tomer service employees.
• Using chatbots, companies can utilize the messaging systems and related apps that are the darlings for consumers,
especially younger ones.
Components of Chatbots and the Process of Their Use The major components of chatbots are:
• A person (client).
• A computer, avatar, or robot (the AI machine).
• A knowledge base that can be embedded in the machine or available and con-nected to the “cloud.”
• A human-computer interface that provides the dialog for written or voice modes.
• An NLP that enables the machine to understand natural language.
Advanced chatbots can also understand human gestures, cues, and voice variations.
PERSON-MACHINE INTERACTION PROCESS The components just listed provide the framework for people-bot conversation.
Figure 12.3 shows the conversation process.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 101
• A person (left side of the figure) needs to find some information, or need some help.
• The person asks a related question from the bot by voice, texting, and so on.
• NLP translates the question to machine language.
• The chatbot transfers the question to cloud services.
• The cloud contains a knowledge base, business logic, and analytics (if appropri-ate) to craft a response to the
question.
• The response is transferred to a natural language generation program and then to the person who asked the question
in the preferred mode of dialog.
Drivers and Benefits
Chatbot use is driven by the following forces and benefits:
• The need to cut costs.
• The increasing capabilities of AI, especially NLP and voice technologies.
• The ability to converse in different languages (via machine translation).
• The increased quality and capability of captured knowledge.
• The push of devices by vendors (e.g., virtual personal assistants such as Alexa from Amazon and Google Assistant
from Alphabet).
• Its use for providing superb and economic customer service and conducting mar-ket research.
• Its use for text and image recognition.
• Its use to facilitate shopping.
• Its support of decision making.
Chatbots and similar AI machines have been improved over time. Chatbots are beneficial to both users and organizations.
For example, several hospitals employ robot receptionists to direct patients to their place of treatment. Zora Robotics
102 Part IV • Robotics, Social Networks, AI and IoT
created a robot named Nao to act as a chatting companion for people who are sick or elderly. The bot acts, for example,
as a form of therapy for those suffering from dementia.
Note: For some limitations of chatbots, see Section 12.7.
Representative Chatbots from Around the World
For a chatbot directory of the more than 1,250 bots in 53 countries as of April 2018, see chatbots.org/ and at
botlist.co/bots/. Examples of chatbots and what they can do from chatbot.org/ are provided here:
• RoboCoke. This is a party and music recommendation bot created for Coca-Cola in Hungary.
• Kip. This shopping helper is available on Slack (a messaging platform). Tell Kip what you want to buy, and Kip will
find it and even buy it for you.
• Walnut. This chatbot can discover skills relevant to you and help you learn them. It analyzes a large set of data
points to discover the skills.
• Ride sharing by Taxi Bot. If you are not sure whether Uber, Lyft, Grab, or Comfort DelGro is the cheapest
service, you can ask this bot. In addition, you can get current promo codes.
• ShopiiBot. When you send a picture of a product to this bot, it will find similar ones in seconds. Alternatively, tell
ShopiiBot what kind of product you are looking for at what price, and it will find the best one for you.
• Concerning desired trips. It can answer questions regarding events, restaurants, and attractions in major
destinations.
• BO.T. The first Bolivian chatbot, it talks to you (in Spanish) and answers your questions about Bolivia, its culture,
geography, society, and more.
• Hazie. She is your digital assistant that aims to close the gap between you and your next career move. Job seekers
can converse directly with Hazie just as they do with a job placement agent or friends.
• Green Card. This Visabot product helps users to properly file requests for Green Cards in the United States.
• Zoom. Zoom.ai (botlist.co/bots/369-zoomai), an automated virtual assistant, is for everyone in the workplace.
• Akita. This chatbot (botlist.co/bots/1314-akita) can connect you to businesses in your area.
As you can see, chatbots can be used for many different tasks. Morgan (2017) classifies bots into the following categories:
education, banking, insurance, retail, travel, healthcare, and customer experience.
MAJOR CATEGORIES OF CHATBOTS’ APPLICATIONS Chatbots are used today for many purposes and in many industries and
countries. We divide the applications into the following categories:
• Chatbots for enterprise activities, including communication, collaboration, cus-tomer service, and sales (such as in
the opening vignette). These are described in Section 12.4.
• Chatbots that act as personal assistants. These are presented in Section 12.5.
• Chatbots that act as advisors, mostly on finance-related topics (Section 12.6).
For a discussion of these categories, see Ferron (2017).
u SECTION 12.3 REVIEW QUESTIONS
1. Define chatbots and describe their use.
2. List the major components of chatbots.
3. What are the major drivers of chatbot technology?
4. How do chatbots work?
5. Why are chatbots considered AI machines?
12.4 ENTERPRISE CHATBOTS
Chatbots play a major role in enterprises, both in external and internal applications. Some believe that chatbots can
fundamentally change the way that business is done.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 103
The Interest of Enterprises in Chatbots
The benefits of chatbots to enterprises are increasing rapidly, making dialog less expensive and more consistent. Chatbots
can interact with customers and business partners more efficiently, are available anytime, and can be reached from
anywhere. Businesses are clearly paying attention to the chatbot revolution. According to Beaver (2016), businesses should
look at enterprise bots for the following reasons:
• “AI has reached a stage in which chatbots can have increasingly engaging and human conversations, allowing
businesses to leverage the inexpensive and widereaching technology to engage with more consumers.
• Chatbots are particularly well suited for mobile-perhaps more so than apps. Messaging is at the heart of the mobile
experience, as the rapid adoption of chat app demonstrates.
• The chatbot ecosystem is already robust, encompassing many different third-party chat bots, native bots,
distribution channels, and enabling technology companies.
• Chatbots could be lucrative for messaging apps and the developers who build bots for these platforms, similar to
how app stores have developed into moneymaking ecosystems.”
A study conducted in 2016 found that 80 percent of businesses want chatbots by 2020 businessinsider.com/80-of-
businesses-want-chatbots-by-2020-2016-12. For more opportunities in marketing, see Knight (2017a).
Enterprise Chatbots: Marketing and Customer Experience
As we saw in the opening vignette to this chapter and will see in in the several examples later in this chapter, chatbots are
very useful in providing marketing and customer service (e.g., Mah, 2016), obtaining sales leads, persuading customers to
buy products and services, providing critical information to potential buyers, optimizing advertising campaigns (e.g., a bot
named Baroj; see Radu, 2016), and much more. Customers want to do business on the app they are already in. For this
reason, many bots are on Facebook Messenger, Snapchat, WhatsApp, Kik, and WeChat. Using voice and texting, it is
possible to provide personalization as well as superb customer experience. Chatbots can enable vendors to improve
personal relationships with customers.
In addition to the marketing areas, plenty of chatbots are in areas such as financial (e.g., banks) and HRM services as
well as production and operation management for communication, collaboration, and other external and internal enterprise
business processes. In general, enterprises use chatbots on messaging platforms to run marketing campaigns (e.g., see the
opening vignette) and to provide superb customer experience.
IMPROVING THE CUSTOMER EXPERIENCE Enterprise chatbots create improved customer experience by providing a
conversation platform for quick and 24/7 contact with enterprises. When customers benefit from the system, they are
more inclined to buy and promote a specific brand. Chatbots can also supplement humans in providing improved customer
experience.
EXAMPLES OF ENTERPRISE CHATBOTS Schlicht (2016) provides a beginner’s guide to chatbots. He presents the following
hypothetical example about today’s shopping at Nordstrom (a large department store) versus the use of chatbots.
If you wanted to buy shoes from Nordstrom online, you would go to their Web site, look around until
you find the shoes you wanted, and then you would purchase them. If Nordstrom makes a bot, which I am
sure they will, you would simply be able to message Nordstrom on Facebook. It would ask you what you are
looking for and you would simply . . . tell it.
Instead of browsing a Web site, you will have a conversation with the Nordstrom bot, mirroring the
type of experience you would get when you go into the retail store.
Three additional examples follow:
104 Part IV • Robotics, Social Networks, AI and IoT
Example 1: LinkedIn
LinkedIn is introducing chatbots that conduct tasks such as comparing the calendars of people participating in meetings
and suggesting meeting times and places. For details, see CBS News (2016).
Example 2: Mastercard
Mastercard has two bots based on massaging platforms, one bot for banks and another bot for merchants.
Example 3: Coca-Cola
Customers worldwide can chat with Coca-Cola bots via Facebook Messenger. The bots make users
feel good with conversations that are increasingly becoming personalized. The bots collect
customers’ data, including their interests, problems, local dialect, and attitudes and then can target
advertisements tailored to each user.
A 5-min. video about Facebook is available at cnbc.com/2016/04/13/ why-facebook-is-
going-all-in-on-chatbots.html. It provides a Q&A session with David Marcus describing
Facebook’s increasing interest in chatbots.
WHY USE MESSAGING SERVICES? So far, we have noted that enterprises are using messaging services
such as Facebook Messenger, WeChat, Kik, Skype, and WhatsApp. The reason is that in 2017, more
than 2.6 billion people were chatting on messaging services. Messaging is becoming the most
widespread digital behavior. WeChat of China was the first to commercialize its service by offering
“chat with business” capabilities as illustrated in Application Case 12.4.
FACEBOOK’S CHATBOTS Following the example of WeChat, Facebook launched users’ conversations
with businesses’s chatbots on a large scale on Messenger, suggesting that users could message a
business just the way they would message a friend. The service allows businesses to conduct text
exchanges with users. In addition, the bots have a
Application Case 12.4 WeChat’s Super Chatbot
WeChat is a very large comprehensive messaging
• Conduct market research.service in
China and other countries with about
• Get information and
recommendations on
1 billion members in early 2018. It pioneered the use products and services. of bots in 2013
(see mp.weixin.qq.com). Users can
use the chatbot for activities such as the following:
• Hail a taxi.
• Order food to be delivered.
• Buy movie tickets and other items.
• Customize and order a pair of Nikes.
• Send an order to the nearest Starbucks.
• Track your daily fitness progress.
• Shop Burberry’s latest collection.
• Book doctor appointments.
• Pay your water bill.
• Host a business conference call.
• Send voice messages, emoticons, and snapshots to
friends.
• Send voice messages to communicate with businesses.
• Communicate and engage with customers.
• Provide a framework for teamwork and collaboration.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 105
• Launch a start-up on WeChat (you can make your own
bot on WeChat for this purpose).
Griffiths (2016) has provided information concerning a
Chinese online fashion flash sales company, Meici. The
company used its WeChat account to gather information
related to sales. Each time new users followed Meici’s account,
a welcome message instructed them on how to trigger
resources. WeChat is available in English and other languages
worldwide due to its usefulness. Facebook installed similar
capabilities in 2015.
Questions for Case 12.4
1. Find some recent activities that WeChat does.
2. What makes this chatbot so unique?
3. Compare the bot of WeChat to bots offered by Facebook.
Vera Gold Mark is a real estate developer of luxury high rises
in Punjab, India.
The Problem
Vera Gold Mark (VGM) is active in a very competitive market.
As a developer of luxury apartments, which are usually
expensive, it must try to attract many potential buyers and thus
needs as many sales leads as possible at a reasonable cost.
Chatting live with potential customers can be expensive since
it requires very knowledgeable and courteous agents available
24/7. VGM has a large inventory of units that must be sold as
soon as possible.
The Solution
VGM decided to use chatbots to supplement or replace
expensive manual live chats. These work in the following
ways. Buyers may click on the “chat with the robot” button
on the company’s Facebook page, and receive any information
they need. The chat helps VGM promote its available
products. When they click, users are able to chat and get
information about pricing, delivery dates, construction sites,
and much more for VGM projects. Users can also tweet. The
chatbots provide answers about the projects. Facebook
provides VGM access to potential buyers’ profiles (with users’
permission), which VGM sales teams can use to refine sales
strategies. The system is available 24/7.
Voice communication is coming soon (2018).
The Results
VGM is now viewed in a very positive way and is considered
to be very professional. VGM is getting good reviews for its
customer service. The builder is considered more honest and
unbiased because it provides written answers and promises to
customers. Salespeople at VGM get an increased number of
sales
leads,
and
because they know more about prospective customers, they
can better align them with units (optimal fit). The system is
also able to attract international buyers without increasing
cost. Because the system is available 24/7, global buyers can
easily evaluate VGM’s available condominiums.
The chatbot is also used as a teaching tool for new
employees. At the time that this case was written, no financial
data were available.
The technology is available to other builders from
Kenyt Technologies of India kenyt.com, which provides the
smart real estate chatbot.
Sources: Based on Garg (2017) and facebook.com/ veragoldmark/
(accessed April 2018).
learning ability that enables them to accurately analyze people’s input and provide correct responses.
Overall, as of early 2018, there were more than 30,000 company bots on Facebook Messenger. Some
companies use Messenger bots to recognize faces in pictures, suggesting recipients for targeted ads.
According to Guynn (2016), Facebook allows software developers access to its tools that build its
personal assistant called “M,” which combines AI with a human touch for tasks such as ordering
food or sending flowers. Using the M tools, developers can build applications for Messenger that
can have an increased understanding of requests made in natural languages. A major benefit of these
bots for Facebook is their collection of data and creation of profiles of users.
The following is another example of how the use of chatbots is facilitating customer service and
marketing (Application Case 12.5).
Application Case 12.5 How Vera Gold Mark Uses Chatbots to Increase Sales
106 Part IV • Robotics, Social Networks, AI and IoT
Questions for Case 12.5
1. List the benefits to VGM.
2. List the benefits to buyers.
3. What is the role of Kenyt Technologies?
Chatbots Magazine provides a three-part overview on the use of chatbots for retail and e-commerce. For details,
see chatbotsmagazine.com/chatbots-for-retail-and- e-commerce-part-three-c112a89c0b48.
Enterprise Chatbots: Financial Services
The second area in which enterprise bots are active is financial services. Here we briefly discuss their use in banking.
In Section 12.6, we present the robo financial advisors for investment.
BANKING A 2017 survey (Morgan, 2017) found that most people in the United States will bank via chatbots by 2019.
Chatbots can use predictive analytics and cognitive messaging to perform tasks such as making payments. They can inform
customers about personalized deals. Banks’ credit cards can be advertised via chatbots on Facebook Messenger. It
seems that customers prefer to deal with chatbots rather than with salespeople who can be pushy.
Examples
POSB of Singapore has an AI-driven bot on Facebook Messenger. The bot was created with the help of Kasisto,
Inc. of the United States. Using actual Q&A sessions, it took IT workers 11,000 hours to create the bot. Its knowledge
base was tested and verified. The bot can learn to improve its performance. Known as POSB digi-bank virtual
assistant, the service is accessed via Messenger. Customers save time rather than waiting for human customer service.
In the future, the service will be available on other messaging platforms. For details, see Nur (2017).
A similar application in Singapore is used by Citi Bank (by Citi Group). It can answer FAQs about people’s
accounts in a natural language (English). The bank is adding progressively more capabilities to its bot.
A generic banking bot is Verbal Access (from North Side Co.) that provides recommendations for banking
services (see Hunt, 2017).
Enterprise Chatbots: Service Industries
Chatbots are used extensively in many services. We provide several examples in the following sections.
HEALTHCARE Chatbots are extremely active in the healthcare area, helping millions of people worldwide (Larson,
2016). Here are a few examples:
• Robot receptionists direct patients to departments in hospitals. (Similar services are available at airports, hotels,
universities, government offices, and private and other public organizations.)
• Several chatbots are chatty companions for people who are elderly and sick (e.g., Zora Robotics).
• Chatbots are used in telemedicine; patients converse with doctors and healthcare professionals who are in
different locations. For example, the Chinese company Baidu developed the Melody chatbot for this purpose.
• Chatbots can connect patients quickly and easily with information they need.
• Important services in the healthcare field are currently provided by IBM Watson (Chapter 6).
For more on bots for healthcare, see the end of Section 12.6.
EDUCATION Chatbot tutors are used in several countries to teach subjects ranging from English (in Korea) to
mathematics (in Russia). One thing is certain: The chatbot treats all students equally. Students like the chatbots in
online education as well. Machine translation of languages will enable students to take online classes in languages
other than their own. Finally, chatbots can be used as private tutors.
GOVERNMENT According to Lacheca (2017), chatbots are spreading in government as a new dialog tool for use
by the public. The most popular use is in providing access to government information and answering
government-related questions.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 107
TRAVEL AND HOSPITALITY Chatbots are working as tour guides in several countries (e.g., Norway). They are not
only cheaper (or free) but also may know more than some human guides. Chatbots work as guides in several
hotels in Japan. In hotels, they act as concierges, providing information and personalized recommendations
(e.g., about restaurants). Chatbots can arrange reservations for hotel rooms, meals, and events. In busy hotels,
there is frequently a wait for human concierges; chatbots are available on smartphones all the time. As with
other computer services, the chatbots are fast, inexpensive, easy to reach, and always nice. They give excellent
customer experience.
An example of external travel service is given in Application Case 12.6.
Chatbot Platforms
CHATBOTS INSIDE ENTERPRISES So far we have seen chatbots that are working in the external side of enterprises,
mostly in customer care and marketing (e.g., the opening vignette). However, companies lately have started to
use chatbots to automate tasks for supporting internal communication, collaboration, and business processes.
According to
Application Case 12.6 Transavia Airlines Uses Bots for Communication and Customer Care Delivery
Background
The air travel business is very competitive, especially in
Europe. There is a clear trend for younger customers to
use wireless devices as well as social media sites and
chatting. Customers like to communicate with travel
businesses by using their preferred technology via their
preferred platforms. Most popular is Facebook
Messenger, where over 1.2 billion people chat, many times
via their smartphones. These users today interact not only
among themselves but also with the business world.
Messaging platforms such as Messenger, WhatsApp,
and WeChat are becoming the norm for this customer
group. Vendors are building smart apps for the messaging
platforms including bots.
Transavia’s Bot
Learning from other companies, Transavia decided to
create a bot on Facebook Messenger. To do so, it hired the
IT consultant Cognizant Digital Business unit, called
Mirabean, which specializes in conversation interfaces,
especially via bots. Transavia’s activities business
processes, marketing, and customer care were combined
with Mirabean’s technological experience to enable a quick
deployment of the bot in weeks. It now enables real-time
dialog with customers. The first application is Transavia
Flight Search, which provides flight information as well as
the ability to buy tickets. The system is now integrated with
business processes that facilitate other transactions via the
bot. Giving customers their digital tool of choice enables
Transavia to increase market share and to drive growth.
Note that KLM, the owner of Transavia, was the
first European airline that implemented a similar chatbot
on Facebook Messenger in 2016.
Sources: Compiled from Cognizant (2017) and transavia.com.
Questions for Case 12.6
1. What drives consumer preference for mobile devices
and chat?
2. Why was the bot placed on Facebook Messenger?
3. What were the benefits of using Cognizant?
4. What is the advantage of buying a ticket from a bot
rather than from an online store?
Hunt (2017), “Enterprise and internal chatbots are revolutionizing the way companies do business.”
Chatbots in enterprises can do many tasks and support decision-making activities. For examples,
see Newlands (2017a). Chatbots can cut costs, increase productivity, assist working groups, and
foster relationships with business partners. Representative examples of chatbot tasks are:
108 Part IV • Robotics, Social Networks, AI and IoT
• Help with project management.
• Handle data entry.
• Conduct scheduling.
• Streamline payments with partners.
• Advise on authorization of funds.
• Monitor work and workers.
• Analyze internal Big Data.
• Find discounted and less expensive products.
• Simplify interactions.
• Facilitate data-driven strategy.
• Use machine learning.
Facilitate and manage personal finance.
Given the large number of bots, it is not surprising that many developers started to offer tools
and platforms to assist in building chatbots as discussed in Technology Insights 12.1.
TECHNOLOGY INSIGHTS 12.1 Chatbots’ Platform Providers
Several companies provide platforms for building enterprise chatbots. The companies can construct
chatbots fairly easily using these tools for their entry into popular messaging platforms or for their Web
sites. Some of the tools have machine-learning capability to ensure that the bots learn with every interaction.
According to Hunt (2017), these are some popular vendors:
1. ChattyPeople. This chatbot builder assists in creating bots requiring minimal programming skills.
It simply allows a business to link its social media pages to its ChattyPeople account. The created
bot can: • Arrange for payments to or from social media contacts. • Use major payment providers such as Apple Pay and PayPal.
• Recognize variations in keywords. • Support messaging.
2. Kudi. This financial helper allows people to make payments to vendors directly from their messaging
apps, specifically, Messenger, Skype, and Telegram and through an Internet browser. Using the bot,
users can: • Pay bills.
• Set bill payment reminders. • Transfer money by sending text messages.
The bot is safe and it protects users’ privacy. Vendors can easily install it for use. 3. Twyla. This chatbot building platform is for improving existing customer care and offering live
chats. It acts as a messaging platform for customers who prefer to use chatting. The major objective
is to free humans in HR departments from routine tasks.
The most popular platforms are:
• IBM Watson. This package uses a neural network of 1 billion words for excellent understanding of natural languages (e.g., English, Japanese). Watson provides free development tools, such as Java
SDK, Node SDK, Pyton SDK, and iOS SDK.
• Microsoft’s Bot Framework. Similar to IBM, Microsoft offers a variety of tools translatable into
30 languages. It is an open source. The system has three parts, Bot Connector, Developer Portal,
and Bot Directory and is interconnected with Microsoft Language Understanding Intelligent Service
(LUIS) that understands users’ intent. The system also includes active learning technology. A
simplified tool is AZURE; see Section 12.7 and Afaq (2017). For a comparative table of 25 chatbots
platforms, see Davydova (2017). For a list of other platforms, see Ismail (2017).
Sources: Compiled from Hunt (2017) and Davydova (2017).
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 109
DisCussion Questions
1. What is the difference between a regular enterprise bot and a platform?
2. Discuss the benefits of ChattyPeople.
3. Discuss the need for Kudi.
4. Discuss the reasons for consumers to prefer messaging platforms.
For additional information about chatbot platforms for building enterprise chatbots, see
entrepreneur.com/article/289788.
INDUSTRY-SPECIFIC BOTS As we have seen, bots can be specialists (e.g., for investment advice,
customer service) or industry-specific experts (e.g., banking, airlines). An interesting bot for the
waste industry is Alto (from Bio Hi Tech Global), which enables users to communicate intelligently
with industrial equipment. This helps owners of the equipment make decisions that improve
performance levels, smooth maintenance routines, and facilitate communication.
Knowledge for Enterprise Chatbots
Knowledge for chatbots depends on their tasks. Most marketing and customer care bots require
proprietary knowledge, which is usually generated and maintained in-house. This knowledge is
similar to that of ES; in many cases, enterprise chatbots operate very similarly to ES except that the
interface occurs in a natural language and frequently by voice. For example, the knowledge of
Sephora’s bot (opening vignette) is specific to that company and its products and is organized in a
Q&A format.
On the other hand, chatbots that are used within the enterprise (e.g., to train employees or to
provide advice on security or compliance with government regulations) may not be company
specific. A company can buy this knowledge and modify it to fit local situations and its specific
needs (as is done in ES; e.g., see Exsys Inc.). Newer chatbots use machine learning to extract
knowledge from data.
PERSONAL ASSISTANTS IN THE ENTERPRISE Enterprise chatbots can also be virtual personal assistants
as will be described in Section 12.5. For example, these bots can answer work-related queries and
help in increasing employees’ decision-making capabilities and productivity.
u SECTION 12.4 REVIEW QUESTIONS
1. Describe some marketing bots.
2. What can bots do for financial services?
3. How can bots assist shoppers?
4. List some benefits of enterprise chatbots.
5. Describe the sources of knowledge for enterprise chatbots.
12.5 VIRTUAL PERSONAL ASSISTANTS
In the previous section, we introduced enterprise chatbots that can be used to conduct
conversations. In marketing and sales, they can facilitate customer relationship management (CRM,
execute searches for customers, provide information, and execute many specific tasks in
organizations for their customers and employees. For comprehensive coverage, including research
issues, see Costa et al. (2018)).
An emerging type of chatbot is designed as a virtual personal assistant for both individuals
and organizations. Known as a virtual personal assistant (VPA), this software agent helps people
110 Part IV • Robotics, Social Networks, AI and IoT
improve their work, assist in decision making, and facilitate their lifestyle. VPAs are basically
extensions of intelligent software agents that interact with people. VPAs are chatbots whose major
objective is to help people better perform certain tasks. At this time, millions of people are using
Siri with their Apple products, Google Assistant, and Amazon’s Alexa. The assistants’ knowledge
bases are usually universal, and they are maintained centrally in the “cloud,” which makes them
economical for a large number of users. Users can get assistance and advice from their virtual
assistants anytime. In this section, we provide some interesting applications. The first set of
applications involves virtual personal assistants, notably Amazon’s Alexa and Apple’s Siri and
Google Assistant. O’Brien (2016) provides a discussion of what personal assistant chatbots can do
for business. The second set (presented in Section 12.6) is about computer programs that act mostly
as advisors on specific topics (mostly investments).
Assistant for Information Search
A major task of virtual personal assistants is to help users conduct a search by voice for information.
Without the assistant, users need to surf the Internet to find information and many times abandon
the search. In business situations, users can call a live customer service agent for assistance. This
may be an expensive service for the vendors. Delegating the search to a machine may save sellers
considerable money and make customers happy by not having to wait for the service. For example,
Lenovo uses the noHold assistant in its Single Point of Search service to help customers find
answers to their questions.
If You Were Mark Zuckerberg, Facebook CEO
While Siri and Alexa were in development, Zuckerberg decided to develop his own personal
assistant to help him run his home and his work as the CEO of Facebook. He viewed this assistant
as Jarvis from Iron Man. Zuckerberg trained the bot to recognize his voice and understand basic
commands related to home appliances. The assistant can recognize the faces of visitors and monitor
the movement of Zuckerberg’s young daughter.
For details, see Ulanoff (2016).
The essentials of this assistant can be seen in a 2:13 min. video at youtube.com/
watch?v=vvimBPJ3XGQ and one (5:01 min.) at youtube.com/watch?v=vPoT2vdVkVc, with
the narration by Morgan Freeman. Today, similar assistants are available for a minimal fee or even
for free. The most well-known such assistant is Amazon’s Alexa.
Amazon’s Alexa and Echo
Of the several virtual personal assistants, the one considered the best in 2018 was Alexa. She was
developed by Amazon to compete with Apple’s Siri and is a superior product. (See Figure 12.4.)
Alexa works with a smart speaker, such as Amazon’s Echo (to be d escribed later).
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 111
FIGURE 12.4 Amazon’s Echo and Alexa. Source: McClatchy-Tribune/Tribune Content Agency LLC/
Alamy Stock Photo
Amazon’s Alexa is a cloud-based virtual personal voice assistant that can do many things
such as:
• Answer questions in several domains.
• Control smartphone operations with voice commands.
• Provide real-time weather and traffic updates.
• Control smart home appliances and other devices by using itself as a home auto-mation hub.
• Make to-do lists.
• Arrange music in Playbox.
• Set alarms.
• Play audio books.
• Control home automation devices, as well as home appliances (e.g., a microwave).
• Analyze shopping lists.
• Control a car’s devices.
• Deliver proactive notification.
• Shop for its user.
• Make phone calls and send text messages.
Alexa has the ability to recognize different voices, so it can provide personalized responses.
Also, she uses a mix of speech and touch to deliver news, hail an Uber, and play games. As time
passes, her capabilities and skill grow. For more capabilities, which are ever-increasing, see Johnson
(2017). For what Alexa can hear and remember and how she learns, see Oremus (2018).
Watch the 3:55 min. video of how Alexa works at youtube.com/ watch?v=jCtfRdqPlbw.
For more tasks, see cnet.com/pictures/what-can- amazon-echo-and-alexa-do-pictures/,
Mangalindan (2017), and tomsguide.com/ us/pictures-story/1012-alexa-tricks-and-easter-
eggs.html.
112 Part IV • Robotics, Social Networks, AI and IoT
ALEXA’S SKILLS In addition to the standard (native) capabilities listed, people can use Alexa apps
(referred to as Skills) to download customized capabilities to Alexa (via your smartphone). Skills are
intended to teach Alexa something new. The following are examples of Alexa’s Skills (Apps):
• Call Uber and find the cost of a ride.
• Order a pizza.
• Order take-out meals.
• Obtain financial advice.
• Start a person’s Hyundai Genesis car from inside her or his house (Korosec, 2016).
These skills are provided by third-party vendors; they are required to activate invocation
commands. There are tens of thousands of them.
For example, a person can say, “Alexa, call Uber to pick me up at my office at 4:30 p.m.” For
more on Amazon’s Alexa, see Kelly (2018); for its benefits, see Reisinger (2016).
Alexa is equipped with NLP user interface, so it can be activated by providing a voice
command. This is done by combining the Alexa software with Amazon’s intelligent speaker, Echo.
ALEXA’S VOICE INTERFACE AND SPEAKERS Amazon has a family of three speakers (or voice
communication devices for Alexa: Echo, Dot, and Tag. Alexa can be accessed by a Fire TV line and
some non-Amazon devices. For the relationship between Alexa and Echo, see Gikas (2016).
AMAZON’S ECHO Echo is a hands-free intelligent (or smart) wireless speaker that is controlled by
voice. It is the hardware companion of Alexa (a software product), so the two operate hand in hand.
Echo is always on, always listening. When Echo hears a question, command, or request, it sends the
audio to Alexa and from there up to the cloud. Amazon’s servers match responses to the questions,
delivering them to Alexa as “responses to questions” in a split second. Amazon’s Alexa/Echo is
now available in some Ford vehicles.
Amazon Echo Dot Amazon Echo Dot is the “little brother” of Echo. It offers full Alexa functionality
but has only one very small speaker. It can be linked to any existing speaker systems to provide an
Echo-like experience.
Amazon Echo Tap Amazon Echo Tap is another “little brother” of Echo that can be used on the go.
It is completely wireless and portable and can be charged via a charging dock.
Both Dot and Tap are less expensive than Echo, but they offer fewer functionalities and
lower quality. However, people who already have good home speakers can use Dot with them. For
a discussion about the three speakers, see Trusted Review at
trustedreviews.com/news/amazon-echo-show-vs-echo-2948302.
Note: Non-Amazon speakers for Alexa are available now (e.g., Eufy Genie, from third-party
vendors); some are inexpensive.
Note: Alexa was smart enough earlier to admit that she did not know an answer, but today,
she will make references to third-party sources for an answer she cannot make. For details and
examples, see uk.finance.yahoo.com/news/alexa-recommendthird-party-skills-
192700876.html.
ALEXA FOR THE ENTERPRISE While the initial use of Alexa was for individual consumers, her use for
business has increased. WeWork Corp. developed a platform for helping companies to integrate an
Alexa skill in meeting rooms, for example. For details, see Crook (2017), and
yahoo.com/news/destiny-2-alexa-skills-let-140946575.html/.
Apple’s Siri
Siri (short for Speech Interpretation and Recognition Interface) is an intelligent virtual personal
assistant and knowledge navigator. It is a part of Apple’s several operating systems. It can answer
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 113
questions, make recommendations, and perform some actions by delegating requests to a set of Web
services in the “cloud.” The software can adapt itself to the user’s individual language, search
preferences with continuing use, and return personalized results. Siri is available for free to iPhone
and iPad users.
Siri can be integrated into Apple’s Siri Remote. Using CarPlay, Siri is available in some auto
brands where it can be controlled by iPhone (5 and higher). Siri 2 is the 2017–2018 model.
VIV In 2016, Dag Kittlaus, the creator of Siri, introduced Viv, “an intelligent Interface for
everything.” Viv is expected to be the next generation of intelligent virtual interactions (for details,
see Matney, 2016). In contrast with other assistants, Viv is open to all developers (third-party
ecosystem products). Viv is now a Samsung Company. In 2017, Samsung launched its own personal
assistant for the Galaxy S8.
Google Assistant
Competition regarding virtual personal assistants is increasing with the improved capabilities of
Google Assistant, which was developed as a competitor to Siri to fit Android smartphones. An
interesting demonstration of it is available at youtube.com/ watch?v=WTMbF0qYWVs; some
advanced capabilities are illustrated in the video at youtube.com/watch?v=17rY2ogJQQs. For
details, see Kelly (2016). The product improved dramatically in 2018 as shown in CES 2018
Conference.
Other Personal Assistants
Several other companies have virtual personal assistants. For example, Microsoft Cortana is well
known. In September 2016, Microsoft combined Cortana and Bing (see Hachman, 2016). Alexa
and Cortana now work together. Note that it is estimated that by the year 2022, voice-enabled
personal assistants will reach 55 percent of all U.S. households. For this and the future of personal
assistants, see Perez (2017).
Competition Among Large Tech Companies
Apple and Google have provided their personal assistants to hundreds of million users of their
mobile devices. Microsoft has equipped over 250 million PCs with its personal assistant. Amazon’s
Alexa/Echo sells many more assistants than others. The competition is on voice-controlled
chatbots. Their competitors view them as “the biggest thing since the iPhone.”
Knowledge for Virtual Personal Assistants
As indicated earlier, the knowledge for virtual personal assistants is kept in the “cloud.” The reason
is that the assistants are commodities, available to millions of users, and need to provide dynamic,
updated information (e.g., weather conditions, news, stock prices). When the knowledge base is
centralized, its maintenance is performed in one place. This is in contrast with the knowledge of
many enterprise bots, for which updating is decentralized. Thus, Siri on an iPhone will always be
updated for its general knowledge by AAPL. Knowledge for the skills of Alexa has to be maintained
locally or by the third-party vendors that create them.
u SECTION 12.5 REVIEW QUESTIONS
1. Describe an intelligent virtual personal assistant.
2. Describe the capabilities of Amazon’s Alexa.
3. Relate Amazon’s Alexa to Echo.
114 Part IV • Robotics, Social Networks, AI and IoT
4. Describe Echo Dot and Tap.
5. Describe Apple’s Siri Google’s Assistant.
6. How is the knowledge of personal assistants maintained?
7. Explain the relationship between virtual personal assistants and chatbots.
12.6 CHATBOTS AS PROFESSIONAL ADVISORS (ROBO ADVISORS)
The personal assistants described in Section 12.5 can provide much information and rudimentary
advice. A special category of virtual personal assistants is designed to provide personalized
professional advice in specific domains. A major area for their activities is investment and portfolio
management where robo advisors operate.
Robo Financial Advisors
It is known that the vast majority of “buy” and “sell” decisions of stock trading on the major
exchanges, especially by financial institutions, are made by computers. However, computers can
also manage an individual’s accounts in a personalized way.
According to an A. T. Kearney’s survey (reported by Regan, 2015), robo advisors are defined
as online providers that offer automated, low-cost, personalized investment advisory services, usually
through mobile platforms. These robo advisors use algorithms that allocate, deploy, rebalance, and
trade investment products. Once enrolled for the robo service, individuals enter their investment
objectives and preferences. Then, using advanced AI algorithms, the robo will offer alternative
personalized investments for individuals to choose from funds or exchange-traded funds [ETFs]. By
conducting a dialog with the robo advisor, an AI program will refine the investment portfolio. This
is all done digitally without having to talk to a live person. For details, see Keppel (2016).
Evolution of Financial Robo Advisors
The pioneering emergence of Betterment Inc. in 2010 (described later) was followed by several
other companies (Future Advisor and Hedgeable in 2010 and Personal Capital, Wealthfront, and
SigFig in 2011 and 2012). Other well-known companies (Schwab Intelligent Portfolios, Acorns,
Vanguard RAS, and Ally) joined the crowd in 2014 and 2015. In 2016 and 2017, the brokerage
houses of E*Trade and TD Ameritrade joined, as did Fidelity and Merrill Edge. There is no question
that robo advisors are game-changing phenomena for the wealth management business, even
though their performance so far has not been much different from that of traditional, manual, and
financial services.
Robo advising companies try to cut costs by using ETFs, whose commission fees are
significantly lower than that of mutual funds. Annual fees vary as does the minimum amount of
required assets. Premium services are more expensive since they offer the opportunity to consult
human experts (advisors 2.0), which are described next.
Robo Advisors 2.0: Adding the Human Touch
As robo advisors matured, it became clear that sometimes they could not do an effective job by
themselves. Therefore, in late 2016, several of the fully automated advisors started to add what they
call the human touch (e.g., see Eule, 2017; Huang, 2017). Companies are adding a human option, or
partner with another company. For example, UBS Wealth Management Americas has partnered
with pure robo advisor SigFig.
Robo advisors with human additions vary in expertise. For example, Betterment (Plus
and Permission options), Schwab Intelligent Advisory, and Vanguard Personal Advisor Service
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 115
use certified financial planners (CFPs); other companies offer less expertise. For details, see
Huang (2017).
Application Case 12.7 describes how Betterment has added the human touch.
QUALITY OF ADVICE PROVIDED BY ROBO ADVISORS You may wonder how good the advice from
robo advisors is. The answer is that it depends on their knowledge, the type of investments
involved, the inference engine of the AI machine, and so on. However, remember that the
robots are not biased and are consistent. They may prove to be even
Application Case 12.7Betterment, the Pioneer of Financial Robo Advisors As the pioneer of financial robo advisors in 2010,
Betterment created an automated platform for wealth
management. Since then, it has played a leading role in a
growing industry. In 2017, the company controlled more
than $9 billion in assets, yielding over an 11 percent return
to its 200,000 members. Like other robo advisors,
Betterment appeals to investors who do not want to
manage their portfolio by themselves or pay the 2 to 3
percent annual fee charged by human advisors. The
company advertises the following benefits:
• Provides unlimited professional expert advice (by
the bot) anytime and anywhere.
• Provides advice from bots that contain the
knowledge of human investment advisors.
• Assists investors in making decisions of how much
to invest.
• Helps investors figure out how much risk to take.
• Helps in lowering investment-related tax.
• Provides actionable answers to questions.
• Advises on college savings.
• Helps plan for retirement.
• Assists in mortgage management (e.g., refinance).
• Provides personalized service via the use of
investors’ goal-based analysis.
Betterment has no account minimum (competitors
require up to $100,000).
Each investor’s portfolio is automatically adjusted to
market conditions to meet his or her goals. All portfolios
are built and managed by AI algorithms.
Premium Service—Adding the Human Touch
Like Amazon.com and Expedia, which started as pure
online companies and later added physical commerce, in
2017 Betterment added what it calls a human touch; its
Plus service is offered to customers with assets of over
$100,000 who are willing to pay an annual fee of 0.4
percent for this service. Using it, customers can interact
with human advisors in addition to the automated bot. An
even better service is the company’s Premium level, which
requires $250,000 in assets and charges 0.5 percent in fees.
While the quality of the automated service is getting
better with added knowledge (machine learning), complex
situations that require human intervention still remain.
This is where the Plus and Premium services enter the
picture. Several competitors also have added the human
touch to their offering.
Sources: Compiled from O’Shea (2017), Eule (2017), and
betterment.com (accessed April 2018).
Questions for Case 12.7
1. What are Betterment’s benefits to investors?
2. Compare Betterment to its major competitors (see
Eule, 2017).
3. What are the benefits of adding the human touch (i.e.,
compared to pure automation and only human
service)?
4. Find some new information about Betterment.
Write a report.
better than humans at one of the most important aspects in investment advising: know how to
legally minimize the related tax. This implies that institutional-grade tax-loss harvesting is now
within the reach of all investors. By contrast, some people believe that it is difficult to replace
investment brokers with robots. De Aenlle (2018) believes that humans are still dominating advisory
services (see the example of Nordea Bank by Pohjanpalo, 2017).
For a list of the best robo advisors, see Eule (2017), O’Shea (2016), and
investorjunkie.com/35919/roboadvisors. For comprehensive coverage of robo advisors in
116 Part IV • Robotics, Social Networks, AI and IoT
finance and investment, including the major companies in the advisory industry, see McClellan
(2016).
An emerging commercial robo advisor is being developed at Cornell University under the
name Gsphere. In addition, robo advisors appear in countries other than the United States (e.g.,
Marvelstone Capital in Singapore).
FINANCIAL INSTITUTIONS AND THEIR COMPETITION Several large financial institutions and banks have
reacted to robo advisors by creating their own or partnering with them. It is difficult to assess the
winners and losers in this competition because there are no sufficient long-term data. So far it seems
that customers like robo advisors, basically because they cost as little as 10 percent of full-service
human advisors. For a discussion and data, see Marino (2016). Note that some observers point to
the danger of using robo advisors in a declining stock market due to their use of ETFs.
Managing Mutual Funds Using AI
Many institutions and some individual investors buy stocks using AI algorithms. Some people prefer
to buy a mutual fund that picks its holding with AI. EquBot is such a fund (its symbol is AIEQ).
Its 2017 performance was above average.
The AI algorithms used by EquBot can process 1 million pieces of data each day.
They follow 6,000 companies. For details, see Ell (2018).
Other Professional Advisors
In addition to investment advisors, there are several other types of robo advisors ranging from travel
to medicine to legal areas.
The following are examples of noninvestment advisors:
• Computer operations. To cut costs, major computer vendors (hardware and software) try
to provide users with self-guides to solve encountered problems. If users cannot get help
from the guides, they can contact live customer service agents. This service may not be
available in real time, which can upset customers. Live agents are expensive, especially when
provided 24/7. Therefore, companies are using interactive virtual advisors (or assistants).
As an example, Lenovo Computers use a generic bot called noHold’s AI to provide
assistance to customers as a single point of help for conducting a search.
• Travel. Several companies provide advice on planning future national and international trips.
For example, Utrip (utrip.com) helps plan European trips. Based on their stated
objectives, travelers get recommendations for what to visit in certain destinations. The service
is different from others in that it customizes trips.
• Medical and health advisors. A large number of health and medical care advisors operate
in many countries. An example is Ad a Health of Germany. Founded in late 2017 as a chatbot,
it assists people in activities such as deciphering their ailments and can connect patients to
live physicians. This can be the future of health in adding bot-based patient-doctor
collaboration.
A list of the top useful chatbots as of 2017 is provided by TalKing (2017). It includes:
• Health Tap acts like a medical doctor by providing a solution to common symptoms provided
by patients.
• YourMd is similar to Health Tap.
• Florence is a personal nurse available on Facebook Messenger.
Other bots include OneStopHealth, HealthBot, GYANT, Buoy, Bouylon, and Mewhat.
• Bots are acting as companions (e.g., Endurance for dementia patients). In Japan, bots that
look and feel like dogs are very popular companions for elderly people. Several bots are
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 117
designed to increase patient engagement. For example, Lovett (2018) reports that a bot for
patient engagement increased patients’ response rate to a flu shot campaign by 30 percent.
Finally, the classic pioneering bot, ELIZA, acted as a very naïve psychologist.
• Shopping advisors (shopbots). Shopbots can act as shopping advisors. An example is Shop
Advisor (see shopadvisor.com/our-platform. It is a comprehensive platform that includes
three components to help companies attract customers. The platform is a self-learning system
that improves its operation over time. Its components are:
1. Product intelligence, which processes complex and diverse product data. It includes a
competitive analysis.
2. Context intelligence, which collects and catalogs contextual data points about marketing
facilities and inventories in different locations.
3. Shopper intelligence, which studies consumers’ actions related to different magazines,
mobile apps, and Web sites.
There are thousands of other shopping advisors. Sephora (opening vignette) has several of
them. There are chatbots for Mercedes cars and for top department stores such as Nordstrom, Saks,
and DFS. The use of shopping chatbots is increasing rapidly due to the use of mobile shopping and
mobile chatting on social networks. Marketers, as we stated earlier, can collect customer data and
deliver targeted ads and customer service to specific customers.
Another trend that facilitates online shopping with the assistance of bots is the increase in the
number of virtual personal shopping assistants. Users only have to tell Alexa by voice, for example,
to buy something for them. Better than that, they can use their smartphones from anywhere to tell
Alexa to go shopping. Ordering via voice directly from vendors (e.g., delivery of pizzas) is becoming
popular. In addition to chatbots that operate by sellers, there are bots for providing advice on what
and where to buy.
Example: Smart Assistant Shopping Bots
Shopping bots ask a few questions to understand what a customer needs and prefers. Then they
recommend the best match for the customer. This makes customers feel they are receiving
personalized service. The assistance simplifies the customer’s decision- making process. Smart
assistants also offer advice on issues of concern to customers via Q&A conversations. For a guided
test, go to a demo at smartassistant.com/advicebots. Note that these bots are essentially
recommendation systems and that users need to ask for advice whereas other recommendation
systems (e.g., that of Amazon.com) provide advice even when users do not ask for it.
A well-known global shopping assistant in the area of fashion is Alibaba’s Fashion AI. It helps
customers who shop in stores. When shoppers enter a fitting room, the AI Fashion Consultant goes
into action. For details of how this is done, see Sun (2017).
Another type of shopping advisor works as a virtual personal advisor to shoppers.
This type was developed from traditional e-commerce intelligent agents, such as bizrate.
com and pricegrabber.com.
IBM Watson
Probably the most knowledgeable virtual advisor is IBM Watson (see Chapter 6). Some examples
of its use follow:
• Macy’s developed a service, Macy’s On Call, to help customers navigate its physi-cal stores
while they shop. Using location-based software, the app knows where they are in the store.
118 Part IV • Robotics, Social Networks, AI and IoT
By using smartphones, customers can ask questions regarding products and services in the
stores and then receive a customized response from the chatbot.
• Watson can help physicians make a diagnosis (or verify one) quickly and suggest the best
treatment. Watson’s Medical Advisor can analyze images very fast and look for things that
physicians may miss. Watson already is used extensively in India where there is a large
shortage of doctors.
• Deep Thunder provides accurate weather-forecasting service.
• Hilton Hotels are using Watson-based “Connie Robot” in their front desks. Connie did a
superb job in experiments, and its service is improving.
Clark (2016) reports that 1 billion people will use Watson by 2018. This is in part because IBM
Watson is coming to smartphones as an advisor. For more, see Noyes (2016).
u SECTION 12.6 REVIEW QUESTIONS
1. Define robo advisor.
2. Explain how robo advisors work for investments.
3. Discuss some of the shortcomings of robo advisors for investments.
4. Explain the people-machine collaboration in robo advising.
5. Describe IBM Watson as an advisor.
12.7 IMPLEMENTATION ISSUES
Several implementation issues are unique to chatbots and personal assistants. Examples of
representative systems are described next.
Technology Issues
Many chatbots, including virtual personal assistants, have imperfect (but improving) voice
recognition. There is no good feedback system yet for voice recognition systems to tell users, in real
time, how well it understands them. In addition, voice recognition systems may not know when to
do a current task and need to ask for human intervention.
Chatbots that are internal to organizations need to be connected to an NLP system. This may
be a problem, but a bigger one may exist when chatbots are connected to the Internet, due to
security and connectivity difficulties.
Some chatbots need to be multilingual. Therefore, they need to be connected to a machine
language translator.
Disadvantages and Limitations of Bots
The following are points (which were observed at the time this book was written during 2017 and
2018) regarding bots’ disadvantages and limitations; some will disappear with time:
• Some bots provide inferior performance, at least during their initiation, making users
frustrated.
• Some bots do not properly represent their brand. Poor design may result in poor
representation.
• The quality of AI-based bots depends on the use of complex algorithms that are expensive
to build and use.
• Some bots are not convenient to use.
• Some bots operate in an inconsistent manner.
• Enterprise chatbots pose great security and integration challenges.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 119
For methods to eliminate some of the disadvantages and limitations, see Kaya 2017.
VIRTUAL ASSISTANTS UNDER ATTACK Cortana, Siri, Alexa, and Google Assistant are under attack by
people who are enraged at machines in general, or just like to make fun of them. In some cases, the
bots’ administrators try to compose a response to the attacks; in other cases, some machines provide
senseless responses to the senseless attacks.
Quality of Chatbots
While the quality of most systems is not perfect, it is improving over time. However, the quality of
those that retrieve information for users and are properly programmed can do a perfect job.
Generally speaking, the more a company invests in acquiring or leasing a chatbot, the better its
accuracy will be. In addition, bots that serve a large number of people, such as Alexa and Google
Assistant, exhibit an increasing level of accuracy.
QUALITY OF ROBO ADVISORS Given the short time since the emergence of robo advisors for financial
services, it is difficult to assess the quality of their advice. Backend Benchmarking publishes a
quarterly report (theroboreport.com) regarding robo advisor companies. Some reports are free.
According to this service, Schwab’s Intelligent Portfolio Robot was the top performer in 2017.
However, note that portfolio performance needs to be measured for the long run (e.g., 5 to 10
years).
A major issue when engaging bots is the potential loss of human touch. It is needed to build
trust and answer complex questions so customers can understand bots’ answers. Also, bots cannot
bring empathy or a sense of friendship. According to Knight (2017b), there is a solution to this.
First, bots should perform only tasks that they are suited to do. Second, they should provide a visible
benefit to the customer. Finally, because the bots face customers, the interactions must be fully
planned to make sure the customers are happy.
In addition, note that robo advisors provide personalized advice. For information as to which
robo may be best for you based on your objectives, see Eule (2017), who also provides a scorecard
for the leading companies in the field. Finally, Gilani (2016) provides a guide for robo advisors as
well as their possible dangers.
MICROSOFT’S TAY Tay was a Twitter-based chatbot that failed and was discontinued by Microsoft. It
collected information from the Internet, but Microsoft had not given the bot the knowledge of how
to deal with some inappropriate material used on the Internet (e.g., trolls, fake news). Therefore,
Tay’s output was useless and frequently offended its users. As a result, Microsoft discontinued the
service of Tay.
Setting Up Alexa’s Smart Home System
Alexa is useful in controlling smart homes. Crist (2017) proposed a six-step process for how to use
Alexa in smart homes:
1. Get a speaker (e.g., Echo).
2. Think about the location of the speaker.
3. Set up the smart home devices.
4. Sync related gadgets with Alexa.
5. Set up group and scene.
6. Fine-tune during the process.
These steps are demonstrated at cnet.com/uk/how-to/how-to-get-started-with- an-
alexa-smart-home/.
120 Part IV • Robotics, Social Networks, AI and IoT
Constructing Bots
Earlier, we presented some companies that provide development platforms for chatbots. In
addition, several companies can build bots for users, so they can also build a simple bot by
themselves. A step-by-step guide with the tools used is provided by Ignat (2017). The bot was
constructed on Facebook Messenger. Another guide for creating a Facebook Messenger bot is
provided by Newlands (2017b), who suggested the following steps:
1. Give it a unique name.
2. Give customers guides on how to build a bot and how to converse with it.
3. Experiment in making a natural conversation flow.
4. Make the bot sound smart, but use simple terminology.
5. Do not deploy all features at the same time.
6. Optimize and maintain the bot to constantly improve its performance.
There are several free sources for building chatbots. Most of them include “how-to”
instructions. Several messaging services (e.g., Facebook Messenger, Telegraph) provide both
chatbot platforms as well as their own chatbots. For a 2017 list of enterprise chatbot platforms and
their capabilities, see entrepreneur.com/article/296504.
USING MICROSOFT’S AZURE BOT SERVICE Azure is a comprehensive but not a very complex bot builder.
Its Bot Service provides five templates for quick and easy creation of bots. According to
docs.microsoft.com/en-us/bot-framework/azure-bot-serviceoverview/, any of the
templates shown in Table 12.1 can be used.
For a detailed tutorial for creating bots, see “Create a Bot with Azure Bot Service” at
docs.microsoft.com/en-us/bot-framework/azure-bot-service-overview/.
TABLE 12.1 Azure’s Templates
Template Description
Basic Creates a bot that uses dialogues to respond to user input.
Form Creates a bot that collects input from users via a guided conversation that is
created using Form Flow.
Language
understanding
Creates a bot that uses natural language models (LUIS) to understand user
intent.
Proactive Creates a bot that uses Azure Functions to alert users of events.
Question & Answer Creates a bot that uses a knowledge base to answer users’ questions.
Note: Microsoft also provides a bot framework on which bots can be constructed (similar to that of Facebook Messenger).
For Microsoft’s Bot and a tutorial, see Afaq (2017).
Chapter Highlights
• Chatbots can save organizations money, provide • Recommenders today use several AI
technologies a 24/7 link with customers and/or business part- to provide personalized
recommendations about ners, and are consistent in what they say. products and services.
• An expert system was the first commercially ap- • People can communicate with chatbots via
writplied AI product. ten messages, voice, and images.
• ES transfer knowledge from experts to machines • Chatbots contain a knowledge base and a
natural so the machines can have the expertise needed language interface.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 121
for problem solving. • Chatbots are used primarily for information • Classical ES use business
rules to represent search, communication and collaboration, and knowledge and generate
answers to users’ ques- rendering advice in limited, specific domains.
tions from it. • Chatbots can facilitate online shopping by pro-
• The major components of ES are knowledge viding information and customer service.
acquisition, knowledge representation, knowl- • Chatbots work very well with messaging
systems edge base, user interface, and interface engine. (e.g., Facebook Messenger, WeChat).
Additional components may include an expla- • Enterprise chatbots serve customers of all
types nation subsystem and a knowledge-refining and can work with business partners. They
can system. also serve organizational employees.
• ES help retain scarce knowledge in organizations. • Virtual personal assistants (VPAs) are
designed to
• New types of knowledge systems are superior to work with individuals and can be customized
for classical ES, making ES disappear. them.
• We distinguish three major types of chatbots: • VPAs are created as “native” products for the
enterprise, virtual personal assistants, and robo masses.
advisors. • A well-known VPA is Amazon’s Alexa that is ac-
• A relatively new application of knowledge sys- cessed via a smart speaker called Echo (or
other tems is the virtual personal assistant. Major smart speakers). examples of such assistants
are Amazon’s Alexa, • VPAs are available from several vendors. Well
Apple’s Siri, and Google’s Assistant. known are Amazon’s Alexa, Apple’s Siri, and •
Knowledge for virtual personal assistants is cen- Google’s Assistant.
trally maintained in the “cloud” and it is usually • VPAs can specialize in specific domains and
work disseminated via a Q&A dialog. as investment advisors.
• Personal assistants can receive voice commands • Robo advisors provide personalized
online in-
that they can execute. vestment advice at a much lower cost than • Personal assistants can
provide personalized ad- human advisors. So far, the quality seems to be vice to their owners.
comparable.
• Special breeds of assistants are personal advisors, • Robo advisors can be combined with
human adsuch as robo advisors, that provide personalized visors to handle special cases.
advice to investors.
Key Terms
Alexa expert systems robo advisors
chatbot Google’s Assistant Siri
Echo recommendation systems virtual personal assistant (VPA)
Questions for Discussion
1. Some people say that chatbots are inferior for chatting.
Others disagree. Discuss. 2. Discuss the financial benefits of chatbots. 3. Discuss how IBM Watson will reach 1 billion people by 2018
and what the implications of that are. 4. Discuss the limitation of chatbots and how to overcome
them.
5. Discuss what made ES popular for almost 30 years before
their decline. 6. Summarize the difficulties in knowledge acquisition from
experts (also consult Chapter 2). 7. Compare the ES knowledge-refining system with knowledge
improvement in machine learning.
8. Discuss the difference of enterprises’ use of chatbots
internally and externally. 9. Some people say that without a virtual personal assis-tant, a
home cannot be smart. Why?
122 Part IV • Robotics, Social Networks, AI and IoT
10. C ompare Facebook Messenger virtual assistant project M
with that of competitors.
11. E xamine Alexa’s skill in ordering drinks from Starbucks. 12. D iscuss the advantages of robo advisors over human
advisors. What are the disadvantages?
13. E xplain how marketers can reach more customers with bots. 14. A re robo advisors the future of finance? Debate; start with
Demmissie (2017). 15. Research the potential impact of chatbots on work and write
a summary.
Exercises
1. C ompare the chatbots of Facebook and WeChat. Which has more
functionalities? 2. Enter nuance.com and find information about Dragon Medical
Advisor. Describe its benefits. Write a report.
3. Enter shopadvisor.com/our-platform and review the platform’s
components. Examine the product’s capabilities and compare them
with those of two other shopping advisors. 4. Enter chatbots.org/ and join a forum of your interest. Also explore
research issues of your interest. Write a report. 5. T here is intense competition between all major tech companies
regarding their virtual personal assistants. New innovations and
capabilities appear daily. Research the status of these assistants for
Amazon, Apple, Microsoft, Google, and Samsung. Write a report. 6. S ome people believe that chatbots will change how people interact
with the Internet and browse online. Prepare a report regarding this. 7. E xplain why is Amazon’s Echo needed to work with Alexa? Read
howtogeek.com/253719/do-i-need-anamazon-echo-to-use-
alexa/. Write a report. 8. Find out how Simon Property Group is using chatbots across over
200 shopping malls. Write about the benefits to different types of
users and to the company. 9. Read recent information about enterprise bots. Write a report. 10. E nter gravityinvestments.com/digital-advice-platform- demo.
Would you invest in this project? Research and write a report.
11. E nter visirule.co.uk and find all products it has for expert systems.
List them and write a short report. 12. R esearch the role of chatbots in helping patients with dementia.
13. F ind information on the Baidu’s Melody chatbot and how it works
with Baidu Doctor. 14. Pose a question related to a chatbot on quora.com. Summarize the
answers received in a report. 15. N ina is an intelligent chatbot from Nuance Communication Inc.
that works for Alexa Internet of Things (IoT), smart homes, and
more. Find information and write a report about Nina’s capabilities
and benefits. 16. Microsoft partners with the government of Singapore to develop
chatbots for e-services. Find out how this is done.
17. S tudy the Tommy Hilfiger Facebook Messenger bot. Find out how
it is (and was) used in the company’s marketing campaigns. 18. Two comprehensive building tools for chatbots are Botsify and
Personality Forge (personalityforge.com). Compare the tools.
Write a report.
19. F ind information about the Alibaba-backed robo advisor Youyu by
Yunfeng’s Investment. What is unique about this service? Start by
visiting http://www. international-
adviser.com/news/1035281/alibababacked-retail-robo-
adviser-youyu-launches-honkkong/. 20. Enter exsys.com. Select three case studies and explain why they
were successful.
21. It is time now to build your own bot. Consult with your instructor
about which software to use. Have several bots constructed in your
class and compare their capabilities. Use Microsoft’s Azure if you
have some programming experience.
References
Afaq, O. “Developing a Chatbot Using Microsoft’s Bot Framework,
LUIS and Node.js (Part 1).” Smashing Magazine, May 30, 2017.
smashingmagazine.com/2017/05/ chatbot-microsoft-bot-
framework-luis-nodejs-part1/ (accessed April 2018). Aggarwal, C. Recommended Systems: The Textbook. [eTextbook]. New York,
NY: Springer, 2016. Arora, S. “Recommendation Engines: How Amazon and Netflix Are
Winning the Personalization Battle.” Martech Advisor, June 28, 2016. Arthur, R. “Sephora Launches Chatbot on Messaging App Kik.” Forbes,
March 30, 2016. Bae, J. “Development and Application of a Web-Based Expert System
Using Artificial Intelligence for Management of Mental Health by
Korean Emigrants.” Journal of Korean Academy of Nursing, April 2013. Beaver, L. “Chatbots Explained: Why Businesses Should Be Paying
Attention to the Chatbot Revolution.” Business Insider, March 4, 2016.
CBS News. “LinkedIn Adding New Training Features, News Feeds and
‘Bots.’” CBS News, September 22, 2016.
cbsnews.com/news/linkedin-adding-new-trainingfeatures-
news-feeds-and-bots (accessed April 2018).
Clark, D. “IBM: A Billion People to Use Watson by 2018.” The Wall Street
Journal, October 26, 2016. Cognizant. “Bot Brings Transavia Airlines Closer to Customers.”
Cognizant Services, 2017. https://www.cognizant.com/
content/dam/Cognizant_Dotcom/landing-page-
resources/transavia-case-study.pdf (accessed April 2018). Costa, A., et al. (eds.) Personal Assistants: Emerging Computational Technologies
(Intelligent Systems Reference Library). New York, NY: Springer, 2018. Crist, R. “How to Get Started with an Alexa Smart Home.” CNET, July
5, 2017. cnet.com/how-to/how-to-get-started-withan-alexa-smart-
home/ (accessed April 2018). Crook, J. “WeWork Has Big Plans for
Alexa for Business.” TechCrunch, November 30, 2017.
Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 123
Davydova, O. “25 Chatbot Platforms: A Comparative Table.” Chatbots
Journal, May 11, 2017. De Aenlle, C. “A.I. Has Arrived in Investing. Humans Are Still
Dominating.” The New York Times, January 12, 2018. Demmissie, L.
“Robo Advisors: The Future of Finance.” The Ticker Tape, March 13, 2017. Ell, K. “ETFs Powered by Artificial Intelligence Are Getting Smarter,
Says Fund Co-Founder.” CNBC News, January 23, 2018.
Eule, A. “Rating the Robo-Advisors.” Barron’s, July 29, 2017. Ferron, E. “Mobile 101: What Are Bots, Chatbots and Virtual
Assistants?” New Atlas, February 16, 2017. newatlas. com/what-is-
bot-chatbot-guide/47965/ (accessed April 2018). Garg, N. “Case Study: How Kenyt Real Estate Chatbot Is Generating
Leads.” Medium, June 22, 2017.
Gikas, M. “What the Amazon Echo and Alexa Do Best.” Consumer Reports,
July 29, 2016. consumerreports.org/ wireless-speakers/what-
amazon-echo-and-alexa-dobest (accessed April 2018).
Gilani, S. “Your Perfectly Diversified Portfolio Could Be in Danger—
Here’s Why.” Money Morning @ Wall Street, December 6, 2016.
Griffiths, T. “Using Chatbots to Improve CRM Data: A WeChat Case
Study.” Half a World, November 16, 2016. Guynn, J. “Zuckerberg’s
Facebook Messenger Launches ‘Chat Bots’ Platform.” USA Today, April
12, 2016. Hachman, M. “Microsoft Combines Cortana and Bing with Microsoft
Research to Accelerate New Features.” PCWorld, September 29, 2016. Huang, N. “Robo Advisers Get the Human Touch.” Kiplinger’s Personal
Finance, September 2017. Hunt, M. “Enterprise Chatbots and the Conversational Commerce
Revolutionizing Business.” Entrepreneur, July 3, 2017.
Ignat, A. “Iggy—A Chatbot UX Case Study.” Chatbot’s Life, August 9,
2017. chatbotslife.com/iggy-a-chatbot-ux-casestudy-
b5ac0379029c/ (accessed April 2018).
Ismail, K. “Top 14 Chatbot Building Platforms of 2014.” CMS Wire,
December 19, 2017. Johnson, K. “Everything Amazon’s Alexa Learned to Do in 2017.”
Venturebeat.com, December 29, 2017.
Kaya, E. Bot Business 101: How to Start, Run & Grow Bot/ AI Business. Kindle
Edition. Seattle, WA: Amazon Digital Services, 2017. Kelly, H. “Amazon wants Alexa everywhere.” CNN Tech, September 22,
2018. Kelly, H. “Battle of the Smart Speakers: Google Home vs. Amazon
Echo.” CNN Tech, May 20, 2016. money.cnn.
com/2016/05/20/technology/google-home-
amazonecho/index.html?iid=EL (accessed April 2018). Keppel, D. Best Robo-Advisor: Ultimate Automatic Wealth Management. North
Charleston, SC: Create Space Pub., 2016. Knight, K. “Expert: Bots May Be a Marketers New Best Friend.”
BizReport, December 7, 2017a. Knight, K. “Expert: How to Engage Chatbots Without Losing the
Human Touch.” BizReport, February 13, 2017b. Knight, K. “Report: Over Half of Millennials Have or Will Use Bots.” Biz
Report, February 24, 2017c.
Korosec, K. “Start Your Car from Inside Your Home Using Amazon’s
Alexa.” Fortune.com, August 18, 2016. Lacheca, D. “Conversational AI
Creates New Dialogues for Government.” eGovInnovation, October 24,
2017. Larson, S. “Baidu Is Bringing AI Chatbots to Healthcare.”
CNNTech, October 11, 2016. Lovett, L. “Chatbot Campaign for Flu Shots Bolsters Patient Response
Rate by 30%.” Healthcareitnews.com, January 24, 2018. Mah, P. “The State of Chatbots in Marketing.” CMOInnovation, November
4, 2016.
Makadia, M. “Benefits for Recommendation Engines to the Ecommerce
Sector.” Business 2 Community, January 7, 2018. Mangalindan, J. P. “RBC: Amazon Has a Potential Mega-Hit on Its
Hand.” Yahoo! Finance, April 25, 2017. Marino, J. “Big Banks Are Fighting
Robo-Advisors Head On.” CNBC News, June 26, 2016. Matney, L. “Siri-Creator Shows Off First Public Demo of Viv, ‘The
Intelligent Interface for Everything.’” Tech Crunch, May 9, 2016.
techcrunch.com/2016/05/09/siri-creatorshows-off-first-public-
demo-of-viv-the-intelligentinterface-for-everything (accessed
April 2018). McClellan, J. “What the Evolving Robo Advisory Industry Offers.” AAII
Journal, October 2016. Morgan, B. “How Chatbots Improve Customer Experience in Every
Industry: An Infograph.” Forbes, June 8, 2017.
Newlands, M. “How to Create a Facebook Messenger Chatbot for Free
Without Coding,” Entrepreneur, March 14, 2017a. Newlands, M. “10 Ways Enterprise Chatbots Empower CEOs.”
MSN.com, August 9, 2017b. msn.com/en-us/
money/smallbusiness/10-ways-enterprise-chatbotsempower-
ceos/ar-AApMgU8 (accessed April 2018). Noyes, K. “Watson’s the
Name, Data’s the Game.” PCWorld, October 7, 2016.
Nur, N. “Singapore’s POSB Launches AI-Driven Chatbot on Facebook
Messenger.” MIS Asia, January 19, 2017. O’Brien, M. “What Can Chatbots Do for Ecommerce?” ClickZ. com, April
11, 2016. Oremus, W. “When Will Alexa Know Everything?” Slate.com, April 6,
2018.
O’Shea, A. “Best Robo-Advisors: 2016 Top Picks.” NerdWallet, March 14,
2016. O’Shea, A. “Betterment Review 2017.” NerdWallet, January 31, 2017.
Perez, S. “Voice-Enabled Smart Speakers to Reach 55% of U.S.
Households by 2022, Says Report.” Tech Crunch, November 8, 2017. Pohjanpalo, K. “Investment Bankers Are Hard to Replace with Robots,
Nordea Says.” Bloomberg, November 27, 2017.
Popper, B. “How Netflix Completely Revamped Recommendations for
Its New Global Audience.” The Verge, February 17, 2016.
theverge.com/2016/2/17/11030200/netflixnew-
recommendation-system-global-regional (accessed April 2018). Quoc, M. “10 Ecommerce Brands Succeeding with Chatbots.” A Better
Lemonade Stand, October 23, 2017.
Radu, M. “How to Pay Less for Advertising? Use Baro—An Ad Robot
for Campaigns Optimization.” 150sec.com, August 18, 2016. Rayome, A. “How Sephora Is Leveraging AR and AI to Transform Retail
and Help Customers Buy Cosmetics.” TechRepublic, February, 2018, “If
This Model Is Right.” Bloomberg Business, June 18, 2015. Reisinger, D. “10 Reasons to Buy the Amazon Echo Virtual Personal
Assistant.” Slide Show. eWeek, February 9, 2016. Schlicht, M. “The
Complete Beginner’s Guide to Chatbots.” Chatbots Magazine, April 20,
124 Part IV • Robotics, Social Networks, AI and IoT
2016. chatbotsmagazine. com/the-complete-beginner-s-guide-to-
chatbots8280b7b906ca (accessed April 2018). StartUp. “How Netflix Uses Big Data.” Medium.com, January 12, 2018.
medium.com/swlh/how-netflix-uses-bigdata-20b5419c1edf
(accessed April 2018). Sun, Y. “Alibaba’s AI Fashion Consultant Helps Achieve Record-Setting
Sales.” MITTechnology Review, November 13, 2017.
TalKing. “Top Useful Chatbots for Health.” Chatbots Magazine, February
7, 2017. Taylor, S. “Very Human Lessons from Three Brands That Use Chatbots
to Talk to Customers.” Fast Company, October 21, 2016.
fastcompany.com/3064845/human-lessonsfrom-brands-using-
chatbots (accessed April 2018). Ulanoff, L. “Mark Zuckerberg’s AI Is
Already Making Him Toast.” Mashable, July 22, 2016.
C H A P T E R
13
The Internet of Things as a
Platform for Intelligent
Applications
LEARNING OBJECTIVES
■■ Describe the IoT and its characteristics ■■ Describe smart appliances and homes ■■ Discuss the benefits and drivers of IoT ■■ Understand the concept of smart cities, their
■■ Understand how IoT works content, and their benefits
■■ Describe sensors and explain their role in IoT ■■ Describe the landscape of autonomous vehicles applications ■■ Discuss
the major issues of IoT implementation
■■ Describe typical IoT applications in a diversity of fields
he Internet of Things (IoT) has been in the technology spotlight since 2014. Its applications
are emerging rapidly across many fields in industry, services, government, and the military
(Manyika et al., 2015). It is estimated that 20 to 50 billion “things” will be connected to the
Internet by 2020–2025. The IoT connects large numbers of smart things and collects data
that are processed by analytics and other intelligent systems. The technology is frequently combined
with artificial intelligence (AI) tools for creating smart applications, notably autonomous cars, smart
homes, and smart cities.
13.1 Opening Vignette: CNH Industrial Uses the Internet of Things to Excel 688
13.2 Essentials of IoT 689
13.3 Major Benefits and Drivers of IoT 694
13.4 How IoT Works 696
13.5 Sensors and Their Role in IoT 697
13.6 Selected IoT Applications 701
13.7 Smart Homes and Appliances 703
13.8 Smart Cities and Factories 707
T
126 Part IV • Robotics, Social Networks, AI and IoT
13.9 Autonomous (Self-driving) Vehicles 714
13.10 Implementing IoT and Managerial Considerations 717
687
13.1 OPENING VIGNETTE: CNH Industrial Uses the Internet of Things to Excel CNH Industrial N.V. (CNH) is a Netherlands-based global manufacturer of vehicles for agriculture, construction, and
commercial markets. The company produces and services more than 300 types of vehicles and operates in 190 countries where
it employs over 65,000 people. The company’s business is continuously growing while operating in a very competitive
environment.
THE PROBLEM
To manage and coordinate such a complex business from its corporate office in London, the company needed a superb
communication system as well as effective analytical capabilities and a customer service network. For example, the availability
of repair parts is critical. Customers’ equipment does not work until a broken part is replaced. Competitive pressures are very
strong, especially in the agriculture sector where weather conditions, seasonality, and harvesting pressure may complicate
operations. Monitoring and controlling equipment properly is an important competitive factor. Predicting equipment failures
is very desirable. Rapid connectivity with customers and the equipment they purchase from CNH is essential as are efficient
data monitoring and data collection. Both CNH and its customers need to make continuous decisions for which real-time
flow of information and communication is essential.
THE SOLUTION
Using PTC Transformational Inc. as an IoT, vendor, CNH implemented an IoT-based system with internal structural
transformation in order to solve its problems and reshape its connected industrial vehicles. The initial implementation was in
the agricultural sector. The details of the implementation are provided by PTC, Inc. (2015). The highlights of this IoT are
summarized next.
• Connects all vehicles (those that are equipped with sensors and are connected to the system) in hundreds of locations
worldwide to CNH’s command and control center. This connection enables monitoring performance.
• Monitors the products’ condition and operation as well as their surrounding envi-ronments through sensors. It also
collects external data, such as weather conditions.
• Enables customization of products’ performance at customers’ sites.
• Provides the data necessary for optimizing the equipment’s operation.
• Analyzes the performance of the people who drive CNH’s manufactured vehicles and recommends changes that can
improve the vehicles’ efficiency.
• Predicts the range of the fuel supply in the vehicles.
• Alerts owners to the needs (and timing) of preventive maintenance (e.g., by moni-toring usage and/or predicting
failures) and orders the necessary parts for such service. This enables proactive and preventive maintenance practices.
• Finds when trucks are overloaded (too much weight), violating CNH’s warranty.
• Provides fast diagnosis of products’ failures.
• Enables the delivery of trucks on schedule by connecting them to planners and with delivery sources and destinations.
• Helps farmers to optimally plan the entire farming cycle from preparing the soil to harvesting (by analyzing the weather
conditions).
• Analyzes collected data and compares them to standards.
All of this is done mostly wirelessly.
THE RESULTS
According to Marcus (2015), CNH halved the downtime of its participating equipment at customer sites by using the IoT. Parts
for incoming orders can be shipped very quickly. IoT use also helped farmers monitor their fields and equipment to improve
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 127
efficiency. The company is now showing customers less effective examples of operations and superb operating practices. In
addition, product development benefits from the analysis of collected data.
Sources: Compiled from PTC, Inc. (2015), Marcus (2015), and cnhindustrial.com/en-us/pages/homepage.aspx.
u QUESTIONS FOR THE OPENING VIGNETTE
1. Why is the IoT the only viable solution to CNH’s problems?
2. List and discuss the major benefits of IoT.
3. How can CNH’s product development benefit from the collected data about usage?
4. It is said that the IoT enables telematics and connected vehicles. Explain.
5. Why is IoT considered the “core of the future business strategy”?
6. It is said that the IoT will enable new services for CNH (e.g., for sales and collaboration with partners). Elaborate.
7. View Figure 13.1 (The process of IoT) and relate it to the use of IoT at CNH.
8. Identify decision support possibilities.
9. Which decisions made by the company and its customers are supported by IoT?
WHAT WE CAN LEARN FROM THIS VIGNETTE
First, we learned how IoT provides an infrastructure for new types of applications that connect thousands of items to a
decision-making center.
Second, we learned about the flow of data collected by sensors from vehicles and the environment around them and their
transmittal for analytical processing.
Third, the manufacturer of the vehicles and their owners and users can enjoy tremendous benefits from using the system.
Finally, this, IoT provides an efficient communication and collaboration framework for decision makers, the
manufacturer’s organization, and the users of the purchased equipment.
In this chapter, we elaborate on the technologies involved and the process of the IoT operation. We also describe its
major application in enterprises, homes, smart cities, and autonomous (smart) vehicles.
13.2 ESSENTIALS OF IoT
The Internet of Things (IoT) is an evolving term with several definitions. In general, IoT refers to a computerized network
that connects many objects (people, animals, devices, sensors, buildings, items) each with an embedded microprocessor. The
objects are connected, mostly wirelessly, to the Internet forming the IoT. The IoT can exchange data and allow communication
among the objects and with their environments. That is, the IoT allows people and things to be interconnected anytime and
anyplace. Embedded s ensors that collect and exchange data make up a major portion of the objects and the IoT. That is, IoT
uses ubiquitous computing. Analysts predict that by the year 2025, more than 50 billion devices (objects) will be connected to the
Internet, creating the backbone of IoT applications. The challenges and opportunities of this disruptive technology (e.g., for
cutting costs, creating new business models, improving quality) are discussed in an interview with Peter Utzschneider, vice
president of product management for Java at Oracle (see Kvitka, 2014). In addition, you can join the conversations at
iotcommunity.com.
For Intel’s vision of a fully connected world, see Murray (2016).
Embedding computers and other devices that can be switched on and off into active items anywhere and connecting all
devices to the Internet (and/or to each other) permit extensive communication and collaboration between users and items.
By connecting many devices that can talk to each other, one can create applications with new functionalities, increase the
productivity of existing systems, and drive the benefits discussed later. This kind of interaction opens the door to many
applications. For business applications of the Internet of Things, see Jamthe (2016). In addition, check the “Internet of Things
Consortium” (iofthings.org) and its annual conferences. For an infographic and a guide, see
intel.com/content/www/us/en/internet-of-things/infographics/ guide-to-iot.html.
128 Part IV • Robotics, Social Networks, AI and IoT
Definitions and Characteristics
There are several definitions of IoT.
Kevin Ashton, who is credited with the term the “Internet of Things,” provided the following definition: “The Internet
of Things means sensors connected to the Internet and behaving in an Internet-like way by making open, ad hoc connections,
sharing data freely, and allowing unexpected applications, so computers can understand the world around them and become
humanity’s nervous system” (term delivered first in a 1999 oral presentation. See Ashton, 2015).
Our working definition is:
The IoT is a network of connected computing devices including different types of objects (e.g., digital machines). Each
object in the network has a unique identifier (UID), and it is capable of collecting and transferring data automatically across
the network.
The collected data has no value until it is analyzed, as illustrated in the opening vignette.
Note that the IoT allows people and things to interact and communicate at any time, any place, regarding any business
topic or service.
According to Miller (2015), the IoT is a connected network in which:
• Large numbers of objects (things) can be connected.
• Each thing has a unique definition (IP address).
• Each thing has the ability to receive, send, and store data automatically.
• Each thing is delivered mostly over the wireless Internet.
• Each thing is built upon machine-to-machine (M2M) communication.
Note that, in contrast with the regular Internet that connects people to each other using computing technology, the IoT
connects “things” (physical devices and people) to each other and to sensors that collect data. In Section 13.4, we explain the
process of IoT.
SIMPLE EXAMPLES A common example of the IoT is the autonomous vehicle (Section 13.9). To drive on its own, a vehicle
needs to have enough sensors that automatically monitor the situation around the car and take appropriate actions whenever
necessary to adjust any setting, including the car’s speed, direction, and so on. Another example that illustrates the IoT
phenomenon is the company Smartbin. It has developed trash containers that include sensors to detect their fill levels. The
trash collection company is automatically notified to empty a trash container when the sensor detects that the bin has reached
the fill level.
A common example people give to illustrate IoT is the idea that a refrigerator could automatically order food (e.g., milk)
when it detects that the food has run out! Clorox introduced a new Brita filter so that a Wi-Fi–enabled mechanism can order
water filters by itself when it detects that it is time to change them. In these examples, a human does not have to communicate
with another human or even with a machine.
IoT IS CHANGING EVERYTHING According to McCafferty (2015), the IoT is changing e verything. This has been verified by a 2016
survey reported by Burt (2016). For how manufacturing is revolutionized by IoT, see Greengard (2016). Here are a few
examples that he provided:
• “Real-time systems make it possible to know where anyone is at any moment, which is helpful to secured locations as
military bases and seeking to push promotions to consumers.”
• “Fleet tracking systems allow logistics and transport firms to optimize routing, track vehicle speeds and locations, and
analyze driver and route efficiencies.”
• “Owners and operators of jet engines, trains, factory equipment, bridges, tunnels, etc., can stay ahead of repairs through
machines that monitor for preventive maintenance.” (opening case)
• “Manufacturers of foods, pharmaceuticals and other products monitor temperature, humidity and other variables to
manage quality control, receiving instant alerts when something goes wrong.”
These changes are facilitated by AI systems, which enhance analytics and automate or support decision making.
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 129
The IoT Ecosystem
When billions of things are connected to the Internet with all the supporting services and connected IT infrastructure, we can
see a giant complex, which can be viewed as a huge ecosystem. The Internet of Things ecosystem refers to all components
that enable users to create IoT applications. These components include gateways, analytics, AI algorithms, servers, data storage,
security, and connectivity devices. A pictorial view is provided in Figure 13.1 in which applications are shown on the left side
and the building blocks and platforms on the right side. An example of an IoT application is provided in the opening vignette.
It illustrates a network of sensors that collects information, which is transmitted to a central place for processing and eventually
for decision support. Thus, the IoT applications are subsets of the IoT ecosystem.
A basic discussion, terms, major companies, and platforms is provided by Meola (2018).
Structure of IoT Systems
Things in IoT refers to a variety of objects and devices ranging from cars and home appliances to medical devices, computers,
fitness tracers, hardware, software, data, sensors, and much more. Connecting things and allowing them to communicate is a
necessary capability of an IoT application; but for more sophisticated applications, we need additional components: a control
system and a business model. The IoT enables the things to sense or be sensed wirelessly across the network. A non-Internet
example is a temperature control system in a room. Another non-Internet example is a traffic signal at intersections of roads
where camera sensors recognize the cars coming from each direction and a control system adjusts the time for changing the
lights according to programmed rules. Later, we will introduce the reader to many Internetbased applications.
130 Part IV • Robotics, Social Networks, AI and IoT
IoT TECHNOLOGY INFRASTRUCTURE From a bird’s-eye view, IoT technology can be divided into four major blocks. Figure 13.2
illustrates them.
1. Hardware: This includes the physical devices, sensors, and actuators where data are produced and recorded. The devices
are the equipment that needs to be controlled, monitored, or tracked. IoT sensor devices could contain a processor or
any computing device that parses incoming data.
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 131
2. Connectivity: There should be a base station or hub that collects data from the sensor-laden objects and sends those
data to the “cloud” to be analyzed. Devices are connected to a network to communicate with other networks or other
applications. These may be directly connected to the Internet. A gateway enables devices that are not directly connected
to the Internet to reach the cloud platform.
3. Software backend: In this layer, the data collected are managed. Software backend manages connected networks and
devices and provides data integration. This may very well be in the cloud.
4. Applications: In this part of IoT, data are turned into meaningful information. Many of the applications can run on
smartphones, tablets, and PCs and do something useful with the data. Other applications can run on the server and
provide results or alerts through dashboards or messages to the stakeholders.
To assist with the construction of IoT systems, one may use IoT platforms. For information, see Meola (2018).
IoT PLATFORMS Because IoT is still evolving, many domain-specific and applicationspecific technology platforms are also
evolving. Not surprisingly, many of the major vendors of IoT platforms are the same ones who provide analytics and data
storage services for other application domains. These include Amazon AWS IoT, Microsoft Azure IoT suite, Predix IoT
Platform by General Electric (GE), and IBM Watson IoT platform (ibm.com/us-en/marketplace/internet-of-things-
cloud). Teradata Unified Data Architecture has similarly been applied by many customers in the IoT domain.
132 Part IV • Robotics, Social Networks, AI and IoT
u SECTION 13.2 REVIEW QUESTIONS
1. What is IoT?
2. List the major characteristics of IoT.
3. Why is IoT important?
4. List some changes introduced by IoT.
5. What is the IoT ecosystem?
6. What are the major components of an IoT technology?
13.3 MAJOR BENEFITS AND DRIVERS OF IoT
The major objective of IoT systems is to improve productivity, quality, speed, and the quality of life. There are potentially
several major benefits from IoT, especially when combined with AI, as illustrated in the opening case. For a discussion and
examples, see Jamthe, 2015.
Major Benefits of IoT
The following are the major benefits of IoT:
• Reduces cost by automating processes.
• Improves workers’ productivity.
• Creates new revenue streams.
• Optimizes asset utilization (e.g., see the opening vignette).
• Improves sustainability.
• Changes and improves everything.
• May anticipate our needs (predictions).
• Enables insights into broad environments (sensors collect data).
• Enables smarter decisions/purchases.
• Provides increased accuracy of predictions.
• Identifies problems quickly (even before they occur).
• Provides instant information generation and dissemination.
• Offers quick and inexpensive tracking of activities.
• Makes business processes more efficient.
• Enables communication between consumers and financial institutions.
• Facilitates growth strategy.
• Fundamentally improves the use of analytics (see the opening vignette).
• Enables better decision making based on real-time information.
• Expedites problem resolution and malfunction recovery.
• Supports facility integration.
• Provides better knowledge about customers for personalized services and marketing.
Major Drivers of IoT
The following are the major drivers of IoT:
• The number of “things”—20 to 50 billion—may be connected to the Internet by 2020–2025.
• Connected autonomous “things”/systems (e.g., robots, cars) create new IoT applications.
• Broadband Internet is more widely available, increasing with time.
• The cost of devices and sensors is continuously declining.
• The cost of connecting the devices is decreasing.
• Additional devices are created (via innovations) and are interconnected easily (e.g., see Fenwick, 2016).
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 133
• More sensors are built into devices.
• Smartphones’ penetration is skyrocketing.
• The availability of wearable devices is increasing.
• The speed of moving data is increasing to 60 THz.
• Protocols are developing for IoT (e.g., WiGig).
• Customer expectations are rising; innovative customer services are becoming a necessity.
• The availability of IoT tools and platforms is increasing.
• The availability of powerful analytics that are used with IoT is increasing.
Opportunities
The benefits and drivers just listed create many opportunities for organizations to excel in the economy (e.g., Sinclair, 2017),
in many industries and in different settings.
McKinsey Global Institute (Manyika et al., 2015) provides a comprehensive list of settings where IoT is or can be used
with examples in each setting. A 2017 study (Staff, 2017) revealed a dramatic increase in the capabilities and benefits of IoT.
HOW BIG CAN AN IoT NETWORK BE? While there will be billions of things connected to the Internet soon, not all of them will be
connected in one IoT network. However, an IoT network can be very large, as we show next.
Example: World’s Largest IoT Is Being Built in India (2017)
This network is being constructed by Tata Communications of India and HP Enterprises (HPE) of the United States, over
the HPE Universal IoT Platform. The things to be connected exist in 2,000 communities and include computing devices,
applications, and IoT solutions, connected over the Lo Ra network, a wireless communication protocol for wide area networks.
The things are in smart buildings, utilities, university campuses, security systems, vehicles and fleets, and healthcare facilities.
The project is to be implemented in phases with proof-of-concept applications to be tested first. The network will bring
services to 400 million people. For details, see Shah (2017).
u SECTION 13.3 REVIEW QUESTIONS
1. List the benefits of IoT for enterprises.
2. List the benefits of IoT for consumers.
3. List the benefits of IoT for decision making.
4. List the major drivers of IoT.
13.4 HOW IoT WORKS
IoT is not an application. It is an infrastructure, platform, or framework that is used to support applications. The following is
a comprehensive process for IoT applications. In many cases, IoT follows only portions of this process.
The process is explained in Figure 13.3. The Internet ecosystem (top of the figure) includes a large number of things.
Sensors and other devices collect information from the ecosystem. The collected information can be displayed, stored, and
processed analytically (e.g., by data mining). This analysis converts the information into knowledge and/or intelligence. Expert
systems or machine learning may help in turning the knowledge into decision support (made by people and/or machines), which is
evidenced by improved actions and results.
The generated decisions can help in creating innovative applications, new business models, and improvements in
business processes These result in “actions,” which may impact the original scenario or other things. The opening vignette
illustrates this process.
Note that most of the existing applications are in the upper part of the figure, which is called sensor to insight, meaning up
to the creation of knowledge or to the delivery of new information. However, now, the focus is moving to the entire cycle
(i.e., sensor to action).
The IoT may generate a huge amount of data (Big Data) that needs to be analyzed by various business intelligence
methods, including deep learning, or advanced AI methods.
134 Part IV • Robotics, Social Networks, AI and IoT
IoT and Decision Support
As stated earlier, the IoT creates knowledge and/or intelligence, which is submitted as support to decision makers or is inputted
to automated decision support entities. The transition from data collection to decision support may not be simple due to the
large amount of data, some of which are irrelevant. Large-scale IoT usually needs to filter the
collected data and “clean” them before they can be used for decision support, particularly if they are used as a base for
automated decision making.
u SECTION 13.4 REVIEW QUESTIONS
1. Describe the major components of IoT.
2. Explain how the IoT works following the process illustrated in Figure 13.3.
3. How does IoT support decision making?
13.5 SENSORS AND THEIR ROLE IN IoT
As illustrated in the opening vignette to this chapter, sensors play a major role in IoT by collecting data about the
performance of the things that are connected to the Internet and monitoring the surrounding environment,
collecting data there too if n ecessary. Sensors can transmit data and sometimes even process it prior to transmission.
Brief Introduction to Sensor Technology
A sensor is an electronic device that automatically collects data about events or changes in its environment. Many IoT
applications include sensors (see the opening vignette). The collected data are sent to other electronic devices for processing.
There are several types of sensors and several methods for collecting data. Sensors often generate signals that are converted
to human-readable displays. In addition to their use in IoT, sensors are essential components in robotics and autonomous
vehicles. Each sensor usually has a limit on the maximum distance that it can detect (nominal range). Sensors of a very short
range known as proximity sensors are more reliable than those that operate in larger ranges. Each IoT network may have millions
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 135
of sensors. Let us see how sensors work with IoT in Application Case 13.1. Application Case 13.1 Using Sensors, IoT, and AI for Environmental Control at the Athens, Greece, International Airport
The Problem
Over 20 million passengers use the airport annually, and their
number increases by more than 10 percent every year.
Obviously, the number of flights is large and also increasing
annually. The growth increases air pollution as well. The airport
has a strong commitment to environmental protection, so
management has looked for an environmental control solution.
The objective was to make the airport carbon neutral. The large
number of planes in the air and on the ground and the fact that
airplanes frequently move require advanced technologies for
the solution.
The Solution
A reasonable way to deal with moving airplanes was to use IoT,
a technology that when combined with AI-based sensors
enables environmental monitoring, analysis, and reporting, all
of which provide the background information for decisions
regarding minimizing the air pollution.
Two companies combined their expertise for this
project: EXM of Greece, which specializes in IoT prediction
analytics and innovative IoT solutions, and Libelium of the
United States, which specializes in AI-related sensors, including
those for environmental use. The objective of the project was
to properly monitor air quality inside and outside the airport
and to identify, in real time, the aircraft location on the ground
and to take corrective actions whenever needed.
Ad Hoc Air Quality Monitoring and Analysis
The airport now has an air quality monitoring network. The
solution includes Libelium’s sensor
(Continued)
136 Part IV • Robotics, Social Networks, AI and IoT
Rockwell Automation is one of the world’s largest providers of
industrial automation and information solutions. It has
customers in more than 80 countries worldwide and around
22,500 employees. One of its business areas of focus is assisting
oil and gas companies in exploration. An example is Hilcorp
Energy, a customer company that drills oil in Alaska. The
equipment used in drilling, extracting, and refining oil is very
expensive. A single fault in the equipment can cost the
company around $100,000 to $300,000 per day in lost
production. To deal with this problem, it needed technology to
monitor the status of such piece of equipment remotely and to
predict failures that are likely to happen in the future.
Rockwell Automation considered the opportunity to
expand its business in oil and gas industries by gathering data
from the exploration sites and analyzing them to improve
preventive maintenance decision making regarding the critical
equipment, thus, minimizing downtime and drive better
performance. The company utilizes its vision of Connected
Enterprise with Microsoft’s software
Application Case 13.1 (Continued)
platform connected in a cost-effective manner. The
different sensors measure temperature, humidity,
atmosphere pressure, ozone level, and particulate matter.
The readings of the sensors are transmitted to IoT for
reporting and then analysis. The sensors were improved by
using AI features. Therefore, their accuracy increased. In
addition, security and energy consumption are also being
controlled.
Aircraft Location at the Airport
To identify the exact location of the aircrafts during takeoff
and landing, the project uses acoustic measurement
mechanisms. This is accomplished by using noise sensors
placed in different locations. The sensors measure real-
time noise level, which is evaluated by analytics. Overall,
the system provides a noninvasive IoT solution.
Placement of sensors was difficult due to safety,
security, and regulation considerations. Therefore, the
sound monitoring subsystem had to be self-managed
(autonomous), bearing solar panels and batteries that
provided the electricity. In addition, the system utilizes a
dual wireless communication system (known as GPPS).
The collected noise data are correlated with types of
airplane and flights at the IoT backend. All data are
analyzed by the airport environmental department and
used for decisions regarding improvements of pollution
control.
Technology Support
The solution combines an IoT system with AI-based
analytics, visualization, and reporting and is executed in the
cloud. In addition, the system has onsite sensors and
communication infrastructures. Low-power wireless
sensors monitor water and gas consumption indoors as
well as air quality in the parking sites. Vendors’ products,
such as Microsoft Azure and IBM Bluemix, support the
project and provide the necessary flexibility.
Sources: Compiled from Hedge (2017) and Twentyman (2017).
Questions for Case 13.1
1. What is the role of IoT in the project?
2. What is the role of sensors?
3. What are the benefits of the project?
How Sensors Work with IoT
In large-scale applications, sensors collect data that are transferred to processing in the “cloud.”
Several platforms are used for this process as discussed in Application Case 13.2.
Application Case 13.2 Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets to Predict Failures
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 137
to monitor and support oil and gas equipment placed in
remote areas. Rockwell is now providing solutions to
predict failure of equipment along the entire petroleum
supply chain, monitoring its health and performance in real
time, and to prevent failures in the future. Solutions are
provided in the following areas.
• Drilling: Hilcorp Energy has its pumping equipment stationed in Alaska where it drills for oil
24 hours a day. A single failure in equipment can cost
Hilcorp a large amount of money. Rockwell
connected electrical variable drives of pumping
equipment to be processed in the “cloud,” to control
its machines thousands of miles away from the
control room in Ohio. Sensors capture data, and
through Rockwell’s control gateway, these data are
passed to Microsoft Azure Cloud. The solutions
derived reach Hilcorp engineers through digital
dashboards that provide real-time information about
pressure, temperature, flow rate, and dozens of other
parameters that help engineers monitor the
equipment’s health and performance. These
dashboards also display alerts about any possible
issues. When one of Hilcorp’s pieces of pumping
equipment failed, it was identified, tracked, and
repaired in less than an hour, saving six hours of
tracing the failure and the large cost of lost
production.
• Building smarter gas pumps: Today, some
delivery trucks use liquid natural gas (LNG) as fuel.
Oil companies are updating their filling stations to
incorporate LNG pumps. Rock-
well Automation installed sensors and variable
frequency drives at these pumps to collect realtime
data about equipment operations, fuel inventory,
and consumption rate. This data are transmitted to
Rockwell’s cloud platform for processing. Rockwell
then generates interactive dashboards and reports
using Microsoft Azure (an IoT platform). Results are
forwarded to the appropriate stakeholders, giving
them a good idea about the health of their capital
assets.
The Connected Enterprise solution by Rockwell has
accelerated growth for many oil and gas companies like
Hilcorp Energy by bringing their operations data to the
cloud platform and helping them reduce costly downtime
and maintenance. It has resulted in a new business
opportunity for industrial age stalwarts like Rockwell
Automation.
Sources: customers.microsoft.com (2015); Rockwell Automation: Fueling
the Oil and Gas Industry with IoT; https://customers.
microsoft.com/Pages/CustomerStory.aspx?recid=19922; Microsoft.com. (n.d.). “Customer Stories | Rockwell Automation,”
https://www.microsoft.com/en-us/cloud-platform/ customer-
stories-rockwell-automation (accessed April 2018).
Questions for Case 13.2
1. What type of information would likely be collected by an oil and gas drilling platform?
2. Does this application fit the three V’s (volume, variety,
velocity) of Big Data? Why or why not?
3. Which other industries (list five) could use similar
operational measurements and dashboards?
Sensor Applications and Radio-Frequency Identification (RFID) Sensors
There are many types of sensors. Some measure temperature; others measure humidity. Many
sensors collect information and transmit it as well. For a list of 50 sensor applications with a large
number of related articles, see libelium.com/resources/
top_50_iot_sensor_applications_ranking/.
A well-known type of sensor that plays an important role in IoT is radio-frequency
identification.
RFID SENSORS Radio-frequency identification (RFID) is part of a broader ecosystem of data
capture technologies. Several forms of RFID in conjunction with other sensors play a major role in
IoT applications. Let us see first what RFID is, as discussed in Technology Insights 13.1.
TECHNOLOGY INSIGHTS 13.1 RFID Sensors
RFID is a generic technology that refers to the use of radio-frequency waves to identify objects.
Fundamentally, RFID is one example of a family of automatic identification technologies that also includes
138 Part IV • Robotics, Social Networks, AI and IoT
ubiquitous barcodes and magnetic strips. Since the mid-1970s, the retail supply chain (among many other
areas) has used barcodes as the primary form of automatic identification. RFIDs can store a much larger
amount of data than barcodes. Also, they can be accessed from a longer distance wirelessly. These potential
advantages of RFID have prompted many companies (led by large retailers such as Walmart and Target) to
aggressively pursue it as a way to improve their supply chains and thus reduce costs and increase sales. For
details, see Chapter 8 in Sharda et al. (2018).
How does an RFID work? In its simplest form, an RFID system consists of a tag (attached to the
product to be identified), an interrogator (i.e., RFID reader), one or several antennae attached to the reader,
and a computer program (to control the reader and capture the data). At present, the retail supply chain has
primarily been interested in using passive RFID tags. Passive tags receive energy from the electromagnetic
field created by the interrogator (e.g., a reader) and backscatter information only when it is requested. The
passive tag remains energized only while it is within the interrogator’s magnetic field. In contrast, active tags have a battery to energize themselves. Because active tags have their own power
source, they do not need a reader to energize them; instead, they can initiate the data transmission process
on their own. As compared to passive tags, active tags have a longer read range, better accuracy, more
complex rewritable information storage, and richer processing capabilities. On the negative side, their
batteries cause active tags to have a limited life span, be larger in size than passive tags, and be more
expensive. Currently, most retail applications are designed and operated with passive tags, each of which
costs only a few cents. Active tags are most frequently found in defense and military systems, yet they also
appear in technologies such as EZ Pass whose tags (called transponders) are linked to a prepaid account that,
for example, enables drivers to pay tolls later, by driving past a reader rather than stopping to pay at a
tollbooth.
Note: There are also semipassive tags with limited active tag capabilities.
The most commonly used data representation for RFID technology is the Electronic Product Code
(EPC), which is viewed by many in the industry as the next generation of the Universal Product Code
(UPC), most often represented by a barcode. Like the UPC, the EPC consists of a series of numbers that
identifies product types and manufacturers across the supply chain. The EPC also includes an extra set of
digits to uniquely identify items.
Use of RFID and Smart Sensors in IoT
Basic RFID tags, either active or passive, are not sensors. The purpose of the tags is to identify objects and
determine their location (e.g., for the purpose of counting objects). To make them useful for most IoT
applications, the tags need to be upgraded (e.g., by adding on-board sensors). These RFIDs called RFID
sensors have more capabilities than RFID tags, or basic sensors. For a detailed discussion about the role of
RFID in the IoT, see Donaldson (2017). RFID sensors are wireless sensors that communicate, via mash networks or conventional RFID
readers, and they include identifiable ID. The RFID reader sends token information into gateways, such as
AWS IoT service. This confirmation can be processed, resulting in some action.
SMART SENSORS AND IoT There are several types of smart sensors with different levels of capabilities
when integrated into IoT. A smart sensor is one that senses the environment and processes the
input it collects by using its built-in computing capabilities (e.g., a microprocessing). The processing
is preprogrammed. Results are passed on. Depending on the internal computing quality, smart
sensors can be more automated and accurate than other sensors and can filter out unwanted noise
and compensate for errors before sending the data.
Smart sensors are crucial and an integral element in the IoT. They can include special
components, such as amplifiers, analog filters, and transducers, to support IoT.
In addition, smart sensors for IoT can include special software for data conversion, digital
processing, and communication capability to external devices.
According to a major study (Burkacky et al., 2018), sensors are getting smarter. Those on
vehicles are examples. Vehicles can make the transition from being a hardwaredriven machine to
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 139
being a software-driven electronic device. Software can cost over 35 percent of the cost of vehicle
production.
For further information, see Scannell (2017), Gemelli (2017), and Technavio (2017).
u SECTION 13.5 REVIEW QUESTIONS
1. Define sensor.
2. Describe the role of sensors in IoT.
3. What is RFID? What is a RFID sensor?
4. What role does the RFID perform in IoT?
5. Define smart sensor and describe its role in IoT.
13.6 SELECTED IoT APPLICATIONS
We start with a well-known example: Imagine that your refrigerator can sense the amount of food
in it and send you a text message when inventory is low (sensor-to-insight in Figure 13.3). One day
refrigerators will also be able to place an order for items that need replenishment, pay for them, and
arrange delivery (sensor-to-action). Let us look at some other, less futuristic enterprise applications.
A Large-scale IoT in Action
Existing contribution of IoT has centered on large organizations.
Example French National Railway System’s Use of IoT
SNCF, the French national railway system, uses IoT to provide quality, availability, and safety for
its nearly 14 million passengers. The company sncf.com improved its operations using IoT
(Estopace, 2017a). To manage 15,000 trains and 30,000 kilometers of tracks is not simple, but IBM
Watson, using IoT and analytics, helped to do just that. Thousands of sensors that are installed on
the trains, tracks, and train stations gather data that Watson processes. In addition, all business
process operations were digitized to fit into the system. Information concerning possible
cyberattacks was also programmed into the system. All collected Big Data were prepared for
decision support. IBM Watson’s platform is scaleable and can handle future expansions.
To understand the magnitude of this IoT network, consider that the mass transit lines in Paris
alone required 2,000 sensors forwarding information from more than 7,000 data points each month.
The systems enable engineers to remotely monitor 200 trains at a time for any mechanical and
electrical operations and malfunctions while trains are moving. In addition, by using a predictive
analytic model, the company can schedule preventive maintenance to minimize failures. Therefore,
if you are one of the train travelers, you can relax and enjoy your trip.
Examples of Other Existing Applications
The following examples of the use of IoT applications are based on information from Koufopoulos
(2015):
• Hilton Hotel. Guests can check in directly to their rooms with their smartphones (no check-
in lobby is needed, no keys are used). Other hotel chains follow suit.
• Ford. Users can connect to apps by voice. Autopaying for gas and preordering drinks at
Starbucks directly from Ford’s cars are in development.
• Tesla. Tesla’s software autonomously schedules a valet to pick up a car and drive it to Tesla’s
facility when a car needs repair or schedule service. Tesla trucks, managed by IoT, will be
driverless one day.
140 Part IV • Robotics, Social Networks, AI and IoT
• Johnnie Walker. The whiskey company connected 100,000 of its bottles to the Internet for
Brazil’s Father’s Day. Using smart labeling, buyers can create personalized videos to share
with their fathers on social networks. Fathers also get promotions to buy more whiskey if
they like it.
• Apple. Apple enables users of iPhones, Apple Watches, and Home kits to streamline
shopping with Apple Pay.
• Starbucks Clover Net in the Cloud. This system connects coffee brewers to customers’
preferences. It also monitors employee’s performance, improves recipes, tracks consumption
patterns, and so on.
A large number of consumer applications of IoT is reported by Jamthe (2016) and Miller
(2015). For a list of IoT applications related to IBM Watson, see ibm.com/ internet-of-things/.
Many companies are experimenting with IoT products for r etailing (business to consumer,
or B2C) and business to business (B2B) in areas such as o perations, t ransportation, logistics, and
factory warehousing. For the approaches of Apple and Amazon, see appadvice.com/post/apple-
amazons-smart-home- race/736365/.
Note: For many case studies and examples of the IoT, see ptc.com/en/ product-lifecycle-report/servicesand-customer-
success-collide-in-the-iot, divante.co/blog/internet-e-commerce, and Greengard (2016). IoT is also used for many a
pplications inside enterprises (see McLellan, 2017a), and military purposes (see Bordo, 2016).
HOW IoT IS DRIVING MARKETING According to Durrios (2017), IoT can drive marketing opportunities
in the following four ways:
1. Disruptive data collection. IoT collects more data about customers from more data sources than
other technologies do. This includes data from wearables, smart homes, and everything
consumers do. In addition, IoT provides data about changes in consumer preferences and
behavior.
2. Real-time personalization. IoT can provide more accurate information about specific customers
buying decisions, for example. IoT can identify customer expectations and direct customers
to specific brands.
3. Environmental attribution. IoT can monitor environments regarding ad delivery for specific
places, customers, methods, and campaigns. IoT can facilitate research of business
environment; factors such competition, pricing, weather conditions, and new government
regulations are observed.
4. Complete conversation path. IoT initiatives expand and enrich the digital channel of conversations
between customers and vendors, especially those using wireless digital engagement. IoT also
provides insight on consumer purchasing paths. In addition, marketers will receive improved
customized market research data (e.g., by following the manner of customers’ engagement and
how customers react to promotions).
Of all the consumer-related IoT initiatives, three types are most well-known: smart homes
and appliances (Section 13.7), smart cities (Section 13.8), and autonomous vehicles (Section 13.9).
For more on IoT and customers, see Miller (2018).
u SECTION 13.6 REVIEW QUESTIONS
1. Describe several enterprise applications.
2. Describe several marketing and sales applications.
3. Describe several customer service applications.
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 141
13.7 SMART HOMES AND APPLIANCES
The concept of the smart home has been in the limelight for several years, even before the concept
of the IoT took a front stage.
A smart home is a home with automated components that are interconnected (frequently
wirelessly), such as appliances, security, lights, and entertainment, and are centrally controlled and
able to communicate with each other. For a description, see
techterms.com/definition/smart_home.
Smart homes are designed to provide their dwellers with comfort, security, low energy cost,
and convenience. They can communicate via smartphones or the Internet. The control can be in
real time or at any desired intervals. Most existing homes are not yet smart, but they can easily and
inexpensively be equipped to for at least partial smartness. Several protocols enable connections;
well-known ones are XIO, UPB, Z-Wave, and EnOcean. These products offer scalability, so more
devices can be connected to the smart home over time.
For an overview, see techterms.com/definition/smart_home,
smarthomeenergy.co.uk/what-smart-home, and Pitsker (2017).
In the United States followed by other countries, thousands of homes are already equipped
with such systems.
Typical Components of Smart Homes
The following are typical components in smart homes:
• Lighting. Users can manage their home lighting from wherever they are.
• TV. This is the most popular component.
• Energy management. Home heating and cooling systems can be fully automated and
controlled via a smart thermostat (e.g., see Nestnest.com/workswith-nest about its
product Nest Learning Thermostat).
• Water control. WaterCop (watercop.com) is a system that reduces water damage by
monitoring water leaks via a sensor. The system sends a signal to a valve, causing it to close.
• Smart speaker and chatbots (see Chapter 12). Most popular are Echo and Alexa, and
Google Assistant.
• Home entertainment. Audio and video equipment can be programmed to respond to a
remote control device. For instance, a Wi-Fi–based remote control for a stereo system
located in a family room can command the system to play on speakers installed anywhere
else in the house. All home automation devices perform from one remote site and one
button.
• Alarm clock. This tells kids to go back to sleep or to wake up.
• Vacuum cleaner. Examples are iRobot Roomba, and LG Roboking vacuum; see Chapter
2).
• Camera. This allows residents to see what is going on in their homes anytime from
anywhere. Nest Cam Indoor is a popular product. Some smart cameras can even know how
residents feel. See tomsguide.com/us/hubble-hugo-smarthome-camera,news-
24240.html.
• Refrigerator. An example of this is Instaview from LG, which is powered by Alexa.
• Home security and safety. Such systems can be programmed to alert owners to
security-related events on their property. As noted, some security can be supported by
cameras for remote viewing of property in real time. Sensors can be used at home to
detect intruders, keep an eye on working appliances, and perform several additional
activities.
The major components of smart homes are illustrated in Figure 13.4.
142 Part IV • Robotics, Social Networks, AI and IoT
Note that only a few homes have all of these components. Most common are home
security, entertainment, and energy management.
Example: iHealthHome
Security measures are common in assisted living facilities in senior communities and for seniors
who live independently. For example, the iHealthHome Touch screen system collects data and
communicates with caregivers using the company’s software. The system provides caregivers
and physicians remote access to a person’s health data. Using this technology, the iHealthHome
program also reminds seniors of daily appointments and when to take their medicine. The system
also reminds people when to self-measure their blood pressure and how to stay in touch with
their caregivers.
Smart Appliances
A smart appliance includes features that can remotely control the appliance operations, based
on the user preferences. A smart appliance may utilize a Home Network or the Internet to
communicate with other devices in the smart home.
FIGURE 13.4 The Components of a Smart Home. McGrath (2016) provides an overview of smart appliances that includes all appliances from
Haier (a large China-based manufacturer). Its goal is to make everything in a house communicate
across other device makers. Examples are smart refrigerators, air conditioners, and washing
machines. Haier offers a control board for all appliances regardless of their manufacturers. Apple is
working on a single control for all smart appliances in a home.
GOOGLE’S NEST A leading manufacturer of IoT smart home applications is Google’s Nest. The
company is a producer of programmable self-learning, sensordriven, Wi-Fi– enabled products. In
the spring of 2018, the company had three major products:
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 143
• Learning thermostat. This device learns what temperature and humidity level that people
like and controls the air conditioner/heating system accordingly. Google claims that its
products provide an average energy savings of 13 percent, which could pay for the device in
two years; see nest.com/thermostats/ nest-learning-thermostat/overview/?alt=3.
• Smoke detector and alarm. This device, which is controlled from a smartphone, tests itself
automatically and lasts for about a decade. For details, see nest.com/ smoke-co-
alarm/overview/.
• Nest.com. This Webcam-based system allows users to see what is going on in their homes
from any location via smartphone or any desktop computer. The system turns itself on
automatically when nobody is at home. It can monitor pets, babies, and so on. A photo
recorder allows users to go back in time. For details, see nest.com/cameras/nest-cam-
indoor/overview/. For how Nest can use a phone to find out when individuals leave home,
see Kastrenakes (2016). For more on Nest, see en.wikipedia.org/wiki/Nest_Labs.
Examples of Available Kits for Smart Homes
Two popular smart-home starter kits are (Pitsker, 2017):
1. Amazon Echo. This includes Amazon Echo, Belkin Wemo Mini, Philips Hue white starter
kit, Ecobee Lite, and Amazon Fire TV stick with Alexa voice remote. Total cost on October
2017 was $495.
2. Google Home. This includes Google Home, Smart Speaker, Belkin Wemo Mini, Philips Hue
white starter kit, Nest learning thermostat, and Google Chromecast (for entertainment). Total
cost on October 2017 was $520.
HOME APPLIANCES IN CONSUMER ELECTRONIC SHOW (CES) 2016–2018 The following smart appliances,
some of which were exhibited at the CES show in Las Vegas in January 2016 (Morris 2016), 2017,
and 2018, are:
• Samsung Smart fridge. Cameras check content; sensors check temperature and humidity.
• Gourmet robotic cooker. It does interesting cooking.
• 10 in 1 device for the kitchen. This stirs food such as scrambled eggs and has 10 cooking
styles (e.g., baking, sauce making).
• LG HUM-BOT Turbo+. This can focus on an area in the home that needs special attention.
A camera monitors the home remotely while the owner is away (similar to Google’s Nest).
• Haier R3D2 Refrigerator. According to Morris (2016), this refrigeration is not the most
practical one, but it has much of entertainment value. It looks like R3D2 in Star Wars. It can
serve you a drink as well as provide lights and sounds.
• Instaview Refrigerator from LG. Powered by Alexa (enabled by voice), this includes a 29-
inch LCD touch screen display. It provides functions such as determining the expiration dates
of food and notifying the user. For details, see Diaz (2017).
• Whirlpool’s smart top load washer. This fully automated machine has smart controls. It
saves energy and even encourages philanthropy by sending a small amount of money to
“Habitat for Humanity” each time washer is loaded.
• LG LDT8786ST dishwasher. This machine has camera whose sensors keep track of what
has already been cleaned in order to save water. In addition, it provides flexibility in
operations.
The following are smart home trends:
• TVs that can be used as a smart Hub for home appliances is coming from Samsung.
144 Part IV • Robotics, Social Networks, AI and IoT
• Dolby Atmos products include speakers, receivers, and other entertainment items.
• DIY home smart security cameras make sure there is an intruder, not just the cat, before
alerting the police.
• Water controls for faucets, sprinklers, and flood detectors are available. In addition, a robot
can teach users how to save water indoors (hydrao.com/us/en/).
For more about home automation, see smarthome.com/sh-learning-center-what-cani-
control.html. Various apps used for home control can be found at smarthome.com/
android_apps.html.
Smart components for the home are available at home improvement stores (e.g., Lowes) and
can be purchased directly from manufacturers (e.g., Nest).
To facilitate the creation of smart components for the home, Amazon and Intel Corp.
partnered in 2017 to provide developers with platforms to advance the smart home ecosystem. For
details, see pcmag.com/news/350055/amazon-intel-partner-to-advance- smart-home-
tech/.
For smart appliances at CES 2018, watch the video at youtube.com/watch?
v=NX-9LivJh0/.
A Smart Home Is Where the Bot Is
The virtual personal assistant that we introduced in Chapter 12 enables people to converse by voice
with chatbots such as Alexa/Echo and Google Assistant. Such assistants can be used to manage
appliances in smart homes.
In a comprehensive smart home, devices not only meet household needs but also are able to
anticipate them. It is predicted that in the near future, an AI-based smart home will feature an
intelligent and coordinated ecosystem of bots that will manage and perform household tasks and
may even be emotionally connected with people. For a prediction of the future bots, see Coumau
et al. (2017). Amazon and Intel joined forces to develop such smart home ecosystems that include
NLP capabilities.
Smart homes will also have smart robots that can serve people snacks, help take care of people
who are handicapped, and even teach children different skills.
Barriers to Smart Home Adoption
The potential of smart homes is attractive, but it will take some time before there will be many of
them. The following are some limiting barriers, per Vankatakrishnan (2017).
• Compatibility. There are too many products and vendors to choose from, making potential
buyers confused. Many of these products do not “speak” to each other, so more industry
standards are needed. In addition, it is difficult to match the products with consumers’ needs.
• Communication. Different consumers have different ideas on what the smart home should
be. Therefore, the capabilities and benefits of a smart home need to be clearly communicated
to users.
• Concentration. Brands need to concentrate on population segments that are most
interested in smart homes (e.g., Gen Y).
In addition are the issues of cost justification, invasion of privacy, security, and ease of use.
For the future of smart homes, including the role of Amazon and Walmart, and how the smart
home will shop for itself, see Weinreich (2018).
Smart homes, appliances, and buildings can be featured in smart cities, the subject of our next
section.
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 145
u SECTION 13.7 REVIEW QUESTIONS
1. Describe a smart home.
2. What are the benefits of a smart home?
3. List the major smart appliances.
4. Describe how Nest works.
5. Describe the role of bots in smart homes.
13.8 SMART CITIES AND FACTORIES
The idea of smart cities took off around 2007 when IBM launched its Smart Planet project and
Cisco began its Smart Cities and Communities program. The idea is that in smart cities, digital
technologies (mostly mobile based) facilitate better public services for citizens, better utilization of
resources, and less negative environmental impact. For resources, see ec.europa.eu/digital-
agenda/en/about-smart-cities. Townsend (2013) provides a broad historical look and coverage
of the technologies. In an overview of his book, he provides the following examples: “In Zaragoza,
Spain, a ‘citizen card’ can get you on the free city-wide Wi-Fi network, unlock a bike share, check a
book out of the library, and pay for your bus ride home. In New York, a guerrilla group of citizen-
scientists installed sensors in local sewers to alert you when storm water runoff overwhelms the
system, dumping waste into local waterways.” According to a prediction made by Editors (2015),
smart cities would use 1.6 billion connected things in 2016. Finally, smart cities can have several
smart entities such as universities and factories (see Lacey, 2016). For more on smart cities, see
Schwartz (2015). In addition, watch the video “Cisco Bets Big on ‘Smart Cities’” at money.cnn.
com/video/technology/2016/03/21/cisco-ceo-smart-cities.cnnmoney. Another video to
watch is “Smart Cities of the Future” (3:56 min.) at youtube.com/ watch?v=mQR8hxMP6SY.
A more detailed video on San Diego (44:06 minutes) is at youtube.com/watch?v=LAjznAJe5uQ.
Cities cannot become smart overnight, as illustrated in Application
Case 13.3, which presents the case of Amsterdam and its evolution into a
smart city.
In many countries, governments and others (e.g., Google) are
developing smart city applications. For example, India has begun to develop
100 smart cities (see enterpriseinnovation. net/article/india-eyes-
development-100-smart-cities-1301232910).
Application Case 13.3 Amsterdam on the Road to Become a Smart City In over seven years, the city of Amsterdam (The
Netherlands) was transformed into a smart city using
information technologies. This case describes the steps the
city took from 2009 to 2016 to become a smart city, as
reported by MIT Sloan School of Management. The city
initiative included projects in the following categories:
mobility, quality of living, transportation, security, health,
and economy as well as infrastructure, big and open source
data, and experimental living labs.
The major findings of the MIT team regarding
Amsterdam’s transformation were:
• Private-sector data are critical for changing
policy. The major categories of the project
involved nongovernmental entities (e.g., using a
GPS provider to manage traffic). For example,
the private sector was involved in a project to
change traffic situations (reduction of 25 percent
in the number of cars and an increase of 100
percent in the number of scooters, in five years).
• It is necessary to have chief technology
officers in smart cities. Smart cities require the
collection of large amounts of data using several
tools and algorithms. Issues such as cost and
security are critical.
146 Part IV • Robotics, Social Networks, AI and IoT
• Expectations of the contribution of the IoT,
Big Data, and AI, need to be managed.
Citizens expect rapid changes and improvement
in areas ranging from parking to traffic. Data
collection is slow, and changes are difficult to
implement.
• Smart city initiatives must start with data
inventory. The problem in Amsterdam was that
data were stored in 12,000 databases across 32
departments. These were organized differently
on different hardware, so data inventory was
needed. This initial activity was boring and
tedious and had no immediate visible payoff.
• Pilot projects are an excellent strategy. Pilot
projects provide lessons for future projects.
The city had over 80 pilot projects, for example,
collecting different types of trash and placing
them in different colored bags. Successful
projects are scaled up in size.
• Citizen input is a critical success factor.
There are several ways to encourage citizens to
provide input. Involvement of universities and
research institutions is also critical. In addition,
social media networks can be used to facilitate
citizens’ engagement.
The smart city initiative may be only in its beginning,
but it is already improving the quality of life of residents
and increasing the economic growth of the city. A critical
success factor of the initiative was the willingness of the
city officials to share their data with technology
companies.
IoT was a major component in the projects. First, it
enabled the flow of data from sensors and databases for
analytic processing. Second, IoT enables autonomous
vehicles of all kinds, which contribute to the reduction of
pollution, vehicle accidents, and traffic jams. Finally, IoT
provides real-time data that help decision makers develop
and improve policies. In April 2016, the city won Europe’s
“Capital of Innovation” award (a prize of 950,000 euros).
Sources: Compiled from Brokaw (2016), Fitzgerald (2016),
amsterdamsmartcity.com, and facebook.com/amsterdam
smartcity.
Questions for Case 13.3
1. Watch the video at youtube.com/watch?v=
FinLi65Xtik/ and comment on the technologies used.
2. Get a copy of the MIT case study at sloanreview.
mit.edu/case-study/data-driven-city-
management/. List the steps in the process and the
applications that were likely used in IoT.
3. Identify the smart components used in this project.
Smart Buildings: From Automated to Cognitive Buildings
IBM’S COGNITIVE BUILDINGS In a white paper (IBM, 2016), IBM discussed the use of IoT to make cognitive buildings,
which are able to learn the behavior of a building’s system in order to optimize it. The cognitive building does
so by autonomously integrating the IoT devices with the IoT operation. Such integration enables the creation
of new business processes and increases the productivity of existing systems. Based on the concept of cognitive
computing (Chapter 6), IBM describes the maturity of the technology as a continuation of the phase that started
with automated buildings (1980 to 2000), the creation of smart building (2000 to 2015), and finally, cognitive building
(beginning in 2015). The process is illustrated in Figure 13.5. The figure also shows the increased capabilities of
buildings over time. The highlights of a cognitive building are:
• By applying advance analytics, buildings can provide insights in near real time.
• It learns and reasons from data and interacts with humans. The system can detect and diagnose abnormal
situations and propose remedies.
• It has the ability to change building temperature subject to humans’ preferences.
• It is aware of its status and that of its users.
• It is aware of its energy status and adjusts it to be comfortable to dwellers.
• Its users can interact with the building via text messages and voice chatting.
• Robots and drones are starting to operate inside and outside the building without human intervention.
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 147
A major collaborator of IBM is Siemens (from Germany). The companies concentrate on global issues related to the
use of IoT to enhance building performance.
Smart Components in Smart Cities and Smart Factories
The major objective of smart cities is to automate as many as possible public services such as transportation,
utilities, social services, security, medical care, education, and economy. So, in the smart city overall project one
may find several subprojects, some of which are independent of the master project.
Example
Hong Kong has a project called a smart mobility for the improvement of road safety. A consortium of private and
public organizations has introduced Intelligent Transport
FIGURE 13.5 IBM’s Cognitive Building Maturity Framework. Source: IBM. “Embracing the Internet of Things in the new era of
cognitive buildings.” IBM Global Business Services, White Paper, 2016. Courtesy of International Business Machines Corporation, ©
International Business Machines Corporation.Used with permission.
Services, including a warning mechanism for collision, and control assistance for finding parking. The system also
manages speed and lane violations and traffic congestion. All of these increase safety and efficiency. For details, see
Estopace (2017b).
Transportation is a major area in which analytics and AI can make cities smarter. Other areas include economic
development, crime fighting, and healthcare. For details, see SAS (2017).
Other examples of smart city components can be found in a smart university, smart medical centers, smart
power grid, and in airports, factories, ports, sport arenas, and smart factories. Each of these components can be
treated as an independent IoT project, and/or as a part of the smart city overall project.
SMART (DIGITAL) FACTORIES Automation of manufacturing has been with us for generations. Robots are making
thousands of products from cars to cellphones. Tens of thousands of robots can be found in Amazon’s distribution
centers. Therefore, it is not surprising that factories are getting smarter with AI technologies and IoT applications.
As such they may be considered a component of smart cities and may be interrelated with other components, such
as clean air and transportation.
A smart factory, according to Deloitte University Press, is “a flexible system that can self-optimize
performance across a broader network, self-adapt to and learn from new conditions in real or near real time, and
autonomously run entire production processes.” For details, see the free Deloitte e-book at DUP_The-smart-
factory.pdf. For a primer, see https://www2.deloitte.com/insights/us/en/focus/internet-of-things/
technical-primer.html.
Tomás (2016) provides a vision of what industrial production will look like in the future. It will be essentially
fully digitized and connected, fast, and flexible. The major idea is that there will be a command center in a factory
148 Part IV • Robotics, Social Networks, AI and IoT
equipped with AI technologies. The AI, combined with IoT sensors and information flow, will enable optimal
organization and sequencing of business processes. The entire production chain, from raw material suppliers,
logistics, and manufacturing to sales, will be connected to IoT systems for planning, coordination, and control.
Planning will be based on analytic predictions of demand.
Production processes will be automated as much as possible and wirelessly controlled. Logistics will be
provided on demand quickly, and quality control will be automated. IoT combined with sensors will be used for
both predictive and preventive m aintenance. Some of these elements exist in advanced factories, and more factories
will be smarter in the future.
For more on smart factories, see Libelium (2015) and Pujari (2017). For the smart factory of the future, read
belden.com/blog/industrial-ethernet/topic/smart- factory-of-the-future/page/0.
The use of IoT in the factory is illustrated in the video “Smart Factory Towards a Factory of Things” at
youtube.com/watch?v=EUnnKAFcpuE (9:10 min.).
Smart factories will have different business processes, new technology solutions, different people-machine
interactions, and a modified culture. For the transformation process to a smart factory, see Bhapkar and Dias (2017).
The accounting firm Deloitte (dupress.deloitte.com/smart-factory) provides a diagram that illustrates “the major
characteristics of a smart factory” (Figure 13.6).
Example: Smart Bike Production in a Smart Factory
The world demand for smart bikes is increasing rapidly, especially in smart cities. Mobike is the world’s first and
largest bike-sharing company. To meet the demand, the company is working with Foxconn Technology Group to
make the bike production smarter. The smart manufacturing involves the creation of a global supply chain from raw
materials to production to sales. Foxconn is known for its high-technology expertise in
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 149
FIGURE 13.6 Five Key Characteristics of a Smart Factory (Deloitte). Source: Burke, Hartigan, Laaper, Martin, Mussomeli,
Sniderman, “The smart factory: Responsive, adaptive, connected manufacturing,” Deloitte Insights (2017),
https://www.deloitte.com/insights/us/en/focus/ industry-4-0/smart-factory-connected-manufacturing.html. Used with permission.
providing efficient manufacturing processes in a cost-efficiency production. It optimizes Internet-driven smart
manufacturing. The production output is expected to double in the near future. For details, see Hamblen (2016) and
enterpriseinnovation.net/article/ foxconn-drives-mobike-smart-bike-production-1513651539.
EXAMPLES OF SMART CITY INITIATIVES Smart city initiatives are diversified, as explained earlier. For examples, see
Application Case 13.4.
Application Case 13.4 How IBM Is Making Cities Smarter Worldwide IBM has been supporting smart city initiatives for several
years. The following examples are compiled from Taft’s
slide show (eweek.com/cloud/ how-ibm-is-making-
cities-smarter-worldwide).
• Minneapolis (United States). The initiative
supports more effective decisions for the city’s
resource allocation. In addition, it aligns the
operations of multiple departments w orking on
the same project. IBM is providing AI-based
pattern recognition algorithms for problem
solving and performance improvement.
• Montpellier (France). IBM’s software is helping
the city in its initiatives of water manage(Continued)
150 Part IV • Robotics, Social Networks, AI and IoT
Application Case 13.4 (Continued)
ment, mobility (transportation), and risk
management (decision making). The rapidly growing
city must meet the increasing demand for s ervices.
To do this efficiently, IBM provides data analysis
and interpretation of activities, research institutions,
and other partners in the region.
• Stockholm (Sweden). To reduce traffic problems, IBM technologies are optimally matching demands
and supplies. The initiative uses sensors and IoT to
alleviate the congestion problem.
• Dubuque (United States). Several initiatives were conducted for efficient use of resources (e.g.,
utilities) and management of transportation
problems.
• Cambridge (Canada). The city is using
IBM’s “Intelligent Infrastructure Planning” for
conducting business analytics and decision support
technologies. Using AI-based algorithms, the city
can make better decisions (e.g., repair or replace
assets). In addition, IBM smart technologies help to
improve project coordination.
• Lyon (France). Transportation management is a major project in any big city and a target for most
smart city initiatives. Smart technologies provide
transportation staff with effective real-time decision
support tools. This helped reduce traffic congestion.
Using predictive analytics, future problems can be
forecasted, so, if they occur, they can be solved
quickly.
• Rio de Janeiro (Brazil). To manage and coordinate the operations of 30 city departments is a complex
undertaking. IBM technologies support a central
command center for the city that plans operations
and handles emergencies in all areas.
• Madrid (Spain). To manage all its emergency situations (fire, police transportation, hospitals), the
city created a central response center. Data are
collected by sensors, GPS, surveillance cameras, and
so on. The center was created after Madrid’s 2004
terrorist attack and is managed with the support of
IBM smart technologies.
• Rochester (United States). The city p olice department is using IoT and predictive analysis to
forecast when and where crimes is likely to be
committed. This AI-based system has proven to be
accurate in several other cities.
These examples illustrate the utilization of IBM’s
Smarter Cities framework in several areas by smart city
initiatives. Note that IBM Watson is using IoT for many of
its own projects.
Questions for Case 13.4
1. List the various services that are improved by IoT in a smart city.
2. How do the technologies support decision making?
3. Comment on the global nature of the examples.
A major area of improvement in a smart city is transportation.
Improving Transportation in the Smart City
A major problem in many cities is the increased number of vehicles and the inability to accommodate all of them effectively.
Building more roads could add more pollution and lead to traffic jams. Public transportation can help alleviate the problem
but may take years to complete. Quick solutions are needed. In the opening case to Chapter 2, we introduced Inrix. The Inrix
company uses AI and other tools to solve transportation problems. It collects data from stationary sensors along roads and
from other sources. In some smart cities, innovators have already placed air quality sensors on bicycles and cars. Sensors also
are taking data from cars on the roads to help generate data that can analyzed and results are transmitted to drivers. An example
of another innovative project is provided in the following examples.
Example 1
Valerann, an Israeli start-up, developed smart road studs to replace the reflective studs of today’s technology. Smart studs can
transmit information of what they sense about what is occurring on the roads. Eventually, the studs will be incorporated with
autonomous vehicles. The smart studs cost more than reflective studs but have a longer life. For details, see Solomon (2017).
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 151
Example 2
Smart Mobility Consortium (Hong Kong) works on mobility in the smart city of Hong Kong. More than 10 million people
there use the public and private transportation systems every day. This transportation project includes several smart subsystems
for parking, collision warning, and alerts for speeders and lane changing violators. For details, see Estopace (2017b).
Combining Analytics and IoT in Smart City Initiatives
Like in many IoT initiatives, it is necessary to combine analytics and IoT. A notable example is IBM Watson. Another one is
the SAS platform.
Example: The SAS Analytics Model for Smart Cities
The amount of data collected by IoT networks in cities can be enormous. Data are collected from many sensors, computer
files, people, databases, and so on. To make sense of these data, it is necessary to use analytics, including AI algorithms. SAS
is using a seven-step process divided into three major phases: Sense, Understand, and Act. The following are definitions of these
(condensed from SAS, 2017).
• Sense. Using sensors, sense anything that matters. SAS analyzes the collected data. The data go through intelligent filters
for cleanliness so that only relevant data go to the next phase. IoT collects and transfers the data from the sensors.
• Understand the signals in the data. Using data mining algorithms, the entire relevant ecosystem is analyzed for pattern
recognition. The process can be complex as the data collected by IoT sensors are combined with data from other
sources.
• Act. Decisions can be made quickly as all relevant data are in place. SAS decision management tools can support the
process. Decisions range from alerts to automated actions.
The SAS process is illustrated in Figure 13.7. For more on analytics and IoT combination, see SAS Analytics for IoT at
https://www.sas.com/en_us/insights/big-data/internetof-things.html. For additional information, see Henderson
(2017).
Bill Gates’ Futuristic Smart City
In November 2017, Bill Gates purchased 60,000 acres of land west of Phoenix, Arizona, where he plans to construct a futuristic
city from scratch. The city will be a model and place for research.
Technology Support for Smart Cities
A large number of vendors, research institutions, and governments are providing technology support for smart cities. Here
are few examples.
TECHNOLOGY SUPPORT BY BOSCH CORP. AND OTHERS Bosch Corp (of Germany), a major supplier of automotive parts, presented
several innovations related to smart cities at CES 2018.
According to Editors (2018), revenues of global smart cities with IoT technology will exceed $60 billion by 2026.
152 Part IV • Robotics, Social Networks, AI and IoT
FIGURE 13.7 SAS Supports the Full IoT Analytics Life Cycle for Smart Cities (SAS). Source: Courtesy of SAS Institute Inc.
Used with permisison.
Finally, in smart cities, connected and self-driven vehicles will be everywhere (see Hamblen,
2016 and the next section).
u SECTION 13.8 REVIEW QUESTIONS
1. Describe smart city.
2. List some benefits of a smart city to the residents.
3. What is the role of IoT in smart city initiatives?
4. How are analytics combined with IoT? Why?
5. Describe smart and cognitive buildings.
6. What is a smart factory?
7. Describe technology support to smart cities.
13.9 AUTONOMOUS (SELF-DRIVING) VEHICLES
Autonomous vehicles, also known as driverless cars, robot-driven cars, self-driving cars, and
autonomous cars, are already on the roads in several places. The first commercial autonomous car
project was initiated by Google (named Google Chauffeur) and is becoming a reality, with several
U.S. states preparing to allow them on the road. France, Singapore, China, and several other
countries already have these cars and buses on their roads. These cars are electric, and they can
create a revolution by reducing emissions, a ccidents, fatalities (an estimate of about 30,000 fatalities
a year, worldwide), and traffic jams (e.g., see Tokuoka, 2016). Thus far, these cars are being tested
in several cities worldwide and in some cities are already on the roads. Experts estimate that 10
million such cars will be on the roads in the United States by 2020, and China is planning for 30
million cars by 2021.
The Developments of Smart Vehicles
The initial efforts to commercialize a self-driving car were started by Google in the 1990s.
These efforts can be seen today in Waymo’s story in Application Case 13.5.
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 153
Application Case 13.5 Waymo and Autonomous Vehicles Waymo is a unit of Alphabet (previously called Google)
that is fully dedicated to the Google selfdriving car project.
Almost 20 years ago, Google, with the help of Stanford
University, started to work on this project. The idea
received a boost in 2005 when DARPA awarded its Grand
Challenge prize to the project. Then, the U.S. Department
of Defense awarded it a $2 million prize. Google
pioneered physical experiments in 2009 after conducting
computer simulation for several years when it ran self-
driving cars 2.5 billion virtual miles. The next step was to
get legislation to allow autonomous vehicles on the roads.
By 2018, 10 states had passed such laws. Some allow
robot-driven cars only in certain areas. Self-driving cars
(see a Waymo car in Figure 13.8) with robot-only
chauffeurs were tested in early 2018 by Waymo in the
Phoenix, Arizona, area. First, corporate engineering will be
in the driver’s seat; but, around November 2018, the cars
were expected to be completely driverless. The company
was ready to start running commercial minivans in five
states in 2018. By the end of 2018, Waymo vans were
expected to pick up regular passengers who volunteered to
take the service (called Early Rider Program), although
most travelers are still skeptic.
This works in the following way. Company
technicians, acting like regular riders, order service via a
mobile app. The AI mechanism figures out how the
vehicle will get to the requested caller as well as how it will
self-drive to the requested destination.
Waymo, the pioneer of autonomous vehicles,
collaborated with Chrysler (using Chrysler Pacifica
minivans). The computing power is provided by Intel
(with its Mobileye division). The high cost of the cars will
limit their use initially to commercial uses. However,
Waymo already has agreed to manage
Avis’s fleet of self-driving minivans. Also, realizing the
power of ride-sharing services, Waymo is working with
Lyft on new autonomous vehicles. Finally, Waymo is
partnering with AutoNation to provide maintenance and
road services for Waymo cars.
Note: On the legal dispute involving Uber, see the opening case of
Chapter 14.
Sources: Compiled from Hawkins (2017), Ohnsman (2017), and Khoury
(2018).
Questions for Case 13.5
1. Why did Waymo first use simulation?
2. Why was legislation needed?
3. What is the Early Rider Program?
4. Why will it take years before regular car owners will be
able to enjoy a ride in the back seat of their self-driving
cars?
5. Why are Lyft, Uber, and Avis interested in selfdriving
cars?
FIGURE 13.8 Waymo (Google) Self-Driving Car. Source: SiliconValleyStock/Alamy Stock Photo. An example of how Nvidia works with Toyota’s initiative is presented in Technology Insights 13.2.
TECHNOLOGY INSIGHTS 13.2 Toyota and Nvidia Corp. Plan to
Bring Autonomous Driving to the Masses
It is not surprising that Toyota is interested in smart cars. As a matter of fact, the company’s cars are expected to be on the
market in 2020. Toyota plans to produce several types of autonomous vehicles. One type will be for elderly and disabled
people. Another type will have the ability to drive completely autonomously or be an assistant (with a mechanism called
“guardian angel”) to drivers. For example, it will have the ability to take full control when the driver falls asleep, or when it
senses that an accident is coming. A tired driver will be able to use Alexa (or a similar device) to tell the guardian angel to take
over.
Autonomous vehicles need a smart control system, and this is where Nvidia enters the picture. Autonomous cars need
to process a vast amount of data collected by sensors and cameras in real time. Nvidia pioneered a special AI-based
supercomputer (called Drive PX2) for this purpose. The computer includes a special processor (called Xavier) that can power
the autonomous driving gear of the cars. The partnership with Toyota enables Nvidia to leverage the power of its processor to
apply AI to the autonomous cars. Nvidia’s supercomputer has an AI algorithm-based special operating system that includes a cloud-based 3D map with
high definition. With these capabilities, the car’s “brain” can comprehend its driving surroundings. Since a car can also exactly
identify its own location, it will know about any potential hazard (e.g., road work or a vehicle coming toward it). The operating
system is being constantly updated, so it makes the car smarter (AI learning capability).
154 Part IV • Robotics, Social Networks, AI and IoT
The Xavier system provides the car’s “brain” on a special chip (called Volta), which can deliver 30 trill ion deep learning
operations per second. Thus, it can process complex AI algorithms involving machine learning. Nvidia is expected to use
Volta to open a new, powerful era in AI computing.
Source: Compiled from Korosec (2017) and blogs.nvidia.com/blog/2016/09/28/Xavier/.
Questions for DisCussion
1. What does a car need to have in order to be autonomous?
2. What is the contribution of Nvidia to self-driving cars?
3. What is the role of Xavier?
4. Why does the process use a supercomputer?
Despite the required complex technology, several car manufacturers are ready to sell or operate such cars soon
(e.g., BMW, Mercedes, Ford, GM, Tesla, and of course—Google). Developments related to driverless vehicles
follow:
• Uber and other ride-sharing companies plan for self-driving cars.
• Mail is delivered to homes by self-driving cars; see uspsoig.gov/blog/ no-driver-needed.
• Driverless buses are being tested in France and Finland. Watch money.cnn.com/
video/technology/2016/08/18/self-driving-buses-hit-the-road-in-helsinki.
cnnmoney about self-driving buses in Helsinki.
• Self-driving taxis already operate in Singapore.
The Self Drive Act is the first national law in the United States pertaining to selfdriving cars. It aims to regulate
the safety of the passengers in autonomous vehicles. It opens the door for the production of 100,000 cars per year
by 2021.
Flying Cars
While autonomous vehicles on the road may have considerable difficulties, there is research on flying cars. As a
matter of fact, drones that can carry people already exist. As long as there is not much traffic in the air, there will be
no traffic problem. However, the navigation of a large number of flying cars may be a problem. Airbus created a
flying taxi demo in 2016 and Uber developed the concept and summarized it in a 98-page report released in October
2016. Toyota is also working on making a flying car. In January 2018, at the Las Vegas CES, Intel showed an
autonomous passenger drone named Volocopter. This machine can be developed as an air taxi one day. For flying
taxis in New Zealand, see Sorkin (2018).
Implementation Issues in Autonomous Vehicles
Autonomous vehicles such as cars, trucks, and buses are already on the roads in several cities worldwide. However,
before we will see millions of them on the roads, it will be necessary to deal with several implementation issues. The
following are reasons why full commercialization is going to take time:
• The cost of real-time 3D map technologies needs to be reduced and their quality needs to be increased.
• AI software must be nimble and its capabilities increased. For example, AI needs to deal with many unexpected
conditions, including that of the behavior of drivers of other cars.
• Bray (2016) posted an interesting question: “Are customers, automakers and insur-ers really ready for self-
driving cars?” Customers seem to acknowledge that such cars are coming. But they resist boarding one.
However, some daring people expect these cars to do a better job than humans in driving.
• The technology needs more research, which is very expensive. One reason is that the many sensors in the cars
and on the road need to be improved and their cost need to be reduced.
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 155
• The IoT is connecting many objects for autonomous vehicles, including those in clouds. The IoT systems
themselves need to be improved. For example, data transmission delays must be eliminated. For more IT/AI
generic implementation issues, see Chapter 14.
u SECTION 13.9 REVIEW QUESTIONS
1. What are self-driving vehicles? How are they related to the IoT?
2. What are the benefits of self-driving vehicles to drivers, society, and companies?
3. Why are Uber and similar companies interested in self-driving vehicles?
4. What AI technologies are needed to support autonomous vehicles?
5. What are flying cars?
6. List some implementation issues of autonomous vehicles.
13.10 IMPLEMENTING IoT AND MANAGERIAL CONSIDERATIONS
In this chapter, we presented a number of successful IoT-based applications. The results so far are more than
encouraging, especially in areas such as monitoring equipment performance to improve its operation and
maintenance (e.g., CNH in the opening vignette and the IBM Watson case of elevators in Chapter 1). However, this
is only the tip of the iceberg. As we indicated earlier, the IoT can change everything. In this section, we present some
of the major issues that are related to successful IoT implementation. Although there is considerable excitement
about the growth and the potential of the IoT, there are that managers should be aware of.
Major Implementation Issues
McKinsey’s Global Institute (Bughin et al., 2015) has put together a comprehensive Executive’s Guide to the Internet of
Things. This guide identifies the following issues:
• Organizational alignment. Although it is true of several other technology initiatives, with IoT, the
opportunities for operational improvements and creating new business opportunities means that IT and
operational personnel have to work as one team rather than separate functions. As noted by the guide’s authors,
“IoT will challenge other notions of organizational responsibilities. Chief financial, marketing, and operating
officers, as well as leaders of business units, will have to be receptive to linking up their systems.”
• Interoperability challenges. Interoperability is a huge detriment thus far in the growth of IoT applications.
Few IoT devices connect seamlessly with each another. Second, there are many technological issues regarding
connectivity. Many remote areas do not yet have proper Wi-Fi connection. Issues related to Big Data
processing are also responsible for slow progress in IoT adoption. Companies are trying to reduce data at the
sensor level so that only a minimal amount goes into clouds. Current infrastructure hardly supports the huge
amount of data collected by IoT. A related problem is retrofitting sensors on devices to be able to gather and
transmit data for analysis. In addition, it will take time for consumers to replace their analog objects with new
IoT digital smart products. As an example, it is easier for people to replace mobile phones than a car, kitchen
appliances, and other things that can benefit from having a sensor and being connected to IoT.
• Security. Security of data is an issue in general, but it is an even bigger one in the context of IoT. Each device
that is connected to IoT becomes another entry point for malicious hackers to get into a large system or at the
least operate or corrupt a specific device. There are stories of hackers being able to breach and control
automated functions of a car or to control a garage door opener remotely. Such issues require that any large-
scale adoption of IoT involve security considerations from the very beginning.
Given that the Internet is not well secured, applying IoT networks requires special security measures, especially
in the wireless sections of the networks. Perkins (2016) summarizes the situation as follows: “IoT creates a pervasive
digital presence connecting organizations and society as a whole. New actors include data scientists; external
156 Part IV • Robotics, Social Networks, AI and IoT
integrators; and exposed endpoints. Security decision makers must embrace fundamental principles of risk and
resilience to drive change.” For a free e-book about IoT, see McLellan (2017b).
Additional issues follow.
• Privacy. To ensure privacy, one needs a good security system plus a privacy protection system and policy (see
Chapter 14). Both may be difficult to construct in IoT networks due to the large size of the networks and the
use of the less protected Internet. For advice from top security experts, see Hu (2016).
• Connection of the silos of data. There are millions of silos of data on the Internet and many of them need
to be interconnected in specific IoT applications. This issue is known as the need for a “fabric” and
connectivity. This can be a complex issue for applications that involve many different silos belonging to
different organizations. Connectivity is needed in machine to machine, people to people, people to machines,
and people to services and sensors. For a discussion, see Rainie and Anderson (2017) and
machineshop.io/blog/the-fabric-of-theinternet-of-things. For how the connection is done at IBM
Watson, see ibm.com/ Internet-of-things/iot-solutions/.
• Preparation of existing IT architectures and operating models for IoT can be a complex issue in many
organizations. For a complete analysis and guide on this subject, see Deichmann et al. (2015). Integrating IoT
into IT is critical for the data flow needed by the IoT and IoT-processed data to flow back to actions.
• Management. As in the introduction of any new technology, the support of top management is necessary.
Bui (2016) recommends hiring a chief data officer in order to succeed in IoT due to the need to deal with silos of
data described earlier. Using such a top manager can facilitate information sharing across all business functions,
roles, and levels. Finally, it solves departmental struggles to own and control the IoT.
• Connected customers. There is evidence of an increased use of IoT in marketing and customer relationships.
In addition, the IoT drives increased customer engagement. According to Park (2017), a successful deployment
of IoT for customers requires “connected customers.” The connection needs to be for data, decisions,
outcomes, and staff related to any contacts relevant to the IoT and marketing. The Blue Hill research
organization provides a free report on this issue (see Park). IoT enables a better connection with key clients
and improves customer service. Of special considerations are hospitality, healthcare, and transportation
organizations.
Finnaly, Chui et al. (2018) provided suggestions in a recent study on how to succeed in IoT implementation.
With so many implementation issues, an implementation strategy is necessary.
Strategy for Turning Industrial IoT into Competitive Advantage
IoT collects large amounts of data that can be used to improve external business activities (e.g., marketing) as well as
internal operations. SAS (2017) proposed a strategy cycle that includes the following steps:
1. Specify the business goals. They should be set with perceived benefits and costs so the initiatives can be justified.
This step involves a high level of planning and examination of resources. Initial return on investment (ROI)
analysis is advisable.
2. Express an analytic strategy. To support ROI and prepare a business case, it will be necessary to plan how Big Data
will be analyzed. This involves the selection of an analytic platform, which is a critical success factor. An
examination of emerging AI technologies, such as deep learning, may be conducted. An appropriate selection
will ensure a powerful IoT solution.
3. Evaluate the needs for edge analytics. Edge analytics is a technology that is needed for some, but not all, applications.
It is designed to introduce real-time capabilities to the applications. It also filters data to enable automated
decision making, frequently in real time because only relevant data results from the filtering.
4. Select appropriate analytics solutions. There are numerous analytic solutions on the market offered by many vendors.
In using one or several for IoT, it is necessary to consider several criteria such as fitness for IoT, ease of
deployment, ability to minimize project risks, sophistication of the tools, and connection to existing IT systems
(e.g., the quality of IoT gateways). Sometimes it is a good idea to look at a group of vendors that offer combined
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 157
products (e.g., SAS and Intel). Finally, appropriate infrastructures, such as high-performance cloud servers and
storage systems, need to be examined.
These must work together as a scalable, effective, and efficient platform.
5. Continues improvement closes the loop. Like in any strategy cycle, performance should be monitored, and
improvements in various steps of the process need to be considered, especially since IoT is evolving and
changing rapidly. The extent of goal achievement is an important criteria and upgrading the goals should be
considered.
A summary of the process is provided in Figure 13.9.
Weldon (2015) suggests the following steps for successful IoT implementation:
• Develop a business case to justify the IoT project including a cost-benefit analysis and a comparison with
other projects.
• Develop a working prototype. Experiment with it. Learn and improve it.
• Install the IoT in one organizational unit; experiment with it. Learn lessons.
• Plan an organization-wide deployment if the pilot is a success. Give special atten-tion to data processing and
dissemination.
The Future of the IoT
With the passage of time, we see an increasing number of IoT applications, both external and internal to organizations
and enterprises. Because all IoT networks are connected to the Internet, it will be possible to have some of the
networks connected to each other, creating larger IoTs. This will create growth and expansion opportunities for
many organizations.
AI ENHANCEMENT OF IoT There are several areas of potential development. One area where AI will enhance IoT is in
its ecosystem. Many IoT applications are complex and could be improved with machine learning that can provide
insights about data. In addition, AI can help in creating devices (“things”) that can self-diagnose problems and even
repair them. For further discussion, see Martin (2017). Another future benefit of AI when combined with IoT is
“shaping up to be a symbiotic pairing” (Hupfer, 2016). This pairing can create cognitive systems that are able to deal
with and understand data that
158 Part IV • Robotics, Social Networks, AI and IoT
conventional analytics cannot handle. The AI and IoT combination can create an embodied cognition that injects AI
capabilities into objects (such as robots and manufacturing machines) to enable the objects to understand their
environments and then self-learn and improve their operation. For details, see Hupfer (2017). Finally, AI can help
the integration of IoT with other IT systems.
A final word! By now you are probably interested to know about getting a job in IoT. Yes, there is a shortage
of IoT experts, and annual salaries can range from
$250,000 to $500,000. For 2017 data, see Violino (2017).
Chapter Highlights
• The IoT is a revolutionary technology that can change
everything.
• The IoT refers to an ecosystem in which a large number of
objects (such as people, sensors, and computers) are
interconnected via the Internet (frequently wirelessly). By
the years 2020 to 2025, there could be as many as 50 billion
connected objects. Subsystems of such connected things can
be used for many purposes.
• Use of the IoT can improve existing business pro-cesses and
create new business applications.
• Billions of things will be connected to the Internet, forming
the IoT ecosystem.
• Things on the IoT will be able to communicate, and the
structure will enable a central control to manipulate things
and support decision making in IoT applications.
• The IoT enables many applications in industry, services, and
governments.
• IoT applications are based on analysis of data collected by
sensors or other devices that flow over the Internet for
processing.
• Sensors can collect data from a large number of things (e.g.,
over 1 million elevators in the opening case of Chapter 1).
• Major efforts are needed to connect the IoT with other IT
systems.
• IoT applications can support decisions made by equipment
manufacturers and by the users
Key Terms
of equipment. (See the opening vignette of this chapter.)
• IBM Watson is a major provider of IoT appli-cations in
many industries and services (e.g., medical research). It was
projected to reach over 1 billion users by the end of 2018.
• Smart appliances and homes are enabled by IoT.
• Smart city projects worldwide are supported by IoT,
increasing the quality of life for residents of the cities and
supporting the decision m aking of city planners and
technology providers.
• Self-driven cars may reduce accidents, pollution, traffic
jams, and transportation costs. Self-driving cars are not fully
implemented yet, but some were introduced in 2018.
• Smart homes and appliances are popular. For a small cost,
owners can use several applications from home security to
controlling appliances in their homes.
• The concept of smart cities is being developed globally with
projects in countries such as India, Germany, and the
United States and the citystate of Singapore. The objective
of smart cities is to provide a better life for their residents.
Major areas covered are transportation, healthcare, energy
saving, education, and government services.
Questions for Discussion
1. Compare the IoT with regular Internet. 2. Discuss the potential impact of autonomous vehicles on our
lives.
3. Why must a truly smart home have a bot? 4. Why is the IoT considered a disruptive technology? 5. Research Apple Home Pod. How does it interact with smart
home devices? 6. Alexa is now connected to smart home devices such as
thermostats and microwaves. Find examples of other
appliances that are connected to Alexa and write a report. 7. Discuss the objective of smart cities to conserve the earth’s
limited resources.
8. What are the major uses of IoT? 9. Accidents involving driverless cars slow down the
implementation of the technology. Yet, the technology can save
hundreds of thousands of lives. Is the slowdown (usually driven
by politicians) justifiable? Discuss.
autonomous vehicles (driverless radio-frequency identification smart cities
cars) (RFID) smart factory Internet of Things sensor smart homes
Internet of Things ecosystem smart appliance smart sensors
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 159
Exercises
1. Go to theinternetofthings.eu and find information about the
IoT Council. Write a summary of it. 2. Go to https://www.ptc.com/en/resource-center or other
sources, and select three IoT implemented cases. Write a
summary of each. 3. AT&T is active in smart city projects. Investigate their activities
(solutions). Write a summary. 4. It is said that the IoT will enable new customer service and B2B
interactions. Explain how.
5. The IoT has a growing impact on business and e-commerce.
Find evidence. Also read Jamthe (2016). 6. Find information about Sophia, a robot from Hanson
Robotics. Summarize her capabilities.
7. Examine the Ecobee thermostat and its integration with Alexa.
What are the benefits of the integration? Write a report. 8. Enter smartcitiescouncil.com. Write a summary of the major
concept found there; list the major enablers and the type of
available resources. 9. Find the status of Bill Gates’s futuristic smart city. What are
some of its specific plans? 10. City Brain is the name of Alibaba’s platform for smart cities.
One project has been adopted in China and Malaysia. Find
information and write a report.
11. Find the status of delivering pizza by self-driving cars. Check
Domino’s Pizza news.
12. India has many IoT applications, including projects for 100
smart cities. Read the 2016 status report
atenterpriseinnovation.net/article/internet-thingsnext-
big-wave-india-1270947471/ and find more recent
information about it. Why do you think IoT is so widespread
in India? Write a report.
13. Read the Blue Hill report (Park, 2017) and summarize all the
issues related to IoT. 14. Find the status of smart cities as it is related to IoT and Cisco.
Write a report. 15. Watch the video atyoutube.com/
watch?v=ZJr0X3XBMmA (14:36 min.). Write a summary
about the five smart devices. 16. Watch the video “Smart Manufacturing” (22 min.) at
youtube.com/watch?v=SfVUkGoCA7s and summarize the
lessons learned.
17. The competition for creating and using autonomous cars is
intensifying globally. Find 12 companies that are competing in
this field.
18. Enter McKinsey Global Institute mckinsey.com/mgi/
overview and find recent studies on IoT. Prepare the summary. 19. AT&T is trying to connect autonomous vehicles to smart cities.
Find information on the progress of this project. Identify the benefits and the difficulties.
References
Ashton, K. How to Fly a Horse: The Secret History of Creation, Invention and
Discovery. New York City, NY: Doubleday, January 2015. Bhapkar, R., and J. Dias “How a Digital Factory Can Transform Company
Culture.” McKinsey & Company, September 2017. Bordo, M. “Israeli Air Force Works on Battlefield IoT Technology.”
ReadWrite.com, June 21, 2016. Bray, E. “Are Consumers, Automakers and Insurers Really for Self-
Driving Cars?” Tech Crunch, August 10, 2016. Brokaw, L. “Six Lessons from Amsterdam’s Smart City Initiative.” MIT
Sloan Management Review, May 25, 2016. Bughin, J., M. Chui, and J. Manyika. “An Executive’s Guide to the
Internet of Things.” McKinsey Quarterly, August 2015.
Bui, T. “To Succeed in IoT, Hire a Chief Data Officer.” Tech Crunch, July
11, 2016. Burkacky, O., et al. “Rethinking Car Software and Electronics
Architecture.” McKinsey & Company, February 2018. Burt, J. “IoT to Have Growing Impact on Businesses, Industries, Survey
Finds.” eWeek, May 4, 2016.
Chui, M., et al. “What It Takes to Get an Edge in the Internet of Things?”
McKinsey Quarterly, September 2018. Coumau, J., et al. “A Smart Home Is
Where the Bot Is.” McKinsey Quarterly, January 2017.
Deichmann, J., M. Roggendorf, and D. Wee. “Preparing IT Systems and
Organizations for the Internet of Things.” McKinsey & Company,
November 2015. Diaz, J. “CES 2017: LG’s New Smart Fridge Is Powered by Alexa.”
Android Headlines, January 4, 2017. androidheadlines.
com/2017/01/ces-2017-lgs-new-smart-fridgepowered-
alexa.html/ (accessed August 2018). Donaldson, J. “Is the Role of RFID in the Internet of Things Being
Underestimated?” Mojix, May 2, 2017. Durrios, J. “Four Ways IoT Is Driving Marketing Attribution.” Enterprise
Innovation, April 8, 2017.
Editors. “Smart Cities Will Use 1.6B Connected Things in 2016.” eGov
Innovation, December 22, 2015. Editors. “Global Smart Cities IoT Technology Revenues to Exceed
US$60 Billion by 2026.” Enterprise Innovation, January 23, 2018. Estopace, E. “French National Railway Operator Taps IoT for Rail
Safety.” eGov Innovation, February 21, 2017a.
Estopace, E. “Consortium to Build a Smart Mobility System for Hong
Kong.” Enterprise Innovation, March 26, 2017b. Fenwick, N. “IoT Devices
Are Exploding on the Market.” Information Management, January 19, 2016. Fitzgerald, M. “Data-Driven City Management: A Close Look at
Amsterdam’s Smart City Initiative.” MIT Sloan Management Review, May
19, 2016.
160 Part IV • Robotics, Social Networks, AI and IoT
Freeman, M. “Connected Cars: The Long Road to Autonomous
Vehicles.” San Diego Union Tribune, April 3, 2017. Gemelli, M. “Smart Sensors Fulfilling the Promise of the IoT.” Sensors
Magazine, October 13, 2017. Greengard, S. “How AI Will Impact the Global Economy.” CIO Insight,
October 7, 2016.
Hamblen, M. “Smart City Tech Connects Cars and Bikes with Big Data
at MCW: Innovators Can Put Air Quality Sensors on Bicycles, While
Wireless Connections Help Pave the Way for Driverless Cars.”
Computerworld, February 22, 2016.
Hawkins, A. “Intel Is Working with Waymo to Build Fully SelfDriving
Cars.” The Verge, September 18, 2017. Hedge, Z. “Case Study: Athens International Airport Uses EXM and
Libelium’s IoT Platform to Enhance Environmental Monitoring.” IoT
Now, September 1, 2017. Henderson, P. “10 Ways Analytics Can Make Your City Smarter.”
InfoWorld and SAS Report AST = 0182248, June 6, 2017.
Hu, F. Security and Privacy in Internet of Things (IoTs): Models, Algorithms, and
Implementations. Boca Raton, FL: CRC Press, 2016. Hupfer, S. “AI Is the Future of IoT.” IBM Blog, December 15, 2016.
ibm.com/blogs/internet-of-things/ai-future-iot/ (accessed July
2018). IBM. “Embracing the Internet of Things in the New Era of Cognitive
Buildings.” White Paper. IBM Global Business Services, 2016.
Jamthe, S. The Internet of Things Business Primer. Stanford, CA: Sudha Jamthe,
2015.
Jamthe, S. IoT Disruptions 2020: Getting to the Connected World of 2020 with
Deep Learning IoT. Seattle, WA: Create Space Independent Publishing
Platform, 2016. Kastrenakes, J. “Nest Can Now Use Your Phone to Tell When You’ve
Left the House.” The Verge, March 10, 2016.
theverge.com/2016/3/10/11188888/nest-nowuses-location-for-
home-away-states-launches-familyaccounts (accessed April
2018). Khoury, A. “You Can Now Hail a Ride in a Fully Autonomous Vehicle,
Courtesy of Waymo.” Digital Trends, February 17, 2018. Korosec, K. “Toyota Is Using Nvidia’s Supercomputer to Bring
Autonomous Driving to the Masses.” The Verge, May 10, 2017.
Koufopoulos, J. “9 Examples of the Internet of Things That Aren’t
Nest.” Percolate, January 23, 2015.
Kvitka, C. “Navigate the Internet of Things.” January/February 2014.
oracle.com/technetwork/issue-
archive/2014/14jan/o14interview-utzschneider-2074127.html
(accessed April 2018). Lacey, K. “Higher Ed Prepares for the Internet of Things.” University
Business, July 27, 2016. universitybusiness.com/article/higher-
prepares-internet-things (accessed April 2018).
Libelium. “Smart Factory: Reducing Maintenance Costs and Ensuring
Quality in the Manufacturing Process.” Libelium World, March 2, 2015.
technology.ihs.com/531114/theinternet-of-everything-needs-a-
fabric (accessed April 2018). Manyika, J., M. Chui, P. Bisson, J. Woetzel, R. Dobbs, J. Bughin, and D.
Aharon. “Unlocking the Potential of the Internet of Things.” McKinsey
Global Institute, June 2015. Marcus, J. “CNH Industrial Halves Product
Downtime with IoT.” Product Lifecycle Report, May 6, 2015. Martin, E. “AI
May Have Your Health and Finances on Record Before the Year Is Out.”
FutureFive, July 20, 2017. futurefive. co.nz/story/five-ways-ai-
machine-will-affect-your-lifeand-business-year/ (accessed April
2018). McCafferty, D. “How the Internet of Things Is Changing Everything.”
Baseline, June 16, 2015.
McGrath, J. “Haier Wants You to Live Smaller and Smarter with Its New
Appliances.” Digital Trends, January 5, 2016.
digitaltrends.com/home/haier-shows-off-u-smartappliances-
at-ces-2016 (accessed April 2018). McLellan, C. “Internet of Things in the Enterprise: The State of Play.”
ZDNet.com, February 1, 2017a. zdnet.com/article/ enterprise-iot-
in-2017-the-state-of-play/ (accessed April 2018).
McLellan, C. “Cybersecurity in an IoT and Mobile World.” Special
Report. ZDNet, June 1, 2017b. Meola, A. “What Is the Internet of Things (IoT)? Meaning & Definition.”
Business Insider, May 10, 2018.
Miller, M. The Internet of Things: How Smart TVs, Smart Cars, Smart Homes,
and Smart Cities Are Changing the World. Indianapolis, IN: Que Publishing, 2015.
Miller, R. “IoT Devices Could Be Next Customer Data Frontier.”
TechCrunch, March 30, 2018. Morris, C. “Ordinary Home Appliances Are About to Get Really Sexy.”
Fortune.com, January 6, 2016. fortune. com/2016/01/06/home-
appliances-ces-2016 (accessed April 2018). Morris, S., D. Griffin, and P. Gower. “Barclays Puts in Sensors to See
Which Bankers Are Their Desks.” Bloomberg, August 18, 2017. Murray, M. “Intel Lays Out Its Vision for a Fully Connected World.” PC
Magazine, August 16, 2016.
Ohnsman, A. “Our Driverless Future Begins as Waymo Transitions to
Robot-Only Chauffeurs.” Forbes, November 7, 2017. Park, H. “The Connected Customer: The Why Behind the Internet of
Things.” Blue Hill Research. White Paper. January 2017.
Perkins, E. “Securing the Internet of Things.” Report G00300281. Gartner
Inc., May 12, 2016. Pitsker, K. “Put Smart Home Technologies to Work for You.” Kiplinger’s
Personal Finance, October 2017. PTC, Inc. “Internal Transformation for IoT Business Model Reshapes
Connected Industrial Vehicles.” PTC Transformational Case Study,
November 12, 2015. ptc.com/~/
media/Files/PDFs/IoT/J6081_CNH_Industrial_Case
_Study_Final_11-12-15.pdf?la=e (accessed April 2018). Pujari, A. “Becoming a Smarter Manufacturer: How IoT Revolutionizes
the Factory.” Enterprise Innovation, June 5, 2017. Rainie, L., and J. Anderson. “The Internet of Things Connectivity Binge:
What Are the Implications?” PewInternet.com, June 6, 2017.
SAS. “SAS Analytics for IoT: Smart Cities.” SAS White Paper
108482_G14942, September 2016. SAS. “5 Steps for Turning Industrial IoT Data into a Competitive
Advantage.” SAS White Paper 108670_G456z 0117. pdf, January 2017. Scannell, B. “High Performance Inertial Sensors Propelling the Internet
of Moving Things.” Technical Article. Analog Devices, 2017.
Chapter 13 • The Internet of Things as a Platform for Intelligent Applications 161
Schwartz, S. Street Smart: The Rise of Cities and the Fall of Cars. Kindle
Edition. New York, NY: Public Affairs, 2015. Shah, S. “HPE, Tata to Build ‘World’s Largest’ IoT Network in India.”
Internet of Business, February 27, 2017. internetofbusiness.com/hpe-
tata-largest-iot-network-india/ (accessed April 2018).
Sharda, R., et al. Business Intelligence, Analytics, and Data Science: A Managerial
Perspective. 4th ed. New York, NY: Pearson, 2018. Sinclair, B. IoT Inc.: How Your Company Can Use the Internet of Things to Win
in the Outcome Economy. Kindle Edition, New York, NY: McGraw-Hill
Education, 2017. Solomon, S. “Israel Smart-Roads Startup Nabs Prestigious EY Journey
Prize.” The Times of Israel, October 26, 2017. Sorkin, A. “Larry Page’s Flying Taxis Now Exiting Stealth Mode.” The
New York Times, March 12, 2018. Staff. “Study Reveals Dramatic Increase in Capabilities for IoT Services.”
Information Management, May 5, 2017. Technavio. “Smart Sensors for the Fourth Industrial Revolution: Molding
the Future of Smart Industry with Advanced Technology.”
Technavio.com, September 12, 2017. Tokuoka, D. Emerging Technologies: Autonomous Cars. Raleigh, NC:
Lulu.com, 2016. Tomás, J. “Smart Factory Tech Defining the Future of Production
Processes.” RCR Wireless News, March 28, 2016.
Townsend, A. Smart Cities: Big Data, Civic, Hackers and the Quest for a New
Utopia. New York, NY: W. W. Norton, 2013. Twentyman, J. “Athens International Airport Turns to IoT for
Environmental Monitoring.” Internet of Business, September 4, 2017.
Venkatakrishnan, K. “Are Connected Consumers Driving Smart
Homes?” Enterprise Innovation, May 31, 2017. Violino, B. “19 Top Paying Internet-of-Things Jobs.” Information
Management, October 25, 2017. Weinreich, A. “The Future of the Smart Home: Amazon, Walmart, & the
Home That Shops for Itself.” Forbes, February 1, 2018.
Weldon, D. “Steps for Getting an IoT Implementation Right.” Information
Management, October 30, 2015.
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Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 163
Implementation Issues: From Ethics and
Privacy to Organizational and Societal
Impacts
LEARNING OBJECTIVES
■■ Describe the major implementation issues of ■■ Discuss the arguments of utopia and dystopia intelligent technologies in a
debate of the future of robots and artificial
■■ Discuss legal, privacy and ethical issues intelligence (AI)
■■ Understand the deployment issues of intelligent ■■ Discuss the potential danger of mathematical systems models in
analytics
■■ Describe the major impacts on organizations and ■■ Describe the major influencing technology trends
society ■■ Describe the highlights of the future of intelligent ■■ Discuss and debate the impacts on jobs and work systems
n this concluding chapter, we cover a variety of issues related to the implementation and future of
intelligent systems. We begin our coverage with technological issues such as security and connectivity.
Then, we move to managerial issues that cover legality, privacy, and ethics. We next explore the impacts
on organizations, society, work and jobs. Then, we review technology trends that point to the future.
Note. In this chapter we refer to all technologies covered in this book as intelligent technologies or intelligent
systems.
This chapter has the following sections:
14.1 Opening Vignette: Why Did Uber Pay $245 Million to Waymo? 727
14.2 Implementing Intelligent Systems: An Overview 729
14.3 Legal, Privacy, and Ethical Issues 731
14.4 Successful Deployment of Intelligent Systems 737
14.5 Impacts of Intelligent Systems on Organizations 740
14.6 Impacts on Jobs and Work 747
726
14.7 Potential Dangers of Robots, AI, and Analytical Models 753
14.8 Relevant Technology Trends 756
14.9 Future of Intelligent Systems 760
14.1 OPENING VIGNETTE: Why Did Uber Pay $245 Million to Waymo? In early 2018, Uber Technologies, Inc. paid $245 million worth of its own shares to Waymo Self-Driving Cars (a subsidiary of Alphabet).
The payment was made to settle a lawsuit filed by Waymo alleging that Uber was using Waymo’s stolen proprietary technology.
I
THE BACKGROUND OF THE CASE
The lawsuit relates to the protection of intellectual property (trade secrets) owned by Waymo. As you may recall from Section 13.9,
Waymo pioneered the self-driving car. A former engineer of Waymo (named Levandowski) allegedly illegally downloaded 14,000 of
Waymo self-driving related confidential files. Worse than that, Levandowski may have convinced several top engineers of Waymo to
leave Waymo and join him to create a startup, Otto Company, for developing self-driving vehicles. Uber acquired Otto Company. For
Uber, self-driving cars are essential for profitable growth when Uber will use such cars in a car-hailing system. Uber is a major car-
hailing company that plans to move from sharing cars owned by individuals to the car-hailing business where self-driven cars will be
owned by Uber and/or by car manufacturers. This way the profit for Uber could be much higher. Furthermore, Uber plans to operate
driverless taxi fleets.
THE LEGAL DISPUTE
The legal dispute is very complicated. It deals with intellectual property and the ability of high technology employees to work after leaving
their jobs for competitors.
Lawyers from Waymo claimed potentially huge damage if the Waymo trade secrets are used by the competitors. Waymo’s legal
team based their case on a digital-forensics investigation that proved that Levandowski deliberately copied the confidential files and
then tried to cover this downloading. Note that Uber did not steal trade secrets, but hired Levandowski, who had these secrets.
From a legal point of view, the case was unique, being the first related to self-driving cars, so there were no previous cases to rely
on. The two companies are large tech companies in Silicon Valley.
Employees that leave companies are interviewed and reminded that they signed an agreement regarding trade secrets they acquired
when working for the company they leave. Levandowski said in his exit interview from Waymo that his future plans did not include
competing activities that may compete with Waymo’s self-driving cars. However, he had already met with Uber and sold it his new
company, Otto Trucking. It became very clear that both Uber and Levandowski were not telling the truth.
WHY DID THEY SETTLE?
The rivals settled after four days in court. The case was in front of a jury, a fact that introduced an uncertain element to the case.
Waymo agreed to settle since, to win the case, it had to prove actual damage, which it was unable to do. Future damage is very
difficult to compute. Furthermore, there was
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 165
no evidence that Uber was using any of Waymo’s trade secrets, and Uber had already fired
Levandowski.
Uber agreed to pay Waymo because the legal case constituted a possible delay in its
development of self-driving cars, which is critical for the future of Uber. Also, legal fees were
mounting (Uber is involved in several other legal issues related mostly to its drivers). Fighting
Waymo did not ensure success given the deep pockets of Google. Actually, Waymo sent a clear
message that it would protect its leading self-driving cars’ position at any cost.
CONCLUSION
• Uber paid about one-third of 1 percent in shares of its company. Uber was valued at $70
billion (January 2018), which makes the payment equivalent to $245 million. Uber is planning
on going public, which may increase its valuation.
• Uber agreed not to incorporate Waymo’s confidential information into its existing or future
technology. This was a major condition of Waymo.
• The reason why this dispute is important to both companies is that the autonomous vehicle
market could be worth $7 trillion by 2050 (per Marshall and Davies, 2018). Note: It is equal to
about one-third of the total current U.S. national debt.
• There has been a major emerging change in the nature of the self-driving cars’ com-petition
between 2016, when the legal dispute settled in July 2018.
Today, there are many more competitors and much more publicly known technologies and
processes (i.e., fewer trade secrets). Finally, companies need to tell their employees what is not a
trade secret.
u DISCUSSION QUESTIONS FOR THE OPENING VIGNETTE
1. Identify the legal issues involved in this case.
2. Why do you think Waymo agreed to take Uber’s shares instead of money?
3. What is the meaning of intellectual property in this case?
4. The presiding federal judge said at the end: “This case is now ancient history.” What did he
mean to say?
5. Summarize the potential damages to the two parties if they had continued with the legal dispute.
6. Summarize the benefits of the settlement to both sides.
WHAT CAN WE LEARN FROM THIS VIGNETTE
Self-driving cars are a major product of intelligent systems and artificial intelligence (AI) with huge
potential benefits to its participants. Also, inevitable is the strong competition in the industry and
the importance of trade secrets acquired along the way. Legal disputes are common in competitive
situations, and the protection of intellectual property is critical. Intellectual property protection is
one topic presented in our concluding chapter. Other issues that are related to the implementation
of intelligent systems and are discussed in this chapter are e thics, security, privacy, connectivity,
integration, strategy, and top management roles.
We also learned in this vignette about the future importance of the new technology of
autonomous vehicles. This technology may have a huge impact on organizations and their structure
and operation. In addition, we discuss in this chapter the societal impacts of intelligent systems, and
particularly their impact on work and jobs. We also explore some potential unintended
consequences of intelligent systems. Finally, we explore the potential future of intelligent systems
and introduce the big debate regarding the dangers versus possible benefits of intelligent systems
and particularly robots and AI.
166 Part V • Caveats of Analytics and AI
Sources: Compiled from A. Marshall & A. Davies. (2018, February 9). “The End of Waymo v. Uber Marks a New Era for
Self-Driving Cars: Reality.” Wired; A. Sage, et al. (2018, February 9). “Waymo Accepts $245 Million and Uber’s ‘Regret’ to
Settle Self-Driving Car Dispute.” Reuters (Business News); K. Kokalitcheva. (2017, May 9). “The Full History of the Uber-
Waymo Legal Fight.” Axios.
14.2 IMPLEMENTING INTELLIGENT SYSTEMS: AN OVERVIEW Now that you have learned the essentials of analytics, data science, artificial intelligence, and decision
support activities, you may be tempted to ask: What can I do with all this in my organization? You
learned about the great benefits and you read about numerous companies that use intelligent
systems. So, what you should do next? First read some of the resources recommended in this book
so you will have a better understanding about the technologies. Next, read this chapter that deals
with the major issues that are involved in implementing intelligent systems in organizations.
Implementing business analytic/AI systems can be a complex undertaking. In addition to
specific issues found in intelligent systems, there are issues that are common to many other
computer-based information systems. In this section, we describe the major types of issues, some
of which are discussed in this chapter. For several success AI implementation factors revealed in a
survey of 3000 executives, see Bughin, McCarthy, and Chui (2017).
The Intelligent Systems Implementation Process
This chapter is divided into three parts. In the first part, we describe some managerialrelated
implementation issues. In the second part, we describe the impacts of intelligent technologies on
organizations, management, work, and jobs. The last part deals with technology trends and the
future of intelligent technologies.
The implementation process of intelligent systems is similar to the generic process of other
information systems. Therefore, we will present it only briefly. The process is illustrated in Figure
14.1.
THE MAJOR STEPS OF IMPLEMENTATION The major steps are:
Step 1 Need assessment. Need assessment needs to provide the business case for the intelligent
systems, including their major parts. (This is a generic IT step and will not be discussed here.)
Step 2 Preparations. In this step, it is necessary to examine the organization readiness for
analytics and AI. It is necessary to check available resources, employees’
FIGURE 14.1 Implementation Process. Drawn by E. Turban
attitudes for the change, projects’ priorities, and so on. This generic IT activity will not be
discussed here. However, it is useful to think about legal, privacy, and ethical issues as they
are related to intelligent technologies as described in Section 14.3.
Step 3 System acquisition. Organizations need to decide on in-house or outsourcing approach
(make or buy) or on a combination of the two and possibly with partnership with a vendor
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 167
or another company. A consultant may help at this step. It is a generic IT step that will not
discussed here.
Step 4 System development. Regardless of who will develop the system, certain activities need
to be done. These include security, integration with other systems, project management
preparation, and other activities. Again, many of those are generic and will not be described
here. Only selected ones are described in Section 14.4.
Step 5 Impact assessment. It is necessary to check the performance of the systems against
plans. Again, this is a generic issue that will not be covered here.
The Impacts of Intelligent Systems
Intelligent systems are impacting all our lives and many businesses and other organizations. It is
much easier to find what is not impacted than what is impacted. In this section, we divide these
impacts into three categories as shown in Figure 14.2 with the section numbers where they are
presented. We exclude from this list the impact on individuals and quality of life, which is a very
large field (health, education, entertainment, crime fighting, social services, etc.).
Example
Here is the example in the entertainment field. In the near future, when you go Disneyland, Disney
World, or one of the Disney International Parks, you will see highflying acrobatic robots. You will
see them everywhere there, and it is amazing. For a preview, watch the following videos:
money.cnn.com/video/news/2018/07/04/disneyrobots-acrobatics-stuntronics-
animatronics.cnnmoney/index.html and youtube. com/watch?v=Z_QGsNpI0J8.
FIGURE 14.2 Impact Landscape. Drawn by E. Turban
u SECTION 14.2 REVIEW QUESTIONS
1. List the major steps in the implementation process.
2. Why is implementation an important subject?
3. Describe the major impact areas of intelligent systems.
168 Part V • Caveats of Analytics and AI
14.3 LEGAL, PRIVACY, AND ETHICAL ISSUES As data science, analytics, cognitive computing, and AI grow in reach and pervasiveness, everyone
may be affected by these applications. Just because something is doable through technology does
not make it appropriate, legal, or ethical. Data science and AI professionals and managers have to
be very aware of these concerns. Several important legal, privacy, and ethical issues are related to
intelligent technologies and they are interrelated. For example, several privacy issues are parts of
ethics or have legal aspects. Here we provide only representative examples and sources as pointed
out in Chapter 1. Our goal here is only to give the reader an exposure to these issues. For why
should we care about the legal, ethical, and privacy of AI, see Krigsman (2017).
Legal Issues
The introduction of intelligent technologies may compound a host of legal issues already relevant
to computer systems. For example, questions concerning liability for the actions of advice provided
by intelligent machines are beginning to be considered. In this section, we provide a sample of
representative issues. Many more exist.
In addition to resolving disputes about the unexpected and possibly damaging results of some
intelligent systems (see the opening vignette and Section 14.7), other complex issues may surface.
For example, who is liable if an enterprise finds itself bankrupt as a result of using the advice of an
AI-based application? Will the enterprise itself be held responsible for not testing the system
adequately before entrusting it with sensitive or volatile issues? Will auditing and accounting firms
share the liability for failing to apply adequate auditing tests? Will the software developers of
intelligent systems be jointly l iable? As self-driving cars become more common, who is liable for
any damage or injury when a car’s sensors, network, or AI system fail to function as planned? A
recent case involving a Tesla car accident where the driver died in a crash while the car was allegedly
on “autopilot” mode has brought this issue to the front pages of newspapers and the legal
profession.
A SAMPLE OF AI POTENTIAL LEGAL ISSUES
• What is the value of an expert opinion in court when the expertise is encoded in a computer?
• Who is liable for wrong advice (or information) provided by an intelligent application? For
example, what happens if a physician accepts an incorrect diagnosis made by a computer and
performs a procedure that results in the death of a patient?
• What happens if a manager enters an incorrect judgment value into an intelligent ap-plication
and the result is damage or a disaster?
• Who owns the knowledge in a knowledge base (e.g., the knowledge of a chatbot)?
• Can management force experts to contribute their expertise to an intelligent system? How will
they be compensated?
• Is it okay for self-driving cars with in-vehicle back-up drivers to drive on public roads? (Yes, in
a few states, notably in California.)
• Who should regulate driverless car: cities, states, or the federal government?
• U.S. federal regulators are creating national laws for self-driving cars (for safe driving).
• Should delivery robots be allowed on sidewalks? (Not in San Francisco but in some European
cities)
• Are drivers of Uber and similar companies self-employed? (Not in London, the United
Kingdom)
• Should robots have human rights? (What if they are citizens like Sophia in Saudi Arabia?) If they
get rights, should they have legal responsibilities as well?
• Should we legalize robot taxis? Would this make trips cheaper? (Yes in Singapore and other
places, and it can be cheaper)
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 169
Source: Turban, Introduction to Information Technology, 2nd edition, John Wiley & Sons, 2006.
Example: Intellectual Property Protection
The opening vignette directed our attention to a legal issue that is very important for technology-
related companies: the ownership and protection of intellectual property.
LEGAL ISSUES OF INTELLIGENT TECHNOLOGIES Several of the ethical issues described later need to be
combined with legal issues. For example, take robots’ legal rights. Do we need these rights? What
for (an ethical issue)? Then, it will be necessary to develop the legal rights. Facebook, for example,
has had legal issues regarding face recognition. Safety rules for robots were developed a long time
ago. At the moment, there are very few laws regarding intelligent technologies. Most of the laws
relate to safety.
AI AND LAW In addition to laws related to robotics and AI, there is a subfield of AI that is concerned
with AI applications to the legal profession and the solution of some legal problems. According to
Donahue (2018), the following are some major topics:
• Analyzing legal-related data (e.g., regulatory conflicts) to detect pattern
• Providing legal advice to consumers (e.g., see DoNotPay.com).
• Document review
• Analyzing contracts
• Supporting legal research
• Predicting results (e.g., likelihood to win)
• AI impact on the legal profession.
AI can execute routine legal-related tasks such as managing documents and drafting c ontracts. For
details, see Kahn (2017). For 35 applications in law and legal practice see Rayo (2018). Legal issues
may be strongly connected to our next topic, privacy.
Privacy Issues
Privacy means different things to different people. In general, privacy is the right to be left alone
and the right to be free from unreasonable personal intrusions. Privacy has long been related to
legal, ethical, and social issues in many countries. The right to privacy is recognized today in every
state of the United States and by the federal government either by statute or by common law. The
definition of privacy can be interpreted quite broadly. However, the following two rules have been
followed fairly closely in past court decisions: (1) The right of privacy is not absolute. Privacy must
be balanced against the needs of society. (2) The public’s right to know is superior to the individual’s
right to privacy. These two rules show why it is difficult, in some cases, to determine and enforce
privacy regulations. Privacy issues online have specific characteristics and policies. One area where
privacy may be jeopardized is discussed next. Privacy issues are getting more and more important
as the amount of data generated on the Internet is increasing exponentially, and in many cases it is
lightly secured. For an overview of privacy as it relates to AI, see Provazza (2017).
COLLECTING INFORMATION ABOUT INDIVIDUALS Intelligent technologies aim to provide targeted
services and marketing to consumers; they do so by collecting information about these customers.
In the past, the complexity of collecting, sorting, filing, and accessing information manually from
numerous government agencies and other public databases was, in many cases, a built-in protection
against the misuse of private information. The Internet in combination with large-scale databases
has created an entirely new dimension of accessing and using data. The inherent power in intelligent
170 Part V • Caveats of Analytics and AI
systems that can access vast amounts of data and interpret them can be used for the good of society.
For example, by analyzing records with the aid of business analysis, it is possible to eliminate or
reduce fraud, crime, government mismanagement, tax evasion, welfare cheating, family-support
filching, employment of illegal workers, and so on. However, what price must the individual pay in
terms of loss of privacy so that the government can better apprehend criminals? The same is true
on the corporate level. Private information about employees may aid in better corporate decision
making, but the employees’ privacy may be compromised.
The use of AI technologies in the administration and enforcement of laws and regulations
may increase public concern regarding privacy of information. These fears, generated by the
perceived abilities of AI, will have to be addressed at the outset of almost any AI development
effort.
VIRTUAL PERSONAL ASSISTANTS Amazon’s Echo/Alexa and similar devices listen to what is going on.
They also may take photos. In other words, your voice assistant is spying on you.
Most advanced is the Echo/Alexa pair. You can ask Alexa to buy Amazon products today.
Amazon and Google filed for a patent that will enable the virtual assistants in your home to advertise
and sell you products. Privacy advocates are not happy, but customers may be. For example, Elgen
(2017) describe how Alexa acts as a fashion consultant, using style check. The system combines the
knowledge of a fashion specialist and AI knowledge. A recommendation provides you with two
photos at a time, telling you which one to buy (based on color, current trends, etc.). To make it
useful, Amazon is improving the privacy. This may not be easy since your record is stored in
Amazon’s cloud.
Huff (2017) provides arguments about the risks of the assistant and the protection provided
by Amazon.
MOBILE USER PRIVACY Many users are unaware of the private information being tracked through their
smartphone usage. Many apps collect user data that track each phone call as it moves from one cell
tower to another, from GPS-enabled devices that transmit users’ locations, and from phones
transmitting information at Wi-Fi hotspots. Major app developers claim that they are extremely
careful and protective of users’ privacy, but it is interesting to note how much information is
available through the use of a single device, especially when smartphones contain more and more
AI components.
PRIVACY IN IOT NETWORKS For privacy and security of the Internet of Things (IoT), see Hu (2016).
More data are flowing with IoT networks. Note that AI data privacy issues are on the rise, especially
when AI deals with consumers’ data. There is a growing amount of data collected, for example, by
machine learning and chatbots. Also, in the enterprise, employers collect and analyze more data on
employees. How do we protect the data and guard against their misuse?
RECENT TECHNOLOGY ISSUES IN PRIVACY AND ANALYTICS With the growth of Internet users in general
and mobile device users in particular, many companies have started to employ intelligent
technologies to develop profiles of users on the basis of their device usage, surfing, and contacts.
The Wall Street Journal has an excellent collection of articles titled “What They Know”
(WallStreetJournal.com, 2016). These articles are constantly updated to highlight the latest
technology and privacy/ethical issues. One of the companies mentioned in this series is Rapleaf
(now part of Towerdata). Rapleaf’s technology claims to be able to provide a profile of a user just
by knowing his or her e-mail address. Clearly, Rapleaf’s technology enables it to gather significant
related information. Another company that aims to identify devices on the basis of their usage is
BlueCava, which recently merged with Qualia (Qualia.com). Qualia’s BlueCava technology
attaches a personal profile to be able to recognize a user as one individual or a household, even
though the user may be working with multiple mobile devices and laptops. All of these companies
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 171
employ analytics such as clustering and association mining to develop profiles of users. Of course,
many of the analytics start-ups in this space claim to honor user privacy, but violations are often
reported. For example, Rapleaf was collecting unauthorized information from Facebook users and
was subsequently banned from Facebook. One user reported that an hour after he gave his e-mail
address to a company that specializes in user information monitoring (reputation.com), the
company was able to discover his Social Security number. So, violations of privacy create fears of
criminal conduct regarding information. This area is a big concern overall and needs careful study.
These examples not only illustrate the power of analytics in being able to learn more about target
customers but also serve as a warning to AI and analytics professionals about being sensitive to
privacy and ethical issues.
Another related application area of privacy concerns is analyzing employee behaviors on the
basis of data collected from sensors that employees wear in a badge. One company, Humanyze, has
reported several such applications of its sensor-embedded badges. These sensors track all
movements of an employee.
Example: Using Sensors and IoT to Observe Bankers at Barclays Bank
Using heat and motion sensors, Barclays tracks how long its bankers are at their desks. The system
was installed in the London, United Kingdom, branches. The formal e xplanation was to find out
the occupancy of the cubes in the bank to optimally allocate and possibly reduce office space. The
IoT network provided dashboards showing which workstations (cubes) were underutilized, and
what the usage trend was. The bank informed the employees and the union that this project did not
measure productivity, only space utilization. The results can be used to better manage energy
consumption in the cubes and to schedule a flexible work environment. As a result, Barclays was
able to save office space and rent it out for $45 million a year.
The bank uses a similar tracking system to find out how much time that different types of
employees spend with customers. The union is watching this IoT application carefully to ensure
that it is not used to spy on employees. Other banks in England use similar systems. For details, see
Bloomberg News (2017).
Of course, situations like those described create major privacy concerns. Should companies
be able to monitor their employees this intrusively?
Finally, there is a possibility of ransomware, or hackers’ attacks on robots, which could be
used against businesses whose employees use such robots. Smith (2018) reported on research that
identified 50 vulnerabilities in robots. Ransomware attacks may interrupt operations, forcing
organizations to pay substantial ransoms.
OTHER ISSUES OF POTENTIAL PRIVACY VIOLATION The following are some more examples of potential
privacy violations in the intelligent technology world:
• Delaware police are using AI dashcoms to look for fugitives in passing cars. Photos and
videos taken are sent to the clouds and analyzed there by AI algorithms.
• Facebook’s face recognition systems create concerns regarding privacy protection.
• Epicenter offers its employees a microchip implant. It acts like a swipe card, opens doors,
buys you food in the company store, and much more. But management can track you too. It
is given only to volunteers.
Who Owns Our Private Data?
With the recent growth of data from our use of technology and the companies’ ability to access and
mine it, the privacy debate also leads to the obvious issue of whose property any user’s data is; see
Welch (2016) for highlights in this issue in a Bloomberg Businessweek column. Take an example of a
relatively new car. The car is equipped with many sensors starting with tire pressure sensors to GPS
172 Part V • Caveats of Analytics and AI
trackers that can keep track of where you have gone, how fast you were driving, when you changed
lanes, and so on. The car may even know the passenger’s weight added to the front seat. As Welch
notes, a car connected to the Internet (most new cars are!) can be a privacy nightmare for the owner
or a data “gold mine” for whoever can possess or analyze these data. A major battle is brewing
between automobile manufacturers and technology providers such as Apple (CarPlay) and Google
(Android Auto) on who owns these data and who can access them. This is becoming more crucial
because as cars become smarter and eventually self-driving, the driver/passenger in the car could
be a highly targeted prospect for marketers’ services. For example, Google’s Waze app collects GPS
data from millions of users to track traffic information and help users find the best routes; but it
also displays pop-up ads on the users’ screens. Yelp, Spotify, and other apps popularly used in cars
have similar approaches.
The bottom line is that intelligent systems professionals and users must be aware of the legal
and ethical issues involved in collecting information that may be privileged or protected. Privacy
issues are considered in many cases as important components of ethics.
Ethics Issues
Several ethical issues are related to intelligent systems. Personal values constitute a major factor in
the issue of ethical decision making. The study of ethical issues is complex because of their
multidimensional nature. One story that upset many users (although it was not illegal) some time
ago was Facebook’s experiment to present different News Feeds to the users and monitor their
emotional reactions as measured by replies, likes, sentiment analysis, and so on. Most companies,
including technology companies, run user testing to identify the features most liked or disliked and
fine-tune their product offerings accordingly. Because Facebook is so large, running this experiment
without the users’ informed consent was viewed as unethical. Indeed, Facebook acknowledged its
error and instituted a more formal review through Internal Review Boards and other compliance
mechanisms for future testing.
Morgan (2017) said that it is necessary to be at the foundations of what AI does for both
vendors and customers in order to stay ethical and have transparency of each situation. This way
people can stay honest and adhere to the goals of AI, so it can play a significant role in our life and
work. For how ethical issues interfere with Alphabet’s (Google) initiatives, see Kahn (2017).
Ethical Issues of Intelligent Systems
Many people have raised questions regarding ethical issues in AI, robotics, and other intelligent
systems. For example, Bossmann (2016) raised the following issues:
1. What are their impact on jobs (see Section 14.5)?
2. How do machines (i.e., robots) affect our behavior and interactions?
3. How can wealth created by intelligent machines be distributed (e.g., Kaplan, 2016)?
4. How can intelligent applications mistakes be guarded against? For example, how long should
training programs in machine learning be?
5. Can intelligent systems be fair and unbiased? How can bias in creation and operation of AI
systems be eliminated?
6. How can intelligent applications be keep safe from adversaries?
7. How can systems be protected against unintended consequences (e.g., accidents in robot
operations)? For example, Facebook researchers had to shut down an AI system that created
its own poor language.
8. How can we stay in control of a complex intelligent system?
9. Should we develop robots’ legal rights? How can we define and plan human treatment of
intelligent machines?
10. Should we allow a self-governing robot society to exist with ours?
11. To what extent should we influence unintended robots’ behavior (or even be able to)?
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 173
12. How would we get around the question of smart machine ownership?
Additional issues are:
• Electronic surveillance.
• Ethics in business intelligence (BI) and AI systems design.
• Software piracy.
• Invasion of individuals’ privacy.
• Use of proprietary databases and knowledge bases.
• Use of personal intellectual property such as knowledge and expertise for the benefits of
companies and the payment to the contributors.
• Accuracy of data, information, and knowledge.
• Protection of the rights of users.
• Accessibility to information by AI users.
• The amount of decision making to delegate to intelligent machines.
• How AI can fail due to inappropriate ethics.
• The ethics of legal analytics (Goldman, 2018).
Other Topics in Intelligent Systems Ethics
• Machine ethics is a part of the ethics of AI that is concerned with the moral behavior of
artificially intelligent beings (per Wikipedia; see details there).
• Robotics is concerned with the moral behavior of designers, builders, and users of robots.
• Microsoft’s Tay chatbot was closed due to its inability to understand many irrelevant and
offending comments.
• Some are afraid that algorithm-based technologies, including AI, may become rac-ists. We
discuss this topic in Section 14.8. Also, see Clozel (2017).
• According to Spangler (2017), self-driving cars may one day face a decision of whom to
save and whom to kill.
• Voice technologies enable the identification of callers to AI machines. This may be great
on one hand, but it creates privacy concerns on the other.
• One area in which there are considerable ethical concerns (frequently combined with legal
concerns) is the healthcare/medical field. Given the large efforts by Alphabet and IBM
Watson initiatives, this is not surprising. For a discussion, see Bloomberg News (2017).
For comprehensive coverage of ethical issues in big data and data sharing, see Anon (2017).
For principles for Big Data analysis, see Kassner (2017).
COMPUTER ETHICS IN GENERAL Computer ethics focuses on the behavior of people toward
information systems and computers in general. The study of ethics in intelligent systems is strongly
related to the ethics of computers and information systems in general. The following are some
resources.
THE TEN COMMANDMENTS OF COMPUTER ETHICS This well-known document is published by
cybercitizenship (cybercitizenship.org/ethics/commandments.html).
1. Thou shalt not use a computer to harm other people.
2. Thou shalt not interfere with other people’s computer work.
3. Thou shalt not snoop around in other people’s files.
4. Thou shalt not use a computer to steal.
5. Thou shalt not use a computer to bear false witness.
6. Thou shalt not use or copy software for which you have not paid.
174 Part V • Caveats of Analytics and AI
7. Thou shalt not use other people’s computer resources without authorization.
8. Thou shalt not appropriate other people’s intellectual output.
9. Thou shalt not think about the social consequences of the program you write.
10. Thou shalt not use a computer in ways that show consideration and respect.
A major upcoming issue is that of ethics for autonomous vehicles. For example, who will
develop them, how will they be programmed into the vehicles, and how will they be enforced? See
Sharma (2017).
For review of ethical issue considerations in information research literature, see
nowpublishers.com/article/Details/ISY-012/.
MIT Media Lab and the Center for Internet & Society at Harvard University manage an
initiative to research ethical and governance topics in AI. SAS, a major analytical and AI vendor,
proposed three essential steps for AI ethics as described in sas.com/en_us/
insights/articles/analytics/artificial-intelligence-ethics.html/.
u SECTION 14.3 REVIEW QUESTIONS
1. List some legal issues of intelligent systems.
2. Describe privacy concerns in intelligent systems.
3. In your view, who should own the data about your use of a car? Why?
4. List ethical issues in intelligent systems.
5. What are the 10 commandments of computer/information systems?
14.4 SUCCESSFUL DEPLOYMENT OF INTELLIGENT SYSTEMS Many experts, consultants, and researchers provide suggestions regarding intelligent systems’
successful deployment. Given the importance of the topic, it is clear that companies need to get
ready for the mass arrival of AI and other intelligent technologies. Here are some topics related to
deployment strategy:
• When to embark on intelligent projects and how to prioritize them.
• How to decide whether to do it yourself or use partners, or to outsource.
• How to justify investments in intelligent projects.
• How to overcome employees’ resistance (e.g., fear of job loss).
• How to arrange appropriate people-robot teams.
• How to determine which decisions to fully automate by AI.
• How to protect intelligent systems (security) and how to protect privacy.
• How to handle possible loss of jobs and retraining of employees (Section 14.5).
• How to determine whether you have the necessary up-to-date technology.
• How to decide what support top management should provide.
• How to integrate the system with business processes.
• How to find qualified personnel for building and using intelligent systems.
For more strategy issues, see Kiron (2017). We cover only several topics in this section and provide
references to more. Most of the implementation topics are generic in nature and will not be covered
here.
Top Management and Implementation
According to Chui et al. (2017), from McKinsey & Company, “Senior executives need to understand
the tactical as well as the strategic opportunities (of AI), redesign their organizations, and commit
to helping shape and debate about the future of work.” Specifically, the executives need to plan for
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 175
integrating intelligent systems into their workplace, making a commitment to conduct a participating
environment for the changes and provide sufficient resources. Snyder (2017) claims that many
executives know that intelligent systems will change their business, but they do not do much about
it.
KPMG, a large management service consultant, provides the following steps regarding digital
labor:
“KPMG’s holistic approach-from strategy through execution will assist companies on each
step of implementation. The steps are:
• Establishing priority areas for technological innovation.
• Developing a strategy and a plan for the employees.
• Identify providers and partners for plans’ execution.
• Establishing a strategy and plans to realize benefits from the digital labor initiatives.”
Source: KPMG Internal Audit: Top 10 in 2018, Considerations for impactful internal audit
departments, © 2018 KPMG LLP.
A complete guide for KPMG is provided by Kiron (2017). It includes robotic process automation,
enhanced process automation, and cognitive automation. For issues regarding leadership in
implementation, see Ainsworth (2017).
System Development Implementation Issues
Since AI and business analytics are broad terms, describing several technologies whose maturity levels
vary, implementation issues may vary considerably. Shchutskaya (2017) cites the following three
major problems:
1. Development approach. Business analytic and AI systems require an approach different from that
of other IT/computer systems. Specifically, it is necessary to identify and deal with different
and frequently large data sources (see the opening vignette to Chapters 1 and 2). It is necessary
to cleanse and curate these data. Also, if learning is involved, one needs to use machine
training. Thus, special methodologies are needed.
2. Learning from data. Many AI and business analytics involve learning. The quality of the input
data determines the quality of the applications. Also, the learning mechanism is important.
Therefore, data accuracy is critical. In learning, systems must be able to deal with changing
environmental conditions. Data should be organized in databases, not in files.
3. No clear view is available of how insights are generated. AI, IoT, and business analytic systems generate
insights, conclusions, and recommendations based on the analysis of the data collected. Given
that data are frequently collected by sensors and there are different types of them, we may not
have a clear view of the insights that are generated.
Related important areas include problems with Big Data, ineffective information access, and limited
integration capabilities (discussed next).
Connectivity and Integration
As part of the development process, it is necessary to connect the AI and analytic applications to
existing IT systems, including the Internet, and other intelligent systems.
Example
The Australian government commissioned Microsoft in August 2017 to build hyperscale cloud
regions to unlock the power of intelligent technologies. The system is expected to dramatically
modernize how the government processes data and delivers services to its citizens. The system can
176 Part V • Caveats of Analytics and AI
handle both unclassified and protected data. The infrastructure is built inside, or near, the
government data centers. The system will enable the government to use innovative applications
based on machine learning, bots, and language translation, and it will improve healthcare, education,
social services, and other government operations. Finally, the system will increase both security and
privacy protection.
Integration needs to be done with almost every system that is being impacted by AI or business
analytic. For example, it is necessary to integrate intelligent applications both to a digital marketing
strategy and to marketing implementation. For a discussion, see
searchenginejournal.com/artificial-intelligence-marketing/200852/.
To overcome the integration difficulty, Huawei of China (a cellphone producer) is installing
an AI system with its knowledge base inside the chips of its products. Other phones’ manufacturers
rely on connecting to the “cloud” to interact there with AI knowledge. For the implications on IoT
connectivity, see Rainie and Anderson (2017).
For considerations regarding IoT connectivity providers, see Baroudy et al. (2018).
Security Protection
Many intelligent applications are managed and updated in the “cloud” and/or c onnected to the
regular Internet. Unfortunately, by adding Internet connection, new vulnerabilities may be created.
Hackers use intelligent technologies to identify these vulnerabilities. For how criminals use AI and
related issues, see Crosman (2017). In Section 14.7, we discuss the potential dangers of robotics.
The safety of passengers in self-driving cars and others who may be involved in collisions with the
self-driving cars is an important safety issue as well. Also, the safety of people working near robots
has been researched for many decades. In addition, hacking robots, chatbots, and other intelligent
systems are areas that require attention. Finally, the safety of robots themselves when they work on
the streets is an issue. Some people attack them (see McFarland, 2017a and the video there).
Leveraging Intelligent Systems in Business
There are many ways to leverage intelligent systems, depending on the nature of the applications.
Catliff (2017) suggests the following ways to do this, leveraging the intelligent technology capabilities
to increase efficiency and provide more customer care.
Specifically, he suggested:
1. Customize the customer experience (e.g., for interactions with customers).
2. Increase customer engagement (e.g., via chatbots).
3. Use intelligent technologies to detect problems and anomalies in data.
Singh (2017a) recommends the following as critical success factors: discover, predict, justify,
and learn from experience. Ross (2017) raised the issue of the need to upgrade employees’ skills and
build an empowered AI-savvy workforce. One of the most important issues is how to handle the
fear of job loss of employees. This is discussed in Section 14.6.
Intelligent System Adoption
Most of the issues related to intelligent systems’ adoption are the same as or similar to that of any
information systems. For example, employees may resist change, management may not provide
sufficient resources, there could be a lack of planning and coordination, and so on. To deal with
such issues, Morgan Stanley drew ideas from hundreds of conversations with experts (see
DiCamillo, 2018). One important issue is to have an appropriate deployment and adoption strategy
that should work in harmony with the implemented technologies and the people involved. In
general, the generic adoption approach to information systems should work here, too.
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 177
u SECTION 14.4 REVIEW QUESTIONS
1. Describe the systems deployment process.
2. Discuss the role of top management in deploying intelligent systems.
3. Why is connectivity such an important issue?
4. Describe system development issues.
5. Discuss the importance of security and safety, and how to protect them.
6. Describe some issues in intelligent systems adoption.
14.5 IMPACTS OF INTELLIGENT SYSTEMS ON ORGANIZATIONS Intelligent systems are important components in the information and knowledge revolution. Unlike
the slower revolutions of the past, such as the Industrial Revolution, this revolution is taking place
very rapidly and affecting every facet of our work and lives. Inherent in this transformation is the
impact on organizations, industries, and managers, some of which are described in this section.
Separating the impact of intelligent systems from that of other computerized systems is a
difficult task, especially because of the trend toward integrating, or even embedding, intelligent
systems with other computer-based information systems. Intelligent systems can have both micro-
and macro implications. Such systems can affect particular individuals and jobs as well as the work
and structures of departments and units within an organization. They can also have significant long-
term effects on total organizational structures, entire industries, communities, and society as a whole
(i.e., regarding macro impact, see Sections 14.6 and 14.7).
Explosive growth in analytics, AI, and cognitive computing is going to have a major impact
on the future of organizations. The impact of computers and intelligent systems can be divided into
three general categories: organizational, individual, and societal. In each of these, computers may
have many possible impacts. We cannot possibly consider all of them in this book, so in the next
paragraphs we cover topics we feel are most relevant to intelligent systems and organizations.
New Organizational Units and Their Management
One change in organizational structure is the possibility of creating an analytics department, a BI
department, a data science department, and/or an AI department in which analytics plays a major
role. Such special units (of any type) can be combined with or replace a quantitative analysis unit,
or it can be a completely new entity. Some large corporations have separate decision support units
or departments. For example, many major banks have such departments in their financial services
divisions. Many companies have small data science or BI/data warehouse units. These types of
departments are usually involved in training in addition to consulting and application development
activities. Others have empowered a chief technology officer over BI, intelligent systems, and e-
commerce applications. Companies such as Target and Walmart have major investments in such
units, which are constantly analyzing their data to determine the efficiency of marketing and supply
chain management by understanding their customer and supplier interactions. On the other hand,
many companies are embedding analytics/data science specialties within functional areas such as
marketing, finance, and operations. In general, this is one area where considerable job opportunities
currently exist. For a discussion of the need for a chief data officer, see Weldon (2018). Also, the
need for a chief AI officer is discussed by Lawson (2017).
Growth of the BI and analytics has resulted in the formation of new units within IT
companies as well. For example, a few years ago, IBM formed a new business unit focused on
analytics. This group includes units in BI, optimization models, data mining, and business
performance. More importantly, the group is focused not just on software but also significantly
more on services/consulting.
Transforming Businesses and Increasing Competitive Advantage
178 Part V • Caveats of Analytics and AI
One of the major impacts of intelligent systems is the transformation of businesses to digital ones.
While such transformation has been going on with other information technologies for years, it has
accelerated with intelligent technologies, mostly with AI.
In many cases, AI is only a supportive tool for humans. However, as AI has become more
capable, machines have been able to perform more tasks by themselves or with people. The fact is
that AI already is transforming some businesses. As seen in Chapter 2, AI already is changing all
business functional areas, especially marketing and finance. The impact ranges from full automation
of many tasks, including managerial ones, to an increase in human-machine collaboration (Chapter
11). A comprehensive description of how AI is driving digital transformation is provided by
Daugherty and Wilson (2018), who concluded that businesses that will miss the AI-driven
transformation would be in a competitive disadvantage. Batra et al. (2018) point to a similar
phenomenon and urge companies to use AI and utilize it for a wave of innovations. For more on
this topic, see Uzialko (2017).
USING INTELLIGENT SYSTEMS TO GAIN COMPETITIVE ADVANTAGE Use of intelligent technologies, and
especially AI, is evidenced in many cases. For example, using robots, Amazon.com enabled the
company to reduce cost and control online commerce. In general, by cutting costs, increasing
customer experiences, improving quality, and speeding deliveries, companies will gain competitive
advantage. Rikert (2017) describes conversations with CEOs about how AI and machine learning
can beat the competitors. Andronic (2017) points to competitive advantage. The benefits include
generating more demand (see Chapter 2), automating sales (Chapter 2), and identifying sales
opportunities.
An important recent factor is the fact that new companies and blurring sector borders are
influencing the competitive picture of many industries. For example, autonomous vehicles will
impact the competition in the automotive industry.
According to Weldon (2017c), a smart use of analytics offers top
competitive advantage. The author provides advice on how organizations
can get the full benefits from analytics. An example of how 1-800-
Flowers.com is using analytics, AI, and other intelligent technologies to
gain a competitive advantage is provided in Application Case 14.1.
Application Case 14.1 How 1-800-Flowers.com Uses Intelligent Systems for Competitive Advantage
1-800-Flowers.com is a leading online retailer of flowers
and gifts. The company moved from telephone to online
ordering in the mid-1990s. Since then, it has grown to
over $1 billion in revenue and over 4,000 employees,
despite fierce competition. In a world dominated by
online giants such as Amazon.com and Walmart.com,
and hundreds of other companies that sell online flowers
and gifts, survival is not easy.
The company is using the following three key
strategies:
• Enhancing the customer experience.
• Driving demand more efficiently.
• Building a workforce that supports the p roducts
and technology innovation (culture of innovation).
The company has been using intelligent technologies
extensively to build a superb supply chain and to facilitate
collaboration. Lately, it started to use intelligent systems
to enhance its competitive strategies. Here are several
technologies covered in this book that the company uses.
1. Optimal customers experience. Using SAS Marketing
Automation and Data Management products, the
retailer collects information regarding customers’
needs and analyzes it. This information enables
senders of flowers and gifts to find perfect gifts for
any occasion. Senders want to make recipients happy,
so appropriate recommendations are critical. The
company uses advanced analytics and data mining
from SAS to anticipate customers’ needs. 1-800-
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 179
Flowers.com marketers can then communicate with
customers more effectively. Using the newest tools,
company data scientists and marketing analysts mine
data more efficiently. Today customer expectations
are higher than ever because it is much easier for
customers to compare vendors’ offerings online.
Analytics and AI enable the company to understand
its customers’ sentiments. Now the company is able
to understand the emotional reasoning behavior for
purchasing decisions and c ustomer loyalty. This
change results in product recommendations
described later.
2. Chatbots. 1-800-Flowers.com has a bot on Facebook
Messenger. As described in Chapter 12, such a bot
can be useful as a source of information and as a
vehicle for conversation. The company also offers
chat on its Web site online, and chat using voice. In
addition, mobile shoppers can use Google Assistant
for voice ordering. The company also offers voice-
enabled Alexa with its “one-shot intent” to expedite
ordering.
3. Customer service. The company offers a portal and one-
stop shopping similar to what Amazon.com offers,
and self-service payment is available. The same
capability is available when shopping with the
company’s bot on Facebook Messenger. Customers
do not have to leave Facebook to complete an order.
4. AI-based recommendation. As you may recall from
Chapter 12, e-commerce retailers excel by providing
product recommendation (e.g., Amazon, Netflix). 1-
800-Flowers.com is doing the same thing, offering
recommendation and advice on gifts from their
brand’s websites (e.g., Harry and David). The
recommendations are generated by IBM’s Watson
and are offered as a “cognitive concierge,” making
online shopping feel as having an in-store experience.
This AI-based service is known as GWYN (Gifts
When You Need) at 1-800-flowers. Watson’s natural
language processing (NLP) enables easy s hopper-
machine conversations.
5. Personalization. SAS advanced analytics enables the
company’s marketing department to segment
customers into groups with similar characteristics.
Then the company can send
promotions targeted to the profile of each segment.
In addition to e-mails, special campaigns are
arranged. Based on the feedback, the company can
plan and revise marketing strategy. SAS also helps
the company to analyze the “likes” and “dislikes” of
the customers. All-inall, the intelligent systems help
the company and its customers to make informed
decisions.
Questions for Case 14.1
1. Why it is necessary to provide better customer experience today?
2. Why do data need sophisticated analytical tools?
3. Read the “Key benefit of SAS Marketing Automation.”
Which benefits do you think are used by 1-800-
Flowers.com and why?
4. Relate IBM Watson to “personalization.”
5. Relate ‘SAS Advanced Analytics’ capabilities to their
use in this case.
6. ‘SAS Enterprise Miner’ is used to do data mining. Explain what is done and how.
7. SAS has a product called ‘Enterprise Guide’ that 1-
800-Flowers.com uses. Find how it is used based on
the tools’ capabilities.
Sources: Compiled from J. Keenan. (2018, February 13). “1-800-
Flowers.com Using Technology to Win Customers’ Hearts This
Valentine’s Day.” Total Retail; S. Gaudin. (2016, October 26). “1-800-
Flowers Wants to Transform Its Business with A.I.” Computer World; SAS.
(n.d.). “Customer Loyalty Blossoms with Analytics.” SAS Publication,
sas.com/en_us/customers/1-800flowers.html/ (accessed July 2018).
Redesign of an Organization Through the Use of Analytics
An emerging area of research and practice is employing data science technologies for studying organizational dynamics,
personnel behavior, and redesigning the organization to better achieve its goals. Indeed, such analytics applications are
known as People Analytics. For example, analytics are used by HR departments to identify ideal candidates from the pool
that submits resumes to the organization or even from broader pools such as LinkedIn. Note that with AI and analytics,
180 Part V • Caveats of Analytics and AI
managers will be able to have a larger span of control due, for example, to the advice managers and employees can get from
virtual assistants. The increased span of control could result in flatter organizational structures. Also, managers’ job
descriptions may have to change.
A more interesting and recent application area relates to understanding employee behavior by monitoring their
movements within the organization and using that information to redesign the layout or teams to achieve better
performance. A company called Humanyze (previously known as Sociometric Solutions) has badges that include a GPS
and a sensor. When employees wear these badges, all of their movement is recorded. Humanyze has reportedly been able
to assist companies in predicting which types of employees are likely to stay with the company or leave on the basis of their
interactions with other employees. For example, those employees who stay in their own cubicles are less likely to progress
up the corporate ladder than those who move about and interact with other employees extensively. Similar data collection
and analysis have helped other companies determine the size of conference rooms needed or even the office layout to
maximize efficiency. According to Humanyze’s Web site, one company wanted to better understand characteristics of its
leaders. By analyzing the data from these badges, the company was able to recognize that the successful leaders indeed have
larger networks with which they interact, spend more time interacting with others, and are also physically active. The
information gathered across team leaders was used to redesign the work space and help improve other leaders’ performance.
Clearly, this may raise privacy issues, but within an organization, such studies may be acceptable. Humanyze’s Web site has
several other interesting case studies that offer examples of how Big Data technologies can be used to develop more
efficient team structures and organizational design.
Intelligent Systems’ Impact on Managers’ Activities, Performance, and Job Satisfaction
Although many jobs may be substantially enriched by intelligent technologies, other jobs may become more routine and
less satisfying. Some claim that computer-based information systems in general may reduce managerial discretion in
decision making and lead managers to be dissatisfied. However, studies of automated decision systems found that
employees using such systems, especially those who are empowered by the systems, were more satisfied with their jobs. If
using an AI system can do routine and mundane work, then it should free managers and knowledge workers to do more
challenging tasks.
The most important task of managers is making decisions. Intelligent technologies can change the manner in which
many decisions are made and can consequently change managers’ job responsibilities. For example, some researchers found
that a decision support system improved the performance of both existing and new managers as well as other employees.
It helped managers gain more knowledge, experience, and expertise and consequently enhanced the quality of their decision
making. Many managers report that intelligent systems have finally given them time to get out of the office and into the
field. They have also found that they can spend more time planning activities instead of putting out fires because they can
be alerted to potential problems well in advance thanks to intelligent system technologies (see the opening vignette, Chapter
1).
Another aspect of the managerial challenge lies in the ability of intelligent technologies to support the decision-
making process in general and strategic planning and control decisions in particular. Intelligent systems could change the
decision-making process and even decision-making styles. For example, information gathering for decision making is
completed much more quickly when algorithms are in use. Research indicates that most managers tend to work on a large
number of problems simultaneously, moving from one to another as they wait for more information on their current
problem. Intelligent technologies tend to reduce the time required to complete tasks in the decision-making process and
eliminate some of the nonproductive waiting time by providing knowledge and information. The following are some
potential impacts of intelligent system on managers’ jobs:
• Less expertise (experience) is required for making many decisions.
• Faster decision making is possible because of the availability of information and the automation of some phases in
the decision-making process (see Chapters 2 and 11).
• Less reliance on experts and analysts is required to provide support to top manag-ers and executives. Today, they
can decide by themselves with the help of intelligent systems.
• Power is being redistributed among managers. (The more information and analysis capability they possess, the more
power they have.)
• Support for complex decisions makes solutions faster to develop and of better quality.
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 181
• Information needed for high-level decision making is expedited or even self-generated.
• Automation of routine decisions or phases in the decision-making process (e.g., for frontline decision making and
using automated decision making) may eliminate some managers.
Source: Decision Support And Business Intelligence Systems, Pearson Education India, 2008.
In general, it has been found that the job of middle managers is the most likely job to be automated. Midlevel
managers make fairly routine decisions, which can be fully automated. Managers at lower levels do not spend much time
on decision making.
Instead, they supervise, train, and motivate nonmanagers. Some of their routine decisions, such as scheduling, can be
automated; other decisions that involve cognitive aspects may not be automated. However, even if managers’ decisional
role is completely automated, many of their other activities could not be automated or could only be partially automated.
Impact on Decision Making
Throughout the book, we illustrate how intelligent technologies improve or automate d ecision making. These technologies,
of course, will impact managers’ job. One aspect is the impact of intelligent technologies supported by the “cloud.” An
example is illustrated in Chapter 9, Figure 9.12. It illustrates the flow of data from data sources and services via an
information service to analytical services for different types of decision making supported by analytics.
Uzialko (2017) describes how humans can use AI to predict and analyze the consequences of different potential
solutions, streamlining the decision-making process. Also, by using machine learning and deep learning, more decisions
can be automated.
One impact of intelligent systems is to support real-time decision making. A popular tool for doing just that is SAS®
Decision Manager, which is described in Technology Insights 14.1.
TECHNOLOGY INSIGHT 14.1 SAS Decision Manager
SAS Real-Time Decision Manager (RTDM) is an analytics-based integrated product that is designed to support real-time decision
making, which is necessary for helping companies respond to rapidly changing marketing, customers’ demands, technology, and other
business environments. SAS answers the following questions:
1. What does SAS RTDM do? It combines SAS analytics with business logic and contact strategies to deliver enhanced real-
time recommendations and decisions to interactive customer channels, such as Web sites, call centers, point of sales (POS)
locations, and automated teller machines (ATMs).
2. Why is SAS RTDM important? It helps you make smarter decisions by automating and applying analytics to the decision
process during real-time customer interactions. By successfully meeting each customer’s specific needs at the right time, the
right place, and in the right context, your business can become more profitable.
3. For whom is SAS® RTDM designed? It provides distinct capabilities for marketers who define communication strategies,
executives who need reports on marketing effectiveness, business analysts who model and predict customer behavior, and
campaign managers who create target customer segments.
The following are the key benefits of RTDM:
• Makes the right decisions every time, all the time. • Realizes customer needs with the right offer, at the right time, in the right channel. • Better allocates valuable IT resources.
The key features according to SAS Inc. are:
• Real-time analytics. • Rapid decision process construction. • Enterprise data throughout. • Campaign testing. • Automated self-learning analytical process. • Connectivity.
182 Part V • Caveats of Analytics and AI
For the details, visit “SAS Real-Time Decision Manager” and read the text there. Also you can download a white paper about RTDM
there.
DisCussion Questions
1. What improvements to the decision-making process are made by SAS RTDM?
2. What SAS products are embedded or connected to RTDM? (You need to read the Web site’s details.)
3. Relate the product to product recommendation capability.
Source: SAS® Real-Time Decision Manager Make context-based marketing decisions during your real-time customer interactions. Copyright © 2018
SAS Institute Inc., Cary, NC, USA. All Rights Reserved. Used with permission.
Industrial Restructuring
A few authors have begun to speculate on the impact of AI, analytics, and cognitive computing on the future of industry.
A few interesting resources to consult are Autor (2016), Ransbotham (2016), a special report by The Economist (Standage,
2016), and a book by Brynjolfsson and McAfee (2016). The report by The Economist is quite comprehensive and considers
many dimensions of the impact of the current developments on industry and society. The main arguments are that
technology is now enabling more and more tasks that were done by humans using computers. Automating work, of course,
has happened before, since the time of the Industrial Revolution. What makes the change this time around significantly
more far reaching is that the technology is enabling many cognitive tasks to be done by machines. And the speed of change
is so radical that the likely impact on organizations and society will be very significant and at times unpredictable. These
authors do not agree in their predictions, of course. Let us focus first on the organizational impacts. Ransbotham (2016)
argues that cognitive computing will convert many jobs done by humans to be done by computers, thus reducing costs for
organizations. The quality of output may increase as well in cognitive work, which has been shown in several studies that
compare a human’s performance with a machine. Everyone is aware of IBM Watson having won in Jeopardy! or Google’s
system winning in the game of GO against human champions. But many other studies in specific domains such as speech
recognition and medical image interpretation have also shown similar superiority of automated systems when the task is
highly specialized yet routine or repetitive. Also, because machines tend to be available at all hours and at all locations, an
organization’s reach may increase, resulting in easier scaling and thus greater competition among organizations. These
organizational impacts mean that yesterday’s top organizations may not remain at the top forever because cognitive
computing and automation can challenge established players. This is the case in the automotive industry. Although
traditional car companies are trying quickly to catch up, Google, Tesla, and other technology companies are disrupting
industry structure by challenging the leaders of the automotive age. Analytics and AI are empowering many of these
changes.
u SECTION 14.5 REVIEW QUESTIONS
1. List the impacts of intelligent systems on managerial tasks.
2. Describe new organizational units that are created because of intelligent systems.
3. Identify examples of analytics and AI applications used to redesign workspace or team behavior.
4. How is cognitive computing affecting industry structure and competition?
5. Describe the impacts of intelligent systems on competition.
6. Discuss the impact of intelligent systems on decision making.
14.6 IMPACTS ON JOBS AND WORK One of the most discussed and debated topics in considering the impacts of intelligent systems is on jobs and work. There
is a general agreement that:
• Intelligent systems will create many new jobs as automation always has.
• There will be a need to retrain many people.
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 183
• The nature of work will be changed.
The discussions, debates, and disagreements relating to the issues of when, how much, and how to deal with these
phenomena occupy many researchers and are the topics of this section.
An Overview
According to Ransbotham (2016), financial advising is typically considered a knowledgeintensive task. As robot (robo)
advisors provide personalized support for individuals, the costs of such services go down. This leads more people to
demand such services, eventually freeing more humans to address advanced financial issues. Robo advisors may also cause
some people to lose their jobs.
Some authors argue that the automation segment, which is related to cognitive computing and AI, will accelerate
what is called polarization of the labor market in the future. This entails significant job growth in the top and bottom tiers of
the labor market but losses in the middle. Jobs requiring low but specialized skills, such as personal care, are continuing to
grow. Similarly, jobs that require very high skill, such as graphics design work, and so on, are also growing. But jobs that
require “middle skills” such as specialized knowledge that is applied over and over with some adaptation, are at the greatest
risk of disappearing. Sometimes technology disintermediates itself! For example, IBM Watson Analytics now includes
querying capabilities to begin asking questions that an intelligent system professional previously asked and, obviously,
providing answers. Other analytics-as-a service offerings with similar services may result in a need for fewer people to be
proficient at using analytics software.
A report by The Economist notes that even if AI does not replace workers directly, it will certainly require employees
to acquire new skills to keep their jobs. Market disruption is always uncomfortable. The next few years will provide excellent
opportunities for intelligent technology professionals to shape the future.
Are Intelligent Systems Going to Take Jobs—My Job?
Tesla’s Elon Musk envisions AI-based autonomous driving trucks all over the world within 10 years. There will be convoys
of such trucks, each of which will follow a lead truck. Trucks will be electrical, economical, and pollution free. In addition,
there will be fewer accidents—sounds great! But what about thousands of drivers who will lose their jobs? What about
many thousands of employees in truck stops who will lose their jobs as well? The same scenario could happen in many
other industries. Amazon has opened its first Go, a cashierless physical store. They plan 3,000 more in a few years. The
post office in some countries already distributes mail using autonomous vehicles. In short, there is a chance for massive
unemployment.
Example: Pilots at FedEx
FedEx has a fleet of close to 1,000 airplanes flying globally. According to Frank Tode, editor and publisher of The Robot
Report, FedEx hopes that around 2020 the company will have one global pilot center with three or four pilots who will
operate the entire FedEx fleet.
Foxcom, an iPhone manufacturer in Taiwan, had planned to replace almost all of its employees
(60,000) in Taiwan with robots (Botton, 2016). The company already produced 10,000 robots for
this purpose.
INTELLIGENT SYSTEMS MAY CREATE MASSIVE JOB LOSSES The debate regarding technology taking jobs
has been going on since the beginning of the industrial revolution.
The issue regarding intelligent systems is strongly debated now due to the following:
• They are moving very fast.
• They may take a large variety of jobs, including many white-collar and nonphysi-cal jobs.
• Their comparative advantage over manual labor is very large and growing rapidly (see Figure
2.2 in Chapter 2).
184 Part V • Caveats of Analytics and AI
• They are already taking some professional jobs from financial advisors, paralegals, and
medical specialists.
• The capabilities of AI are growing rapidly.
• In Russia, robots are already teaching mathematics in schools (some do a better job than
humans). Just think about what could happen to the teaching profession.
AI Puts Many Jobs at Risk
For the potential impact of AI on jobs, see Dormehl (2017), who explores the possibility of creative
intelligent machines. For example, McKinsey’s study estimates that AI is poised to take over 30
percent of all bank jobs in the near future. The study also predicts that robots will take 800 million
jobs worldwide by 2030 (Information Management News, 2017).
To research the potential danger of job loss, McKinsey & Company divided jobs into 2,000
distinct work activities, such as greeting customers and answering questions about products, which
retail salespeople do. Its researchers (see Chui et al., 2015) found that 45 percent of all 2,000 activities
could be economically and physically automated. The activities include physical, cognitive, and social
types.
While autonomous vehicles are not taking jobs, yet, they will take jobs from taxi d rivers,
Uber, and similar companies’ drivers. Also, bus drivers may lose their jobs. Other jobs that have
already been replaced by intelligent systems are listed in Application Case 14.2.
Application Case 14.2 White-Collar Jobs That Robots Have Already Taken While it may be sometime before FedEx will have pilotless
airplanes and schools will have no human teachers, some
jobs, according to Sherman (2015), have already been
taken by robots. They include:
• Online marketers. Using NLP, companies are
automatically developing marketing ads and e-mails
that influence people to buy (robo marketers). These
are based on a dialog with potential buyers and on an
automatic database search of historical cases. “Who
needs an online marketer that may have inferior,
biased, or incomplete knowledge?”
• Financial analysts and advisors. As was
described in Chapter 12, robo advisors are all over
the scene. Equipped with the ability to deal with
Big Data in real time and conduct predictive
analysis in seconds, these programs are liked by
investors who pay about one-tenth of what human
advisors charge. Furthermore, robo advisors can
personalize recommendations.
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 185
• Anesthesiologists, diagnosticians, and
surgeons. The medical field seems to be immune from AI. This is not the case. Expert systems for
diagnosis have been in place for about 40 years. The
FDA has already approved the J&J Sedasys system
for delivery of low-level anesthesia in surgeries, such
as colonoscopies. IBM’s Watson has demonstrated a
far more accurate diagnosis in lung disease cases
than humans (90% vs. 50%). Finally, surgeons
already use automated machines in some invasive
procedures.
• Financial and sports reporters. These jobs involve gathering information, interviewing people,
answering questions, analyzing the material, and
writing reports. The Associated Press (AP) has
experimented with AI machines since 2014. Results
so far are virtually error and bias free (and no fake
news!).
Palmer (2017) reported an additional five jobs in
danger, including middle management, commodity
salespeople, report writers, accountants and bookkeepers,
and some types of doctors.
McFarland (2017b) lists as high-risk jobs cashiers, toll
booth operators, fast-food employees,
and drivers. Low-risk jobs include nurses, doctors, dentists,
youth sport coaches, and social workers.
Questions for Case 14.2
1. Watch the 4:22 min. video about an interview with Palmer, at linkedin.com/pulse/5-jobsrobots-take-
first-shelly-palmer/. Discuss some of the assertions
made regarding doctors.
2. Discuss the possibility of your checkup by a robot-
diagnostician. How would you feel?
3. With the bombardment of fake news and their biased
creators, it may be wise to replace all of them by
intelligent machines. Discuss such a possibility.
4. You are a defendant in a crime you did not commit. Would you prefer a traditional lawyer or one equipped
with an AI e-discovery machine? Why?
Sources: Compiled from E. Sherman. (2015, February 25). “5 White-Collar
Jobs Robots Already Have Taken.” Fortune.com.
fortune.com/2015/02/25/5-jobs-that-robots-already-aretaking
(accessed April 2018); S. Palmer. (2017, February 26). “The 5 Jobs Robots Will Take First.” Shelly Palmer.
Let us look at some other studies. A 2016 study done in the United Kingdom predicted that
robots will take 50 percent of all jobs by 2026. Egan (2015) reports that robots already threaten the
following jobs: marketers, toll booth operators and cashiers, customer service, financial brokers,
journalists, lawyers, and phone workers. Note that automation may affect portions of almost all jobs
to a greater or lesser degree. Experts estimate that about 80 percent of IT jobs may be eliminated
by AI.
According to Manyika et al. (2017), automation is spreading because “robots are also
increasingly capable of accomplishing activities that include cognitive capabilities once considered
too difficult to automate successfully, such as making tacit judgments, sensing emotion, or even
driving.”
Given all this, you may wonder whether your job is at risk.
Which Jobs Are Most in Danger? Which Ones Are Safe?
If want to know about your job, it obviously depends on the type of job you are holding. Oxford
University in the United Kingdom looked at 700 jobs and ranked them from zero (no risk of
automation) to 1 (very high risk of automation). Straus (2014) provided a list of the top 100 most
at-risk jobs (all above 0.95) and the 100 jobs with the lowest risk (with 0.02 or less). The top 10
“safe” and the 10 at risk are listed in Table 14.1.
A 2017 study conducted by the Bank of England found that almost half of the U.K. jobs (15
million out of 33.7 million) are at risk of loss within 20 years. Creative robots are the greatest threat
because they can learn and increase their capabilities. While in the past, automation may not have
decreased the total number of jobs, this time the situation may be different.
A side effect of this situation may be that workers will have less income while the owners of
robots will have a larger income. (This is why Bill Gates suggested taxing the robots and their
owners.)
186 Part V • Caveats of Analytics and AI
TABLE 14.1 Ten Top Safe and at Risk Occupations
Probability of Job Loss
0.0036 First-Line supervisors of firefighting and prevention workers
0.0036 Oral and maxillofacial surgeons
0.0035 Healthcare social workers
0.0035 Orthotists and prosthetists
0.0033 Audiologists
0.0031 Mental health and substance abuse social workers
0.0030 Emergency management directors
0.0030 First-Line supervisors of mechanics, installers, and repairers
0.0028 Recreational therapists
High-Risk Jobs
0.99 Telemarketers
0.99 Title examiners, abstractors, and searchers
0.99 Sewers, hand
0.99 Mathematical technicians
0.99 Insurance underwriters
0.99 Watch repairer
0.99 Cargo and freight agents
0.99 Tax preparers
0.99 Photographic process workers and processing machine operators
0.99 New account clerks
Source: Based on Straus (2014) Straus, R.R. “Will You Be Replaced by a Robot? We Reveal the 100 Occupations Judged Most and Least at
Risk of Automation.” ThisisMoney.com, May 31, 2014. thisismoney.co.uk/money/news/article-2642880/ Table-700-jobs-reveals-
professions-likely-replaced-robots.html
SOME MORE JOB LOSSES OBSERVATIONS
• Kelly (2018) predicts that robots could eliminate many Las Vegas jobs. And indeed, in many
casinos worldwide, you can play several traditional games on machines.
• People with doctoral degrees have a 13 percent chance of being replaced by robots and AI
versus 74 percent for those with only a high school education (Kelly, 2018).
• Women will lose more jobs to automation than men (Krauth, 2018).
Intelligent Systems May Actually Add Jobs
Despite the fear, uncertainty, and panic related to job losses, many reports contradict this. Here are
some examples: de Vos (2018) reported that AI will create 2.3 million jobs in 2020 while eliminating
1.8 million. Also, one needs to consider the great benefits of AI and the fact that human and
machine intelligence will complement each other in many jobs. Also, AI will increase international
trade, adding more jobs. de Vos also cites studies that show the creation of jobs due to equipment
maintenance and service that cannot be automated. The following are predictions on both sides of
the issue:
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 187
• A PricewaterhouseCoopers (PwC) study forecast that robots will bolster U.K. eco-nomic
growth. So, even though robots could destroy about 7 million jobs in the United Kingdom,
they will create at least 7 million new jobs and probably more over 20 years (Burden, 2018).
• IBM’s new deep learning service may help save IT jobs.
• There is a shortage of millions of skilled workers (e.g., about 50,000 truck drivers in the United
States), so automation will reduce millions of unfilled positions.
• Korolov (2016) claims that there is plenty of work, especially for people who keep up with
technology and broaden their skills.
• Gartner Inc. predicts that by 2020, AI will create more jobs than it eliminates Singh, (2017b).
• Wilson et al. (2017) report on new categories of human jobs that have been created by AI.
• Some believe that there will be a total of increase in jobs due to AI-induced innovations.
• It was estimated that in 2018 there would be over 490,000 jobs open for data scien-tists, but
only 200,000 scientists will be available. However, in the long run, AI and machine learning
may replace most data scientists (Perez, 2017).
• Violino (2018) contradicts those who claim that there is a huge fear among employ-ees
regarding job loss, saying that most workers see robots as an aid to their jobs. See also Leggatt
(2017).
Note: When this book went to press, there was a shortage of IT employees (several million in the United States). Automation
can alleviate this shortage. Note that a study reported by Weldon (2017b) showed that most workers actually welcome the
impact on jobs by AI and automation. As a final note, Guha (2017) provides a view of work and AI as a vision of “despair,
hope, and liberation.” He concludes that AI can liberate work—it is a historical opportunity.
Jobs and the Nature of Work Will Change
While you may not lose your job, intelligent applications may change it. One aspect of this change
is that low-skill jobs will be taken by machines, but high-skill jobs may not. Therefore, jobs may be
redesigned either to be low skilled in order to be automated, or to be high skilled so that they will
be executed exclusively by humans. In addition, there will be many jobs where people and machines
will work together as a team.
Changes in jobs and business processes will impact training, innovation, wages, and the nature
of work itself. Manyika (2017) and Manyika et al. (2017) of McKinsey & Company analyzed the
shifts that can be fundamental, and arrived at the following conclusions:
• Many activities done by humans will have the potential to be automated.
• Productivity growth from robotics, AI, and machine learning will be tripled com-pared to
pre-2015.
• AI will create many new jobs paying high salaries.
• Since more than half the world is still offline, the changes will not be too rapid.
Example: Skills of Data Scientists Will Change
According to Thusoo (2017) of the McKinsey Global Institutes study group, there will be a shortage
of 250,000 data scientists by 2024. There will be a need to retrain or train scientists so they can deal
with intelligent technologies and the changes in data science and in solving related real-world
problems. Thus, proper education must evolve. The job requirements of data scientists are already
changing. The scientists will need to know how to apply machine learning and intelligent
technologies to build IoT and other useful systems. New algorithms improve operations and
security, and data platforms are changing to fit new jobs.
Snyder (2017) found that 85 percent of executives know that intelligent technologies will
impact their workforce within five years, and 79 percent expect the current skill sets to be
188 Part V • Caveats of Analytics and AI
restructured. They also expect 79 percent productivity improvement. Employees fear that intelligent
systems will take over some of their activities, but they hope that intelligent systems will also help
with their work.
TIPS FOR SUCCESS A McKinsey study of 3,000 executives (Bughin, McCarthy, and Chui, 2017) reports
the following success tips for implementing AI provided by the executives:
• Digital capabilities need to come before AI.
• Machine learning is powerful, but it is not the solution to all problems.
• Do not put technology teams solely in charge of intelligent technologies.
• Adding a business partner may help with AI-based projects.
• Prioritize a portfolio approach to AI initiatives.
• The biggest challenges will be people and business processes.
• Not every business is using intelligent systems, but almost all those that use them increase
income and profit.
• Top leadership support is necessary for a transformation to AI.
DEALING WITH THE CHANGES IN JOBS AND THE NATURE OF WORK Manyika (2017) made the following
suggestions for policymakers:
1. Use learning and education to facilitate the change.
2. Involve the private sector in enhancing training and retraining.
3. Have governments provide incentives to the private sector so employees can invest in
improved human capital.
4. Encourage private and public sectors to create appropriate digital infrastructure.
5. Innovative income and wage schemes need to be developed.
6. Carefully plan the transition to the new work. Deal properly with displaced employees.
7. Properly handle new technology-enabled technologies.
8. Focus on new job creation, particularly digital jobs.
9. Properly capture the productivity increase opportunities.
Baird et al. (2017) of McKinsey & Company provide a video interview with industry experts
discussing how to deal with the changing nature of work. Another exploration of the nature of work
in the era of intelligent systems is provided by Crespo (2017). Chui et al. (2015) researched the
impact of automation on redefining jobs and business processes, including the impact on wages,
and the future of creativity. Finally, West (2018) provides a comprehensive study on the future of
work as it is influenced by robotics and AI-driven automation.
Conclusion: Let’s Be Optimistic!
Assuming that the disasters will not occur, then, as in the past, concerns about technology replacing
many human jobs and reducing wages are hopefully exaggerated. Instead, intelligent technologies
will clearly contribute to shorter work time for humans. Today, most people work long hours just
for survival.
u SECTION 14.6 REVIEW QUESTIONS
1. Summarize the arguments of why intelligent systems will take away many jobs.
2. Discuss why job losses may not be catastrophic.
3. How safe is your job? Be specific.
4. How may intelligent systems change jobs?
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 189
5. In what ways may work be changed?
6. Discuss some measures to deal with the changes brought by intelligent systems.
7. One of the areas of potential job loss is due to autonomous vehicles. Discuss the logic of this.
14.7 POTENTIAL DANGERS OF ROBOTS, AI, AND ANALYTICAL MODELING During the period 2016–2018, we witnessed a heated debate regarding the future of AI and
particularly robots. Dickson (2017) called the optimistic approach Utopia and the pessimistic one
Dystopia. The debate began with the industrial revolution regarding automation, and it has
accelerated because of the rapid technological innovations of AI. In Section 14.5, we presented one
aspect of this debate, the impact on jobs. In the center of the debate is the prediction of when AI’s
capabilities to reason and make decisions will become similar or even superior to that of people.
Furthermore, will such a development be beneficial or dangerous to society?
Position of AI Dystopia
The camp that supports this prediction includes well-known tech executives. Here are three of them:
• Elon Musk: “We need to be super careful with AI. Potentially more dangerous than nukes.”
(See the 10 min. video at youtube.com/watch?v=SYqCbJ0AqR4). Musk predicts that
World War III will start because of AI. “Robots will kill us all, one day,” he said in his several
presentations.
• Bill Gates: “I am in the camp that is concerned about super intelligence. Musk and some
others are on this and I don’t understand why some people are not concerned.” (Comments
made on TV and interviews, several times). He also suggested taxing the manufacturers and
users of robots and other AI machines.
• Stephen Hawking: The late scientist stated, “The development of full artificial intelligence
could spell the end of the human race.”
Many people are afraid of AI because they believe that computers will become smarter than
we are. See Bostrom’s video of his famous TED presentation at youtube.com/
watch?v=MnT1xgZgkpk. See also Maguire (2017) for a discussion regarding learning robots and
the risk of rebelling robots. For how robots can learn motor skills through trial and error, see the
video at youtube.com/watch?v=JeVppkoloXs/. For more, see Pham (2018).
The AI Utopia’s Position
A good place to begin for information on this position is to watch the 26 min. documentary video
on the future of AI at youtube.com/watch?v=UzT3Tkwx17A. This video concentrates on the
contribution of AI to the quality of life. One example is crime fighting in Santa Cruz, California,
where AI was able to predict where and when crimes will occur. Following the predictions, the
police department has been planning its work strategies. The result is a 20 percent reduction in
crime.
A second example is the prediction of the probability that a certain song will be a hit. The
prediction helps both artists and managers to plan their activities. Great success has been made. In
the future, AI is predicted to compose top songs.
Finally, there is a story about dating. The capabilities of AI enabled a scientist to find a perfect
match in a population of 30,000 potential candidates.
A basic argument of the Utopianists expressed in interviews, TV lectures, and more, is that
AI will support humans and enable innovations. AI also will partner with humans. The Utopians
believe that as AI expands, humans will become more productive and will have time to do more
190 Part V • Caveats of Analytics and AI
innovative tasks. At the same time, more tasks will be fully automated. Prices of products and
services will drop and the quality of life will increase.
At one point, we may achieve a fully automated and self-sustaining economy. Ultimately,
people will not have to work at all to make a living.
A leading proponent of AI benefits is Mark Zuckerberg of Facebook. He is in a heated debate
with Elon Musk (CEO of Tesla Corp), the unofficial leader of the Dystopia camp of believers.
Zuckerberg criticized those that believe that AI will cause “doomsday scenarios” (see the next
section). Musk claimed that Zuckerberg has a “limited understanding” of AI, and Zuckerberg
answered by referring to his paper on AI that won an award at the “top computer vision
conference.” For details, see Vanian (2017).
SOME ISSUES RELATED TO THE UTOPIA Several issues are related to the Utopianists’ position. Here are
three examples:
1. AI will be so great that people will have a problem of what to do with their free time. If you
have not yet seen Disney’s Wall-E movie, go and see it. It shows how humans are served by
robots. Dennis Hassabis, a strong proponent of Utopia (from Deep Mind, an AI company),
believes that AI will one day help people have a better life by understanding what makes
humans unique, what the mysteries of the mind are, and how to enjoy creativity.
2. The road to AI Utopia could be rocky, for example, there will be impacts on jobs and work.
It will take time to stabilize and adjust work and life of living with robots, chatbots, and other
AI applications.
3. One day we will not drive anymore and there may not be human financial advisors; everything
will be different, and the changes may be rapid and turbulent and we may even face disasters,
as projected by the Dystopia camp.
The Open AI Project and the Friendly AI
To prepare against the unintended action of robotics and AI, Elon Musk and others have created
Open AI, a nonprofit organization. With the unintended potential danger in mind, Musk and others
created a nonprofit AI research company endowed with $1 billion. The major objective is to enact
the path to safe artificial general intelligence (AGI). As you recall from Chapter 1, AGI is not here
yet, but it is coming.
The plan of Open AI is to build safe AGI and ensure that its benefits will be evenly
distributed. The research results are published in top journals. In addition, Open AI creates open
source software tools. The organization has a blog and it disseminates important AI news. For
details, see openai.com.
THE FRIENDLY AI Eliezer Yudkowsky, a cofounder of the Machine Intelligence Research Institute,
developed the idea of friendly AI, according to which AI machines should be designed so that they
will benefit humans rather than harm them (i.e., use a system of checks and balances in designing
the AI capabilities). For details, see Sherman (2018), and view a fascinating 1:29:55 min. video by
Yudkowsky (2016) at youtube.com/ watch?v=EUjc1WuyPT8.
CONCLUSION It is difficult to know what will happen in the future. But some actions are already
being taken to prevent a disaster. For example, several major companies have declared that they will
not produce or support killer robots.
The O’Neil Claim of Potential Analytics’ Dangers
Managers and data science professionals should be aware of the social and long-term effects of
mathematical models and algorithms. Cathy O’Neil, a Harvard PhD in mathematics who worked in
finance and the data science industry, expressed her experiences and observations in the popular
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 191
book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. We suggest
you read the book, or at least visit the author’s blog site at mathbabe.org/. The blog site
highlights social issues related to analytics. A good summary/review of the book is available at
knowledge.wharton.upenn. edu/article/rogue-algorithms-dark-side-big-data/.
In her book, O’Neil (2016) argues that models must satisfy three conditions. First, they must
be transparent. That is, if the model is not understandable, its application can lead to unintended
consequences.
Second, the model must have clear quantifiable objectives. For example, the celebrated
application of analytics in the book and movie Moneyball includes a model that was aimed at
increasing the number of financial wins. And the proposed input measures were well
understandable. Rather than using the more commonly reported measure “run base in” (RBI), the
analyst in Moneyball proposed and used on-base percentage and other measures (which were also
easily calculated and understood by anyone with basic math skills). On the other hand, models built
to assess the risk of mortgage-backed securities when no one fully understood the underlying
assumptions of collateralized securities, but financial traders were trading, have been blamed for
leading the financial crisis of 2008.
The third requirement is that the models must have a self-correcting mechanism and a process
in place so that they are audited regularly and new inputs and outputs are constantly being
considered. This third issue is particularly critical in applying models in social settings. Otherwise,
the models perpetuate the faulty assumptions inherent in the initial modeling stage. O’Neil discusses
several situations where such is the case. For example, she describes the models built in the United
States to identify underperforming teachers and reward better teachers. Some of these models
utilized the test scores of the pupils to assess the teachers. O’Neil cited several examples where the
models were used to fire “underperforming” teachers even though those teachers were loved by the
students and parents. Similarly, models are used to optimize the scheduling of workers in many
organizations. These schedules may have been developed to meet seasonal and daily demand
variations, but the models do not take into account the deleterious impacts of such variability in
schedules on the families of these usually lower-income workers. Other such examples include credit
score assessment models that are based on historical profiles and thus may negatively impact
minorities. Without mechanisms to audit such models and their unintended effects, they can do
more harm than good in the long term. So, model builders need to consider such concerns.
Note: In May 2018, General Data Protection Regulation (GDPR) became effective in the European Union. It includes the
need to explain data. According to Civin (2018), an explainable AI could reduce the impact of biased algorithms.
A comment: There is evidence that in some cases O’Neil’s claims are valid, and therefore
model builders and implementers must pay attention to the issues. However, in general, analytics
are properly designed and bring considerable benefits to society. Furthermore, analytical models
increase the competitiveness of companies and countries, creating many highly paid jobs. In many
cases, companies have social responsibility policies that minimize biases and inequality. Finally, as
Weldon (2017a) observed, algorithms and AI can be seen as great equalizers in bringing services
that were traditionally reserved for a privileged few, to everyone.
u SECTION 14.7 REVIEW QUESTIONS
1. Summarize the major arguments of the Utopia camp.
2. Summarize the major arguments of the Dystopia camp.
3. What is the friendly AI?
4. What is Open AI? Relate it to the dystopia vision.
5. What are the potential risks in using modeling and analytics?
192 Part V • Caveats of Analytics and AI
14.8 RELEVANT TECHNOLOGY TRENDS As we near the last section of this book that discusses some aspects of the future of intelligent
systems, it is worthwhile to describe some of the technology trends that will shape this future.
Unfortunately, there are hundreds of technology trends relevant to the content of this book. The
reason is that there are hundreds of variations of analytics, Big Data tools, AI, machine learning,
IoT robotics, and other intelligent systems. Therefore, we provide here only a sample of technology
trends. We divide this section into the following subsections:
• Gartner’s 2018 and 2019 lists.
• List of technology trends in intelligent systems.
• Ambient computing.
Gartner’s Top Strategic Technology Trends for 2018 and 2019
Gartner Inc. is a top technology research organization and consultant as well as an organizer of an
annual technology symposium attended by over 23,000 people (Gartner Symposium IT expo). It
provides an annual prediction of the technologies that it thinks will impact most organizations. The
2018 and 2019 lists of trends includes 10 items each, most of which relate directly to the content of
our book.
The summary of the 2018 list is shown in Figure 14.3. It was extracted from Gartner’s press
release of October 4, 2017, which is available at gartner.com/newsroom/id/3812063. The
essentials are provided in a video (5:36 min.) at youtube.com/watch?v=TPbKyD2bAR4.
GARTNER’S 2018 AND 2019 LISTS The following is extracted from
gartner.com/newsroom/id/3812063, for 2018, and from Weldon (2018), for 2019.
1. AI Foundation and Development. Advanced AI systems that support decision making,
some of which are autonomous, and other AI systems are developed in conjunction with
analytics and data science.
2. Intelligent Apps and Analytics. Almost all IT systems will include AI in the next few years.
See[gartner.com/smarterwithgartner/the-cios-journey-to-artificial- intelligence/].
3. Intelligent and Autonomous Things. Utilizing the IoT capabilities, there will be an
explosion of autonomous vehicles and a significant increase of other intelligent things (e.g.,
smart homes and factories where robots are assembling robots).
4. Digital Twin. A digital twin, see [gartner.com/smarterwithgartner/preparefor-the-
impact-of-digital-twins/], refers to digital representations of real-world objects and
systems. This includes mainly IoT systems with 20 billion connected things in two to three
years.
5. Empowered Cloud (Cloud to the Edge). In Edge computing, information collection,
processing, and delivery are conducted closer to the sources of the information.
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 193
Today
Narrow AI
(Chapter 2)
Increase capabilities with time, but no
match to human
intelligence
In 20–25 years
Artificial General
Intelligence
(Chapter 2)
Autonomous systems all over; limited reasoning
capabilities; adopt to changes in the environment; can
self-expand tasks; can
reason, innovate
In Distant Future
Super AI
As intelligent as human and even
more in some cases. Major support to
research, innovation
and learning
Possible
impacts Autonomous
vehicles are all around. Robots
assume more tasks. People have more
time. Compete with
humans
Can be dangerous if not controlled. Can significantly improve
our quality of life
Increasingly perform routine tasks, some
with human. Provide speed, quality and
advice. Cut cost
FIGURE 14.3 Predict the future of AI (Drawn by E.Turban)
6. Conversational Human-Machine Platforms. These platforms already facilitate natural
language interactions, resulting in improved collaboration. These include smart collaborative
spaces..
7. Immersive Experience. These systems change the manner in which people can see and
perceive the world (e.g., augmented reality). See [gartner.com/smarterwithgartner/
transform-business-outcomes-with-immersive-technology/].
8. Blockchain. Blockchain technologies [gartner.com/smarterwithgartner/areyou-ready-
for-blockchain-infographic/] offer a radical platform for increased security and trust,
significantly improving business transactions.
9. Augmented Analytics. Using machine learning enables this technology to focus on
transformation of analytics, so it will be better shared and consumed. This will facilitate data
preparation management and analysis to improve decision support.
10. Others. These include smart collaboration space, Quantum computing, digital and ethical
privacy, and adopting risks and trust.
Other Predictions Regarding Technology Trends
• The IEEE computer society also has 10 top predictions for 2018. computer.org/
web/pressroom/top-technology-trends-2018. The list includes deep learning, industrial
IoT, robotics, assisted transportation, augmented (assisted) reality, blockchain, and digital
currencies.
• Newman (2018) provides a list of 18 tech trends at CES 2018. These are related to displays at
CES.
• The potential business application and value for several analytics and AI technolo-gies based
on studies of 400 real-world cases done at McKinsey & Company is available as interactive
data visualization at mckinsey.com/featured-insights/ artificial-
intelligence/visualizing-the-uses-and-potential-impact-of-ai-andother-analytics/
(posted April 2018).
194 Part V • Caveats of Analytics and AI
• Top 10 trends for analytics in 2018 are provided by Smith (2018). The list is fairly technical
in nature. It includes “Data Gravity will accelerate to the cloud,” “Insightas-a-service will
rise,” and “End-to-end cloud analytics will emerge.”
• Top 10 AI technology trends for 2018 as envisioned by Rao et al. (2017) include “Deep
reinforcement learning: interacting with the environment to solve business problems” and
“Explainable AI: understanding the black box.”
• For seven data and analytical trends, see datameer.com/blog/seven-data- analytics-
trends-2018/.
• Computers will learn to think and think to learn.
• Robots will replace humans in more nonphysical and cognitive roles.
• Intelligent augmentation is part of the narrow AI (Chapter 1) and will continue to control
new AI applications.
• Edge computing was cited by Gartner, but it has much more value that may not be related to
the “cloud.” The technology will have a major impact on the future of data centers. For
details, see Sykes (2018a). Note that most of the new capabilities for the “cloud” exist in the
use of the “Edge.” For further information, visit Wikipedia. Edge AI enhancements will excel
in supporting machine learning and augmented reality.
Sommer (2017) lists the following:
• Data literacy will spread both in organizations and in society.
• Information points will be connected via hybrid multi-cloud systems.
• The mystery of rural networks will be exposed by deep learning theory.
• Self-service systems will use data catalogs as their frontier.
• Need to focus on Application Programming Interfaces (APIs).
• Analytics become conversational (e.g., via chatbots).
• Analytics will include immersive capabilities.
• Using augmented intelligence users will be turned to participants.
• For 11 top trends that drove business intelligence in 2018, see Sommer (2017).
• For six data analytics trends in 2018, see Olavsrud (2018).
• For robotics trends in 2018, see Chapman (2018).
• For 10 predictions of intelligent systems, see Press (2017).
Summary: Impact on AI and Analytics
Now that you have seen the many technologies trends for the future, you may also want to see when
they will impact AI. Figure 14.3 illustrates the long-term projection of AI. The future is divided into
three sections: today, in about 20 years, and in a distant future.
The future of BI and analytics is illustrated in Figure 14.4. Some additional predictions are
intelligent analytics, insight-as-a-service, and data cataloging. Finally, we describe one technology in
more detail. It may impact both analytics and AI.
Ambient Computing (Intelligence)
Closely related to the IoT, chatbots, smart homes, analytics, sensors and “things” are included in
the concept of ambient computing (or paradigm computing). It has several definitions, but
essentially it refers to electronic environments (e.g., network devices such as sensors) that are
sensitive and responsive to people and their environments. So ambient devices can support people
in whatever task they are doing. Once sensing their surroundings, the devices provide different
input/output methods that depend on the configuration of situations (e.g., what people are doing
at a given
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 195
FIGURE 14.4 Future of Analytics Source: “Analytics and BI Trends”, Datapine, in Top 10 Analytics and
Business Intelligence Trends for 2018, Business Intelligence, Dec 13th 2017, © 2017, Used with permission.
time). In summary, everything in our life will be computerized and intelligent. The concept is based
on previous research in the areas of pervasive computing, human– machine interaction, context
awareness, profiling, personalization, and interaction design. For details, see
en.wikipedia.org/wiki/Ambient_intelligence and Charara’s (2018) guide.
POTENTIAL BENEFITS OF AMBIENT INTELLIGENCE While the concept is mostly futuristic, its
characteristics and benefits are already envisioned. The networked devices can:
• Recognize individuals and other “things” and their context at any given time and place.
• Integrate into the environment and existing systems.
• Anticipate people’s desires and needs without asking (e.g., context awareness).
• Deliver targeted services based on people’s needs.
• Be flexible (i.e., can change their actions in response to people’s needs or activities).
• Be invisible.
Many of the devices and services described in this book already exhibit some of the
capabilities of ambient computing. Amazon’s Alexa is probably currently the closest to the ambient
concept. For details, see Kovach (2018). For more on ambient computing and its relationship to
IoT and smart cities, see Konomi and Roussos (2016).
u SECTION 14.8 REVIEW QUESTIONS
1. Identify three of the Gartner 10 that are mostly related to analytics and data science.
2. Identify three of the Gartner 10 that are most related to AI and machine learning.
3. Identify three of the Gartner 10 that are most related to IoT, sensors, and connectivity.
4. Identify three technologies related to analytics from the other predictions list and explore them
in more detail. Write a report.
196 Part V • Caveats of Analytics and AI
5. Identify three data science–related technologies from the long list and explore them in more
detail. Write a report.
6. Identify three AI-related technologies from the long list and explore them in more detail. Write
a report.
7. Describe ambient computing and its potential contribution to intelligent systems.
14.9 FUTURE OF INTELLIGENT SYSTEMS There is a general agreement among AI experts that AI is going to change everything in our world
for the better (e.g., see Lev-Ram [2017] and Violino [2017]). However, there are disagreements on
when such changes will occur and what their impact is going to be. AI research is accelerating due
to improvements in different related computer technologies (e.g., chips, IoT), improvements in
intelligent methodologies and tools, the increased activities in high-tech companies that are striving
to gain leadership in certain intelligent systems areas and firms that are investing billions of dollars
in AI, the development of AI tools and methodologies, and much more. In this section, we first
provide a presentation of what some major corporations are doing in the intelligent technologies
field.
What Are the Major U.S. High-Tech Companies Doing in the Intelligent Technologies
Field?
One way to predict the future of AI is to look at what the major companies are currently doing.
GOOGLE (ALPHABET) Google uses NLP in its Google Translate as well as in its search processes. It
uses neural networks in its immersed databases (for pattern recognition) and for making decisions
on them. In addition, Google uses other machine- learning algorithms for personalization
advertising decisions. Google Assistant and Home are two applied projects that attracted
considerable attention in CES 2018. Google Assistant is trying to dethrone Alexa. In addition,
Google is most active in the autonomous vehicles field. Google purchased several AI companies
and is conducting extensive research in the field. Google has a special team that attempts to provide
Google AI speech dialog with a personality (see Eadicicco, 2017). Google DeepMind’s AlphaGo is
the machine that beat the game Go champions. Google is using machine learning for managing its
huge databases and search strategies. Finally, Google is teaching its AI machines how people behave
(e.g., cook, huy) by showing them film clips (see Gershgorn, 2017).
APPLE Apple is known to secretly be working on several AI projects. The most known is its Siri
chatbot, which is embedded in several of its products (e.g., iPhone). In 2016, Apple acquired a
machine-learning company, Turi. While lagging behind Google, Amazon, and Microsoft, Apple is
rapidly closing the gap, using acquisitions and extensive research and development. Apple acquired
companies in speech recognition (Vocal!), image recognition (Perception), and facial expression
recognition (Emotion). Thus, Apple is becoming a leader in AI. With several hundred millions of
Siri users and new acquisitions in AI, Apple is charging forward rapidly.
FACEBOOK Mark Zuckerberg, Facebook’s CEO, is a major believer in the future of AI. In addition
to his personal investments in AI, he hired Yann LeCun, a deep-learning pioneer, to lead AI research
in the company. LeCun created a special Facebook unit that identifies important AI developments
and incorporates them into Facebook’s products. Facebook invested billions of dollars in AI. With
Facebook, AI goes mainstream. With its over 2 billion users, Facebook is spreading its AI
applications globally.
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 197
MICROSOFT Microsoft is very active in all AI technology research. In 2017, it acquired Maluuba, a
start-up that specializes in deep learning and NLP. Some believe that this acquisition will help
Microsoft outperform both Facebook and Google in the areas of speech and image recognition.
Maluuba excels in reading and comprehending text with near human capabilities in its virtual
personal assistant, Cortana. This assistant helps people deal with e-mail and messaging difficulties.
The AI will examine the content of messages and any stored documents and advice for what actions
to take. For a comprehensive video about AI today and tomorrow by Stanford University, watch a
74 min. seminar at youtube.com/watch?v=wJqf17bZQsY.
IBM IBM entered robotics as early as 1973. By 1980, it had developed the QS-1; by 1977, it had
developed Deep Blue; and by 2014, a mature IBM Watson entered the scene. IBM is also known
for its artificial brain project. (For Blue Brain, see artificialbrains.com/ blue-brain-project.) IBM
is also known for its Deep QA project.
IBM is very active in AI research, especially in the area of cognitive computing; see Chapter
6 and research.ibm.com/ai/. IBM Watson was developed in collaboration with MIT AI labs.
Some other current projects focus on distributed deep learning software, creation of music
and movie trailers by machines, gesture recognition, combining AI and IoT (e.g., embodied
cognition), and medical applications supported by Watson (cognitive care, e.g., cancer detection,
mental health care, and visually impaired people). IBM Watson is already considered the strongest
applied brand of AI. One billion users were expected to use it in 2018, gaining substantial benefits
from its applications.
AI Research Activities in China
AI research is done in many countries, notably Germany, Japan, France, the United Kingdom, and
India. But most research outside the United States is done in China. China plans to be the world
leader in AI, and its government is strongly supporting the activities of many AI companies. As you
may recall from Chapter 1, Vladimir Putin has said that whoever leads AI will control the world.
And, indeed, China plans to be that leader by 2030. The country plans an AI industry of $150 billion.
Among the many companies that are engaged in AI, three are investing billions of dollars,
employing thousands of AI experts and robotic engineers, and acquiring global talents in AI. The
three companies are Alibaba Group, Tencent, and Baidu. AI is already the priority of the Chinese
government. In a cover story in Fortune, Lashinsky (2018) describes and analyzes the competition
between Tencent and Alibaba.
TENCENT This giant e-commerce company has created a huge AI lab to manage its AI activities. The
goal is to improve AI capabilities and support decision making in the following areas: computer
vision, NLP, speech recognition, machine learning, and chatbots. AI is already embedded in over
100 Tencent products, including WeChat and QQ. A well-known AI slogan in China is “Juey, GO
AI.” Tencent supports the robotic company UBTech Alpha. Tencent is the world’s largest Internet
company, and AI improves its operations. Another slogan is “AI in all.” The company has a lab in
Bellevue, Washington. Healthcare is a main research priority there. For more on AI at Tencent, see
Marr (2018).
BAIDU Baidu started NLP research five years before Google to improve its search e ngine
capabilities. The company is located in the Silicon Valley, Seattle, and Beijing. Baidu has several
products. One is Duer OS, a voice assistant that is embedded in more than 100 brands of appliances
in several countries. The product is now optimized for smartphones. Baidu is also working on
autonomous vehicles. Finally, the company promotes facial recognition in the enterprise (replacing
ID badges). Baidu’s AI is growing but still much smaller than that of Alibaba.
198 Part V • Caveats of Analytics and AI
ALIBABA The world’s largest e-commerce company and the provider of cloud computing and IoT
platforms, Alibaba is active in AI projects and is an investor in AI companies, such as in the face
recognition giant SenseTime. Alibaba has developed a methodology for conducting AI, which is
described in Application Case 14.3.
Technologies
Big data Neural networks processing Real-time analytics Advance data processing
Multi-faced Video recognition and security analysis
Brain family : city brain : industrial brain : environmental brain aviation brain :
global data exchange brain : medical brain
Alibaba has developed a cloud-based model
known as ET Brain alibabacloud.com/e t . The
logic is that today and in the near future, we are
and will be doing business in the cloud computing
environment. Content, knowledge, and data are in
the cloud, and Alibaba is both a user and a pro-
vider of iCloud. The ET Brain model is illustrated
in Figure 14.6.
ETBrain consists of three parts: technolo-
gies, capabilities, and applications. Technologies
include Big Data and analytic processing, neu-
ral networks, video recognition analysis, and
machine learning. These technologies provide
four major capabilities such as cognitive percep-
tion, reasoning, real-time decision making, and
machine learning (see the middle level in the
figure). The capabilities drive a large amount of
applications, such as e-commerce activities (both
business-to-business and business-to-consumers),
medical and health care, smart cities, agricul-
ture, travel, finance, and aviation. All-in-all, it is
a super-intelligent AI platform. The ET Brain is
illustrated in a min 26:29 . video at / youtube.com
Y watch?v=QmkPDtQTar .
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 199
ETBrain’s Capabilities
Reasoning Machine Cognitive Strategic Learning Perception decision making
Real-time Perceptual Multi- Situational decision making innovation dimensional intelligence awareness
Applications ; Innovations
Smart cities Environment Aviation Travel Agriculture Transportation Fashion Retail Voice recognition Medical Financial Facial recognition Image application AI assistant Text recognition
FIGURE 14.6 Alibaba’s ET Brain Model. Drawn by E. Turban. Based on text at Alibabacloud.com/et Alibaba’s mission is to reach 2 billion consumers
and to help 10 million businesses worldwide. To attain this
mission, the company invested in seven research labs that
focus on AI, machine learning, NLP, face (image)
recognition, and network security. Alibaba is using AI to
optimize its supply chain, personalize recommendations,
and provide virtual personal assistants. Alibaba
concentrates on several industries and on AI-supported
bricks-and-mortar shopping. For example, in its AI office
in Hong Kong, the company opened “Fashion AI,”
working with Guess Inc., helping shoppers to create an
online ensemble while they are in a physical store. See
engadget.com/2018/07/04/guess-alibaba-aifashion-
store/. The company plans to rewire the world with AI
(see Knight, 2018) and may control the world commerce.
Questions for Case 14.3
1. Relate cloud computing to AI at Alibaba.
2. Explain the logic of the ET Brain model.
3. Search the Web to find recent Alibaba activities in the
AI field.
4. Read Lashinsky (2018). Why is Alibaba in such strong
competition with Tencent?
Sources: Compiled from W. Knight. (2018, March 7). “Inside the Chinese
Lab That Plans to Rewire the World with AI.” MIT Technology Review;
Marr, B. (2018, June 4). “Artificial Intelligence (AI) in China: The
Amazing Ways Tencent Is Driving Its Adoption.” Forbes; A. Lashinsky.
(2018, June 21). “Alibaba v. Tencent: The Battle for Supremacy in
China.” Fortune. alibabacloud.com/et.
The U.S.–China Competition: Who Will Control AI?
At the moment, U.S. companies are ahead of Chinese companies. However, this situation may be changed in the
future due to the huge investments in AI in China and the support provided by the Chinese government. Note that
a major topic in the U.S.–China trade negotiations in 2018 centered on the use of technology by Chinese companies
that employ U.S. knowledge and trade secrets.
The Largest Opportunity in Business
According to McCracken (2017), intelligent technologies provide the largest opportunity for tech companies since
mobile computing. This is why tech giants and start-ups are trying to exploit AI. Desjardins (2017) provides an
infographic about the future impact of AI that includes $15.7 trillion by 2030 in the form of productivity gains and
increased consumer spending. By 2018, tech giants and others will invest $30 billion in research and development
and $13.2 billion in start-ups. The largest improvement is expected in image and speech recognition products.
Note that despite their rivalry, Facebook, Amazon, Google, IBM, and Microsoft launched a partnership to
research advancements and best practices in AI.
Conclusion
200 Part V • Caveats of Analytics and AI
Now that you have completed reading this book you may ask, “What will happen to intelligent technologies in the
future?” There will be a significant impact on business and quality of life. There will be changes, and they will be
significant. With billions of dollars invested, mostly in AI, there will be advancements. Machines are getting smarter
and smarter. For example, Alibaba’s copywriting machine, which is based on deep learning and NLP, can generate
20,000 lines of text in one second. The machine is so smart that it passed the Turing test (Chapter 2), which means
that it is smart like a human but can work much faster. We will now look at two areas: business and quality of life.
IMPACT ON BUSINESS According to Kurzer (2017), there might be challenges, but AI was expected to flourish as of
2018. There is very little doubt that we will see increased commercialization of AI, especially in marketing, financial
services, manufacturing, and IT support. For example, the quality and nature of the customer experience could be
improved, augmented by AI applications, and IoT. Kurzer also predicted that there will be more proactive processes
rather than reactive ones. There will be more people-machine collaboration and while many jobs will be automated,
many new ones will be created. There is going to be more conversational AI due to the increased capabilities of
chatbots and personal assistants such as Alexa, Siri, and Google Assistant. Gartner predicted that by the end of this
decade, people will have more conversations with machines than with their immediate family members
(gartner.com/smarterwithgartner/gartnerpredicts-a- virtual-world-of-exponential-change/). Another area
with promising applications is image recognition. Google is a major force in both conversational and image recognition
AI.
IMPACT ON QUALITY OF LIFE There will be impacts on life that will change the way we drive, eat, entertain, get services,
learn, and fight.
A major area where AI intelligent systems have already made a stride is the healthcare field. Bernard Tyson,
CEO of Kaiser Permanente, made the following public statement: “I don’t think any physician should be practicing
without AI assisting in their practice. It’s just impossible (otherwise) to pick up on patterns, to pick up on trends, to
really monitor care.” Editors (2018) report that smart solutions can improve quality of life indicators by 10 to 30
percent. (The longer we wait, the higher the percentage will be.) Among the indicators that they cite are: having
longer and healthier lives, reducing greenhouse gas emissions, saving 200,000 lives worldwide over 10 years (thanks
to self-driving cars), reducing the commute time for people (fewer traffic problems), increasing the number of jobs
(e.g., by new technologies and more productive business environments), and providing better and more affordable
housing.
Autonomous vehicles, including drones, will clearly change our lives for the better, and robots will be able to
serve us (especially people who are elderly and those that are sick), entertain us, and if properly managed, be our
companions. For an impact of AI in the future on society, watch the video at youtube.com/watch?v=KZz6f-
nCCN8/.
What will the unintended results be? What if robots will kill us all? Well, that probably will never happen.
People are smart enough to make sure that only good results will come from intelligent systems.
u SECTION 14.9 REVIEW QUESTIONS
1. Describe the AI activities of major U.S. tech companies.
2. Describe the work by Chinese giant companies.
3. Describe Alibaba’s approach to AI (The ET Brain model).
Chapter Highlights • Intelligent systems can affect organizations in many ways as
stand-alone systems, or integrated among themselves or with
other computer-based information systems.
• The impact of analytics on individuals varies—it can be positive,
neutral, or negative.
• Serious legal issues may develop with the intro-duction of
intelligent systems; liability and privacy are the dominant
problem areas.
• Many positive social implications can be expected from
intelligent systems. These range from providing
opportunities to people to lead the fight against terrorism.
Quality of life, both at work and at home, is likely to
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 201
improve as a result of the use of these technologies. Of course,
there are potentially negative issues to be concerned about.
• Growth of intelligent systems is going to lead to major changes
in industry structure and future employment.
• A major battle is brewing about who owns the user data that are
being generated from the use of smartphones, cars, and so on.
• In deploying intelligent systems, it is necessary to consider legal,
privacy, and ethical issues.
• Placing robots as coworkers in the work force raises legal and
ethical issues.
• Intelligent technologies may impact business pro-cesses,
organizational structure, and management practices.
• It may be necessary to create independent orga-nizational units
that deploy and manage intelligent systems.
• Intelligent systems may provide a considerable competitive
advantage to their users.
• Intelligent systems may create massive unem-ployment mainly
in routine and mid-management jobs.
• Eventually, intelligent system may cause unem-ployment even
in skilled jobs. So retraining may be needed.
• Intelligent systems may result in restructur-ing many jobs
notably through human-machine collaboration.
• Intelligent systems will create many new jobs that require
specialized training.
• The use of intelligent systems automation may result in a
shorter work week and a need to compensate those people
who will lose their jobs.
• Some people are afraid of unintended conse-quences of
having AI and robots. Machines will learn and may harm
humans.
Exercises 1. Identify ethical issues related to managerial decision making.
Search the Internet, join discussion groups/ blogs, and read
articles from the Internet. Prepare a report on your findings.
2. Search the Internet to find examples of how intelligent systems
can facilitate activities such as empowerment, mass
customization, and teamwork. 3. Investigate the American Bar Association’s Technology
Resource Center
(americanbar.org/groups/departments_offices/legal_te
chnology_resources.html) and nolo.com. What are the
major legal and societal concerns regarding intelligent systems?
How are they being dealt with?
Key Terms ambient computing computer ethics
privacy
Questions for Discussion
1. Some say that analytics in general dehumanize managerial
activities, and others say they do not. Discuss arguments for
both points of view. 2. Diagnosing infections and prescribing pharmaceuticals are the
weak points of many practicing physicians. It seems, therefore,
that society would be better served if analytics-based diagnostic
systems were used by more physicians. Answer the following
questions: a. Why do you think such systems are used minimally by
physicians? b. Assume that you are a hospital administrator whose
physicians are salaried and report to you. What would you
do to persuade them to use an intelligent system? c. If the potential benefits to society are so great, can society
do something that will increase doctors’ use of such
intelligent systems?
3. What are some of the major privacy concerns in employing
intelligent systems on mobile data? 4. Identify some cases of violations of user privacy from current
literature and their impact on data science as a profession. 5. Some fear that robots and AI will kill all of us. Others
disagree. Debate the issue. 6. Some claim that AI is overhyped. Debate the issue. Place a
question on Quora and analyze five responses. 7. Some claim that AI may become a human rights issue (search
for Safiya Noble). Discuss and debate. 8. Discuss the potential impact of the GDPR on privacy,
security, and discrimination. 9. Discuss ethics and fairness in machine learning. Start by
reading Pakzad (2018). 10. Should robots be taxed like workers? Read Morris (2017) and
write about the pros and cons of the issue.
202 Part V • Caveats of Analytics and AI
4. Explore several sites related to healthcare (e.g., WebMD. com,
who.int). Find issues related to AI and privacy. Write a report on
how these sites suggest improving privacy. 5. Go to Humanyze.com. Review various case studies and
summarize one interesting application of sensors in understanding
social exchanges in organizations. 6. Research the issue of voice assistants and privacy protection. Start
by reading Collins (2017) and Huff (2017). 7. Is granting advanced robots rights a good or bad idea? Read
Kottasova (2018) for a start. 8. Face and voice recognition applications are mushrooming.
Research the state of their regulation in a country of your choice.
Use the United States if your country is not regulating. 9. Research the ethical issues of self-driving cars. Start by reading
Himmelreich (2018). 10. Is your organization ready for AI? Research this issue and find all
major activities that it includes.
11. Research the role of IoT as a tool for providing connectivity
between sensors and analytics. Write a report. 12. Some people say that robots and chatbots may increase insurance
risk and fees. Research this and write a report.
13. Watch the video at youtube.com/watch?v=wwuovuCfDU/ and
comment about the robot’s potential impacts. 14. Research the issue stated in quotation marks: “When will robots
rebel?” and “Will AI take control of the plant?” Start by reading
Maguire (2017) and read advancedmp. com/artificial-
intelligence/. Write a report.
15. Read Chui et al. (2016) and research the areas in which machines
can replace humans and where they cannot (yet). Find changes
since 2016. Write a report.
16. Watch the 3:38 min . video at
youtube.com/watch?v=781Mlkxyql/. Relate it to Musk’s
predictions about robots reigning in this world (Section 14.7). 17. Read the SAS report on AI ethics at sas.com/en_us/
insights/articles/analytics/artificial-intelligenceethics.html.
Comment on each of the three proposed steps. Also comment on
the human-machine collaboration in problem solving.
References Ainsworth, M. B. (2017, October). “Artificial Intelligence for Executives.”
SAS White Paper, ai20for20executives.pdf, October 2018. Andronic, S. (2017, September 18). “5 Ways to Use Artificial Intelligence
as a Competitive Advantage.” Moonoia.com. Anon. (2017, February 20). “Big Data and Data Sharing: Ethical Issues.”
UK Data Service. ukdataservice.ac.uk/ media/604711/big-data-
and-data-sharing_ethical- issues.pdf (accessed July 2018). Autor, D. H. (2016, August 15). “The Shifts—Great and Small— in
Workplace Automation.” MIT Sloan Review. sloanreview.
mit.edu/article/the-shifts-great-and-small-in- workplace-
automation/ (accessed July 2018).
Baird, Z. et al. (2017, August). “The Evolution of Employment and
Skills in the Age of AI.” McKinsey Global Institute.
Baroudy, K., et al. (2018, March). “Unlocking Value from IoT
Connectivity: Six Considerations for Choosing a Provider.”
McKinsey & Company. Batra, G., A. Queirolo, & N. Santhanam. (2018, January). “Artificial
Intelligence: The Time to Act Is Now.” McKinsey & Company. Bloomberg News. (2017, November 29). “Ethical Worries Are
Marring Alphabet’s AI Healthcare Initiative.” Information
Management. Bossmann, J. (2016). “Top 9 Ethical Issues in Artificial Intelligence.”
World Economic Forum.
Botton, J. (2016, May 28). “Apple Supplier Foxconn Replaces 60,000
Humans with Robots in China.” Market Watch.
Brynjolfsson, E., & A. McAfee. (2016). The Second Machine Age: Work,
Progress, and Prosperity in a Time of Brilliant Technologies. Boston, MA:
W.W. Norton. Bughin, J., B. McCarthy, & M. Chui. (2017, August 28). “A Survey of
3,000 Executives Reveals How Businesses Succeed with AI.”
Harvard Business Review. Burden, E. (2018, July 16). “Robots Will Bolster U.K. Growth and
Create New Jobs, PwC says.” Bloomberg News.
Catliff, C. (2017, August 15). “Three Ways Your Business Can
Leverage Artificial Intelligence.” The Globe and Mail. Chapman, S.
(2018, January 16). “The Robotics Trends of 2018, According to
Tharsus.” Global Manufacturing.
Charara, S. (2018, January 4). “A Quick and Dirty Guide to Ambient
Computing (and Who Is Winning So Far).” Theambient.com. Chui, M., K. George, & M. Miremadi. (2017, July). “ACEO Action
Plan for Workplace Automation.” McKinsey Quarterly. Chui, M., J. Manyika, & M. Miremadi. (2015, November). “Four
Fundamentals of Workplace Automation.” McKinsey Quarterly.
Chui, M., J. Manyika, & M. Miremadi. (2016, July). “Where Machines
Could Replace Humans—and Where They Can’t (Yet).” McKinsey
Quarterly.
Civin, D. (2018, May 21). “Explainable AI Could Reduce the Impact
of Biased Algorithms.” Ventura Beat. Clozel, L. (2017, June 30). “Is Your AI Racist? This Lawmaker Wants
to Know.” American Banker. Cokins, G. (2017, March 22). “Opinion Could IBM’s New Deep
Learning Service Tool Help Save IT Jobs?” Information
Management. Collins, T. (2017, December 18). “Google and Amazon Really DO
Want to Spy on You: Patent Reveals Future Version of Their
Voice Assistants Will Record Your Conversations to Sell You
Products.” Daily Mail. Crespo, M. (2017, July 31). “The Future of Work in the Era of
Artificial Intelligence.” Equal Times.
Crosman, P. (2017, August 17). “Why Cybercriminals Like AI As
Much As Cyberdefenders Do.” American Banker.
Daugherty, P. R., & J. Wilson. (2018). Human + Machine: Reimagining
Work in the Age of AI. Boston, MA: Business Review Press. Desjardins, J. (2017, August 21). “Visualizing the Massive $15.7
Trillion Impact of AI.” Visual Capitalist. de Vos, B. (2018, July 11).
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 203
“Opinion: These 3 Business Functions Will Be the First to Benefit from
Artificial Intelligence.” Information Management. DiCamillo, N. (2018, July 12). “Morgan Stanley Draws from ‘Hundreds
of Conversations’ with Experts to Build Its AI.” American Banker.
Dickson, B. (2017, July 28). “What Is the Future of Artificial
Intelligence?” Tech Talk. Donahue, L. “A Primer on Using Artificial Intelligence in the Legal
Profession.” Jolt Digest, January 3, 2018.
Dormehl, L. (2017). Thinking Machines: The Quest for Artificial Intelligence—
and Where It’s Taking Us Next. New York, NY: TarcherPerigee. Eadicicco, L. (2017, October 13). “Google Searches for Its Voice.” Time
for Kids. Editors. (2018, July 12). “Smart Solutions Can Help ASEAN Cities
Improve Quality-of-Life Indicators by 10-30%.” eGov Innovation.
Egan, M. (2015, May 13). “Robots Threaten These 8 Jobs.”
CNNMoney.com. Ekster, G. (2015). Driving Investment Performance with Alternative
Data. integrity-research.com/wp-content/
uploads/2015/11/Driving-Investment-PerformanceWith-
Alternative-Data.pdf (accessed July 2018). Elgan, M. (2017, April 29). “How the Amazon Echo Look Improves
Privacy?” Computer World. Elson, R. J., & LeClerc, R. (2005). Security and Privacy Concerns in the
Data Warehouse Environment. Business Intelligence Journal, 10(3), 51. Gaudin, S. (2016, October 26). “1-800-Flowers Wants to Transform Its
Business with A.I.” Computer World. Gershgorn, D. (2017, October 22).
“Google Is Teaching Its AI How Humans Hug, Cook and Fight.” Quartz.
qz. com/1108090/google-is-teaching-its-ai-how-humanshug-cook-
and-fight/ (accessed April 2018). Goldman, S. (2018, March 22). “The Ethics of Legal Analytics.”
Law.com. Guha, A. (2017, June 5). “Labour and Artificial Intelligence: Visions of
Despair, Hope and Liberation.” Hindustan Times.com.
Himmelreich, J. (2018, March 27). “The Ethical Challenges Self-Driving
Cars Will Face Every Day.” Smithsonian.
Hu, F. (2016). Security and Privacy in Internet of Things (IoTs): Models,
Algorithms, and Implementations. Boca Raton, FL: CRC Press. Huff, E. (2017, January 17). “Proof That Amazon Devices Are Spies in
Your Own Home: Alexa Automatically Orders Product after ‘Hearing’
Audio in Private Homes.” Natural News.
Kahn, J. (2017, November 29). “Legal AI Gains Traction as U.K. Startup
Targets U.S.” Bloomberg Technology. Kaplan, J. (2017). Startup Targets. Artificial Intelligence: What Everyone Needs
to Know. London, United Kingdom: Oxford University Press.
Kassner, M. (2017, January 2). “5 Ethics Principles Big Data Analysts
Must Follow.” Tech Republic. Keenan, J. (2018, February 13). “1-800-Flowers.com Using Technology
to Win Customers’ Hearts This Valentine’s Day.” Total Retail. Kelly, H. (2018, January 29). “Robots Could Kill Many Las Vegas Jobs.”
Money.CNN.com.
Kiron, D. (2017, January 25). “What Managers Need to Know About
Artificial Intelligence.” MIT Sloan Management Review.
Knight, W. (2018, March 7). “Inside the Chinese Lab That Plans to
Rewire the World with AI.” MIT Technology Review. Kokalitcheva, K. (2017, May 9). “The Full History of the
UberWaymo Legal Fight.” Axio.
Konomi, S., & G. Roussos (ed.). (2016). Enriching Urban Spaces with
Ambient Computing, the Internet of Things, and Smart City Design
(Advances in Human and Social Aspects of Technology). Hershey, PA:
GI Global. Korolov, M. (2016, December 2). “There Will Still Be Plenty of Work
to Go Around So Job Prospects Should Remain Good.” IT World.
Kottasova, I. (2018, April 12). “Experts Warn Europe: Don’t Grant
Rights.” Money.CNN.com. Kovach, S. (2018, January). “Amazon Has Created a New Computing
Platform That Will Future-Proof Your Home.” Business Insider.
businessinsider.com/amazon-alexabest-way-future-proof-
smart-home-2018-1/ (Accessed July 2018).
Krauth, O. (2018, January 23). “Robot Gender Gap: Women Will
Lose More Jobs Due to Automation Than Men, WEF Finds.”
Tech Republic.
Krigsman, M. (2017, January 30). “Artificial Intelligence: Legal,
Ethical, and Policy Issues.” ZDNet. Kurzer, R. (2017, December 21). “What Is the Future of Artificial
Intelligence?” Martechnology Today.
Lashinsky, A. (2018, June 21). “Alibaba v. Tencent: The Battle for
Supremacy in China.” Fortune. Lawson, K. (2017, May 2). “Do You Need a Chief Artificial
Intelligence Officer?” Information Management. Leggatt, H. (2017, June 7). “Biggest Stressor in U.S. Workplace Is
Fear of Losing Jobs to AI, New Tech.” Biz Report.
Lev-Ram, M. (2017, September 26). “Tech’s Magic 8 Ball Says
Embrace the Future.” Fortune. Maguire, J. (2017, February 3). “Artificial Intelligence: When Will the
Robots Rebel?” Datamation. datamation.com/ data-
center/artificial-intelligence-when-will-therobots-
rebel.html (accessed April 2018). Manyika, J. (2017, May). “Technology, Jobs, and the Future of
Work.” McKinsey Global Institute. Manyika, J., M. Chi, M. Miremadi, J. Bughin, K. George, P. Willmott,
& M. Dewhurst. (2017, January). “Harnessing Automation for a
Future That Works.” Report from the McKinsey Global Institute.
mckinsey.com/global-
themes/digitaldisruption/harnessing-automation-for-a-
future-thatworks/ (accessed April 2018).
Marr, B. (2018, June 4). “Artificial Intelligence (AI) in China: The
Amazing Ways Tencent Is Driving Its Adoption.” Forbes. Marshall, A., & A. Davies. (2018, February 9). “The End of Waymo
v. Uber Marks a New Era for Self-Driving Cars: Reality.” Wired.
Mason, R., F. Mason, & M. Culnan. (1995). Ethics of Information
Management. Thousand Oaks, CA: Sage. McCracken, H. (2017, October 10). “How to Stop Worrying and
Love the Great AI War of 2018.” Fast Company.
204 Part V • Caveats of Analytics and AI
McFarland, M. (2017a, April 28). “Robots Hit the Streets—and the Streets
Hit Back.” CNN Tech. McFarland, M. (2017b, September 15). “Robots: Is Your Job At Risk?”
CNN News.
Morgan, B. (2017, June 13). “Ethics and Artificial Intelligence with IBM
Watson’s Rob High.” Forbes. Morris, D. (2017, February 18). “Bill Gates Says Robots Should Be Taxed
Like Workers.” Fortune.com. Newman, D. (2018, January 16). “Top 18 Tech Trends at CES 2018.”
Forbes.com.
Olavsrud, T. (2018, March 15). “6 Data Analytics Trends That Will
Dominate 2018.” CIO.
O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases
Inequality and Threatens Democracy (Crown Publishing). Pakzad, R. (2018, January 21). “Ethics in Machine Learning.”
Medium.com. Palmer, S. (2017, February 26). “The 5 Jobs Robots Will Take First.” Shelly
Palmer. Perez, A. (2017, May 31). “Opinion Will AI and Machine Learning
Replace the Data Scientist?” Information Management. Pham, S. (2018,
February 21). “Control AI Now or Brace for Nightmare Future, Experts
Warn.” Money.cnn.com (News). Press, G. (2017, November 9). “10 Predictions for AI, Big Data, and
Analytics in 2018.” Forbes.com.
Provazza, A. (2017, May 26). “Artificial Intelligence Data Privacy Issues
on the Rise.” Tech Target (News). Rainie, L., & J. Anderson. (2017, June 6). “The Internet of Things
Connectivity Binge: What Are the Implications?” Pew Research Center. Ransbotham, S. (2016). “How Will Cognitive Technologies Affect Your
Organization?” sloanreview.mit.edu/ article/how-will-cognitive-
technologies-affect-yourorganization/ (accessed July 2018).
Rao, A., J. Voyles, & P. Ramchandani. (2017, December 5). “Top 10
Artificial Intelligence (AI) Technology Trends for 2018.” USBlogs
PwC.
Rayo, E. A. “AI in Law and Legal Practice – A Comprehensive View of
35 Current Applications.” Techemergence, September 19, 2018. Rikert, T. (2017, September 25). “Using AI and Machine Learning to Beat
the Competition.” NextWorld. insights. nextworldcap.com/ai-
machine-learningb01946a089b2 (accessed July 2018). Ross, J. (2017, July 14). “The Fundamental Flaw in AI Implementation.”
MIT Sloan Management Review. sloanreview.mit.edu/article/the-
fundamental-flaw-in-ai- implementation/ (accessed July 2018). Sage, A. et al. (2018, February 9). “Waymo Accepts $245 Million and
Uber’s ‘Regret’ to Settle Self-Driving Car Dispute.” Reuters (Business
News). SAS. (n.d.). “Customer Loyalty Blossoms with Analytics.” SAS Publication,
sas.com/en_us/customers/1-800-flowers. html/ (accessed July
2018). SAS. (2018). “Artificial Intelligence for Executives.” White Paper. Sharma, K. (2017, June 28). “5 Principles to Make Sure Businesses Design
Responsible AI.” Fast Company.
Shchutskaya, V. (2017, March 20). “3 Major Problems of A rtificial
Intelligence Implementation into Commercial Projects.” InData Labs.
https://indatalabs.com/blog/ data-science/problems-of-
artificial-intelligenceimplementation/ (accessed April 2018).
Sherman, E. (2015, February 25). “5 White-Collar Jobs Robots
Already Have Taken.” Fortune.com fortune. com/2015/02/25/5-
jobs-that-robots-already-are- taking (accessed April 2018). Sherman, J. (2018, October 16). “Human-Centered Design for
Empathy Values and AI.” AlMed.
Singh, G. (2017a, September 20). “Opinion: 5 Components That Artificial Intelligence Must Have to Succeed.” Health
DataManagement. Singh, S. (2017b, December 13). “By 2020, Artificial Intelligence Will
Create More Jobs Than It Eliminates: Gartner.” The Economic
Times (India). Smith, Ms. (2018, March 12). “Ransomware: Coming to a Robot
Near You Soon?” CSO, News. Smith, N. (2018, January 3). “Top 10 Trends for Analytics in 2018.”
CIO Knowledge.
Snyder, A. (2017, September 6). “Executives Say AI Will Change
Business, But Aren’t Doing Much About It.” Axios.com. Sommer, D. (2017, December 20). “Opinion Predictions 2018:
11 Top Trends Driving Business Intelligence.” Information
Management. Spangler, T. (2017, November 24). “Self-Driving Cars Programmed
to Decide Who Dies in a Crash.” USA Today.
Standage, T. (2016) “The Return of the Machinery Question.” Special
Report. The Economist. economist.com/sites/
default/files/ai_mailout.pdf (accessed July 2018).
Steinberg, J. (2017, April 26). “Echo Lock: Amazon’s New Alexa
Device Provide Fashion Advice.” INC. Straus, R. (2014, May 31). “Will You Be Replaced by a Robot? We
Reveal the 100 Occupations Judged Most and Least at Risk of
Automation.” ThisisMoney.com. thisismoney.
co.uk/money/news/article-2642880/Table-700-
jobsreveals-professions-likely-replaced-robots.html
(accessed April 2018). Sykes, N. (2018a, March 27). “Opinion: Edge Computing and the
Future of the Data Center.” Information Management.
Sykes, N. (2018b, January 17). “Opinion: 9 Top Trends Impacting
the Data Center in 2018.” Information Management. Thusoo, A. (2017, September 27). “Opinion: AI Is Changing the
Skills Needed of Tomorrow’s Data.” Information Management. Uzialko, A. (2017, October 13). “AI Comes to Work: How Artificial
Intelligence Will Transform Business.” Business News Daily. Vanian, J. (2017, July 26). “Mark Zuckerberg Argues Against Elon
Musk’s View of Artificial Intelligence. . . Again.” Fortune. Violino, B. (2017, June 27). “Artificial Intelligence Has Potential to
Drive Large Profits.” Information Management.
Violino, B. (2018, February 21). “Most Workers See Smart Robots
As Aid to Their Jobs, Not Threat.” Information Management.
WallStreetJournal.com. (2016). “What They Know.” wsj.
com/public/page/what-they-know-digital-privacy. html
(accessed April 2018).
Chapter 14 • Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 205
Welch, D. (2016, July 12). The Battle for Smart Car Data. Bloomberg
Technology. bloomberg.com/news/articles/ 2016-07-12/your-car-
s-been-studying-you-closelyand-everyone-wants-the-data
(accessed April 2018). Weldon, D. (2017a, May 5). “AI Seen as Great ‘Equalizer’ in Bringing
Services to the Masses.” Information Management.
Weldon, D. (2017a, August 11). “Majority of Workers Welcome Job
Impacts of AI, Automation.” Information Management.
Weldon, D. (2017c, July 21). “Smarter Use of Analytics Offers Top
Competitive Advantage.” Information Management. Weldon, D. (2018,
February 28). “Knowing When It’s Time to Appoint a Chief Data
Officer.” Information Management. Weldon, D. (2018, October 18) “Gartner’s top 10 strategic technology
trends for 2019.” Information Management. West, D. (2018). The Future of Work: Robots, AI, and Automation.
Washington, DC: Brooking Institute Press. Wilson, H. et al. (2017, March 23). “The Jobs That Artificial Intelligence
Will Create.” MIT Sloan Management Review. Yudkowsky (2016, May 5)
youtube.com/watch?v=EUjc1 WuyPT8.
active data warehousing See real-time data warehousing.
ad hoc query A query that cannot be determined prior to the
moment the query is issued. agency The degree of autonomy
vested in a software agent.
Alexa The virtual personal assistant of Amazon.com.
algorithm A step-by-step search in which improvement is made at
every step until the best solution is found.
ambient computing Electronic environment that is sensitive and
responsive to people. The technology serves the environment and
acts to support the involved people in their tasks.
analytic hierarchy process (AHP) A modeling structure for
representing multi-criteria (multiple goals, multiple objectives)
problems—with sets of criteria and alternatives (choices)—
commonly found in business environments.
analytical models Mathematical models into which data are loaded
for analysis.
analytical techniques Methods that use mathematical formulas to
derive an optimal solution directly or to predict a certain result,
mainly in solving structured problems. analytics The science of
analysis.
analytics ecosystem A classification of sectors, technology/
solution providers, and industry participants for analytics.
application service provider (ASP) A software vendor that offers
leased software applications to organizations.
Apriori algorithm The most commonly used algorithm to discover
association rules by recursively identifying frequent itemsets.
area under the ROC curve A graphical assessment technique for
binary classification models where the true positive rate is plotted
on the Y-axis and the false positive rate is plotted on the X-axis.
artificial brain People-made machine that attempts to be
intelligent, creative, and self-aware.
artificial intelligence (AI) Behavior by a machine that, if
performed by a human being, would be called intelligent. artificial
neural network (ANN) Computer technology that attempts to
build computers that operate like a human brain. The machines
possess simultaneous memory storage and work with ambiguous
information. Sometimes called, simply, a neural network. See neural
computing.
association A category of data mining algorithm that establishes
relationships about items that occur together in a given record.
asynchronous Occurring at different times.
augmented intelligence This is an alternative conceptualization
of artificial intelligence that focuses on AI’s assistive
770 role, emphasizing the fact that it is designed to enhance human
intelligence rather than replace it.
augmented reality The integration of users’ senses with the
surrounding environment and information technology. It provides
people with real-world interactive experiences with the
environment.
authoritative pages Web pages that are identified as particularly
popular based on links by other Web pages and directories.
automated decision system (ADS) A business rule–based system
that uses intelligence to recommend solutions to repetitive decisions
(such as pricing).
automation The process by which special purpose machines or
systems are able to complete tasks without human intervention.
autonomous cars A vehicle that can guide itself without human
intervention.
autonomous vehicles Self-driving vehicles that do not need a
driver and are preprogrammed to drive to destinations; also referred
to as robot-driven cars, self-driving cars, and autonomous cars.
autonomy The ability to make your own decisions.
axon An outgoing connection (i.e., terminal) from a biological
neuron.
backpropagation The best-known learning algorithm in neural
computing where the learning is done by comparing computed
outputs to desired outputs of training cases.
backward chaining A search technique (based on if-then rules)
used in production systems that begins with the action clause of a
rule and works backward through a chain of rules in an attempt to
find a verifiable set of condition clauses.
bagging The simplest and most common type of ensemble
method; it builds multiple prediction models (e.g., decision trees)
from bootstrapped/resampled data and combines the predicted
values through averaging or voting.
balanced scorecard (BSC) A performance measurement and
management methodology that helps translate an organization’s
financial, customer, internal process, and learning and growth
objectives and targets into a set of actionable initiatives.
Bayes theorem (also called Bayes rule) Named after the British
mathematician Thomas Bayes (1701–1761), this is a mathematical
formula for determining conditional probabilities.
Bayesian belief networks (or Bayesian networks) These are
powerful tools for representing dependency structure among
variables in a graphical, explicit, and intuitive way.
Bayesian network model This is a directed acyclic graph where
the nodes correspond to the variables, and the arcs signify
conditional dependencies between variables and their possible
values.
best practices In an organization, the best methods for solving
problems. These are often stored in the knowledge repository of a
knowledge management system.
Big Data Data that are characterized by the volume, variety, and
velocity that exceed the reach of commonly used hardware
environments and/or capabilities of software tools to process.
Big Data analytics Application of analytics methods and tools to
Big Data.
Glossary 207
boosting This is an ensemble method where a series of prediction
models are built progressively to improve the predictive
performance of the cases/samples incorrectly predicted by the
previous ones.
bootstrapping A sampling technique where a fixed number of
instances from the original data is sampled (with replacement) for
training and the rest of the data set is used for testing.
bot An intelligent software agent. Bot is an abbreviation of robot
and is usually used as part of another term, such as knowbot,
softbot, or shopbot. brainstorming A process for generating
creative ideas.
business (or system) analyst An individual whose job is to
analyze business processes and the support they receive (or need)
from information technology.
business analytics (BA) The application of models directly to
business data. Business analytics involve using DSS tools, especially
models, in assisting decision makers. See also business intelligence
(BI).
business intelligence (BI) A conceptual framework for
managerial decision support. It combines architecture, databases (or
data warehouses), analytical tools, and applications. business
network A group of people who have some kind of commercial
relationship; for example, sellers and buyers, buyers among
themselves, buyers and suppliers, and colleagues and other
colleagues.
business performance management (BPM) An advanced
performance measurement and analysis approach that embraces
planning and strategy.
business process reengineering (BPR) A methodology for
introducing a fundamental change in specific business processes.
BPR is usually supported by an information system.
Caffe This is an open-source deep learning framework developed
at UC Berkeley and Berkeley AI Research.
case-based reasoning (CBR) A methodology in which knowledge
or inferences are derived from historical cases.
categorical data Data that represent the labels of multiple classes
used to divide a variable into specific groups.
certainty The business situation where complete knowledge is
available so that the decision maker knows exactly what the
outcome of each course of action will be.
certainty factors A popular technique for representing uncertainty
in expert systems where the belief in an event (or a fact or a
hypothesis) is expressed using the expert’s unique assessment.
chatbot A robot that a person can chat with (in a text or voice) and
get information and advice in natural language.
choice phase A phase where the actual decision and the
commitment to follow a certain course of action are made.
chromosome A candidate solution for a genetic algorithm.
classification Supervised induction used to analyze the historical
data stored in a database and to automatically generate a model that
can predict future behavior.
clickstream analysis The analysis of data that occur in the Web
environment.
clickstream data Data that provide a trail of the user’s activities
and show the user’s browsing patterns (e.g., which sites are visited,
which pages, how long).
cloud computing Information technology infrastructure
(hardware, software, applications, platform) that is available as a
service, usually as virtualized resources.
clustering Partitioning a database into segments in which the
members of a segment share similar qualities.
cognitive computing The application of knowledge derived from
cognitive science in order to simulate the human thought process
so that computers can exhibit or support decision-making and
problem-solving capabilities.
cognitive limits The limitations of the human mind related to
processing information.
cognitive search A new generation of search method that uses
artificial intelligence (e.g., advanced indexing, NLP, and machine
learning) to return results that are much more relevant to the user.
collaboration hub The central point of control for an e-market. A
single collaboration hub (c-hub), representing one e-market owner,
can host multiple collaboration spaces (c-spaces) in which trading
partners use c-enablers to exchange data with the c-hub.
collaborative filtering A method for generating recommendations
from user profiles. It uses preferences of other users with similar
behavior to predict the preferences of a particular user.
collaborative planning, forecasting, and replenishment
(CPFR) A project in which suppliers and retailers collaborate in
their planning and demand forecasting to optimize the flow of
materials along the supply chain.
collaborative workspace Is where people can work together from
any location at the same or a different time.
collective intelligence The total intelligence of a group. It is also
referred to as the wisdom of the crowd.
community of practice (COP) A group of people in an
organization with a common professional interest, often self-
organized, for managing knowledge in a knowledge management
system.
complexity A measure of how difficult a problem is in terms of its
formulation for optimization, its required optimization effort, or its
stochastic nature.
computer ethics Ethical behavior of people toward information
systems and computers in general.
computer vision Computer program that helps to recognize
scenery (photos, videos).
confidence In association rules, the conditional probability of
finding the RHS of the rule present in a list of transactions where
the LHS of the rule already exists.
connection weight The weight associated with each link in a neural
network model. Neural networks learning algorithms assess
connection weights.
208 Glossary
consultation environment The part of an expert system that a
nonexpert uses to obtain expert knowledge and advice. It includes
the workplace, inference engine, explanation facility, recommended
action, and user interface.
content management system (CMS) An electronic document
management system that produces dynamic versions of documents
and automatically maintains the current set for use at the enterprise
level.
content-based filtering A type of filtering that recommends items
for a user based on the description of previously evaluated items
and information available from the content (e.g., keywords).
convolution In convolutional neural networks, this is a linear
operation that aims at extracting simple patterns from sophisticated
data patterns.
convolution function This is a parameter sharing method to
address the issue of computational efficiency in defining and
training a very large number of weight parameters that exist in
CNN.
convolution layer This is a layer containing a convolution function
in a CNN.
convolutional neural networks (CNNs) These are among the
most popular deep learning methods. CNNs are in essence a
variation of the deep MLP-type neural network architecture, initially
designed for computer vision applications (e.g., image processing,
video processing, text recognition) but also applicable to nonimage
data sets.
corporate (enterprise) portal A gateway for entering a corporate
Web site. A corporate portal enables communication, collaboration,
and access to company information.
corpus In linguistics, a large and structured set of texts (now usually
stored and processed electronically) prepared for the purpose of
conducting knowledge discovery.
CRISP-DM A cross-industry standardized process of conducting
data mining projects, which is a sequence of six steps that starts with
a good understanding of the business and the need for the data
mining project (i.e., the application domain) and ends with the
deployment of the solution that satisfied the specific business need.
critical event processing A method of capturing, tracking, and
analyzing streams of data to detect certain events (out of normal
happenings) that are worthy of the effort.
critical success factors (CSF) Key factors that delineate the areas
that an organization must excel at to be successful in its market
space.
crowdsourcing Outsourcing tasks (work) to a large group of
people.
cube A subset of highly interrelated data that is organized to allow
users to combine any attributes in a cube (e.g., stores, products,
customers, suppliers) with any metrics in the cube (e.g., sales, profit,
units, age) to create various two-dimensional views, or slices, that can
be displayed on a computer screen.
customer experience management (CEM) Applications
designed to report on the overall user experience by detecting Web
application issues and problems, by tracking and resolving business
process and usability obstacles, by reporting on site performance
and availability, by enabling real-time alerting and monitoring, and
by supporting deep diagnosis of observed visitor behavior.
dashboard A visual presentation of critical data for executives to
view. It allows executives to see hot spots in seconds and explore
the situation.
data Raw facts that are meaningless by themselves (e.g., names,
numbers).
data cube A two-dimensional, three-dimensional, or
higherdimensional object in which each dimension of the data
represents a measure of interest.
data integration Integration that comprises three major processes:
data access, data federation, and change capture. When these three
processes are correctly implemented, data can be accessed and made
accessible to an array of ETL, analysis tools, and data warehousing
environments.
data integrity A part of data quality where the accuracy of the data
(as a whole) is maintained during any operation (such as transfer,
storage, or retrieval).
data mart A departmental data warehouse that stores only relevant
data.
data mining A process that uses statistical, mathematical, artificial
intelligence, and machine-learning techniques to extract and identify
useful information and subsequent knowledge from large databases.
data quality (DQ) The holistic quality of data, including their
accuracy, precision, completeness, and relevance.
data scientist A person employed to analyze and interpret complex
digital data to assist a business in decision making.
data stream mining The process of extracting novel patterns and
knowledge structures from continuously streaming data records. See
stream analytics. data visualization A graphical, animation, or video presentation of
data and the results of data analysis.
data warehouse (DW) A physical repository where relational data
are specially organized to provide enterprise-wide, cleansed data in
a standardized format.
database A collection of files that are viewed as a single storage
concept. The data are then available to a wide range of users.
database management system (DBMS) Software for
establishing, updating, and querying (e.g., managing) a database.
deception detection A way of identifying deception (intentionally
propagating beliefs that are not true) in voice, text, and/or body
language of humans.
decision analysis A modeling approach that deals with decision
situations that involve a finite and usually not too large number of
alternatives. decision making The action of selecting among
alternatives.
Glossary 209
decision or normative analytics Also called prescriptive analytics,
this is a type of analytics modeling that aims at identifying the best
possible decision from a large set of alternatives.
decision room Expensive, customized, special-purpose facility
with a group support system in which PCs are available to some or
all participants. The objective is to enhance group work.
decision support systems (DSS) A conceptual framework for a
process of supporting managerial decision making, usually by
modeling problems and employing quantitative models for solution
analysis.
decision table A tabular representation of possible condition
combinations and outcomes.
decision tree A graphical presentation of a sequence of interrelated
decisions to be made under assumed risk. This technique classifies
specific entities into particular classes based upon the features of
the entities; a root is followed by internal nodes, each node
(including root) is labeled with a question, and arcs associated with
each node cover all possible responses. decision variable The
variable of interest.
deep learning The newest and perhaps the most popular member
of the artificial intelligence and machine learning family, deep
learning has a goal similar to those of the other machine learning
methods that came before it: mimic the thought process of
humans—using mathematical algorithms to learn from data (both
representation of the variables and their interrelationships).
deep neural networks These are a part of deep learning algorithms
where numerous hidden layers of neurons are used to capture the
complex relationships from very large training data sets.
defuzzification The process of creating a crisp solution from a
fuzzy logic solution. dendrite The part of a biological neuron that provides inputs to the
cell.
dependent data mart A subset that is created directly from a data
warehouse.
descriptive (or reporting) analytics An earlier phase in analytics
continuum that deals with describing the data answering the
questions of what happened and why did it happen.
design phase This phase involves inventing, developing, and
analyzing possible courses of action.
development environment The part of an expert system that a
builder uses. It includes the knowledge base and the inference
engine, and it involves knowledge acquisition and improvement of
reasoning capability. The knowledge engineer and the expert are
considered part of the environment.
dimensional modeling A retrieval-based system that supports
high-volume query access.
directory A catalog of all the data in a database or all the models in
a model base.
discrete event simulation A type of simulation modeling where a
system is studied based on the occurrence of events/ interaction
between different parts (entities/resources) of the system.
distance measure A method used to calculate the closeness
between pairs of items in most cluster analysis methods. Popular
distance measures include Euclidean distance (the ordinary distance
between two points that one would measure with a ruler) and
Manhattan distance (also called the rectilinear distance, or taxicab
distance, between two points). distributed artificial intelligence
(DAI) A multiple-agent system for problem solving. DAI involves
splitting a problem into multiple cooperating systems to derive a
solution.
DMAIC A closed-loop business improvement model that includes
these steps: defining, measuring, analyzing, improving, and
controlling a process.
document management systems (DMS) Information sys- tems (e.g., hardware, software) that allow the flow, storage, retrieval,
and use of digitized documents.
drill-down The investigation of information in detail (e.g., finding
not only total sales but also sales by region, by product, or by
salesperson). Finding the detailed sources. DSS application A DSS
program built for a specific purpose (e.g., a scheduling system for a
specific company).
dynamic models A modeling technique to capture/study systems
that evolve over time.
Echo The speaker that works together with Alexa.
effectiveness The degree of goal attainment. Doing the right
things.
effectors An effector is a device designed for robots to interact with
the environment.
efficiency The ratio of output to input. Appropriate use of
resources. Doing things right. electronic brainstorming A computer-supported methodology of
idea generation by association. This group process uses analogy and
synergy.
electronic meeting systems (EMS) An information technology–
based environment that supports group meetings (groupware),
which may be distributed geographically and temporally.
ensembles (or more appropriately called model ensembles or
ensemble modeling) These are combinations of the outcomes
produced by two or more analytics models into a compound output.
Ensembles are primarily used for prediction modeling where the
scores of two or more models are combined to produce a better
prediction.
Enterprise 2.0 Technologies and business practices that free the
workforce from the constraints of legacy communication and
productivity tools such as e-mail. Provides business managers with
access to the right information at the right time through a Web of
interconnected applications, services, and devices.
enterprise application integration (EAI) A technology that
provides a vehicle for pushing data from source systems into a data
warehouse.
enterprise data warehouse (EDW) An organizational-level data
warehouse developed for analytical purposes.
210 Glossary
entropy A metric that measures the extent of uncertainty or
randomness in a data set. If all the data in a subset belong to just
one class, then there is no uncertainty or randomness in that data
set, and therefore the entropy is zero.
environmental scanning and analysis A continuous process of
intelligence building identification of problems and/or
opportunities via acquisition and analysis of data/ information.
evolutionary algorithm A class of heuristic-based optimization
algorithms modeled after the natural process of biological
evolution, such as genetic algorithms and genetic programming.
expert A human being who has developed a high level of
proficiency in making judgments in a specific, usually narrow,
domain.
expert location system An interactive computerized system that
helps employees find and connect with colleagues who have
expertise required for specific problems—whether they are across
the county or across the room—in order to solve specific, critical
business problems in seconds.
expert system (ES) shell A computer program that facilitates
relatively easy implementation of a specific expert system.
Analogous to a DSS generator.
expert systems Computerized systems that transfer expert and
documented knowledge to machines that help nonexperts use this
knowledge for decision making.
expertise The set of capabilities that underlines the performance of
human experts, including extensive domain knowledge, heuristic
rules that simplify and improve approaches to problem solving,
metaknowledge and metacognition, and collective forms of
behavior that afford great economy in a skilled performance.
explanation subsystem The component of an expert system that
can explain the system’s reasoning and justify its conclusions.
explicit knowledge Knowledge that deals with objective, rational,
and technical material (e.g., data, policies, procedures, software,
documents). Also known as leaky knowledge.
extraction The process of capturing data from several sources,
synthesizing them, summarizing them, determining which of them
are relevant, and organizing them, resulting in their effective
integration.
facilitator (in a GSS) A person who plans, organizes, and
electronically controls a group in a collaborative computing
environment.
forecasting Using the data from the past to foresee the future
values of a variable of interest.
forward chaining A data-driven search in a rule-based system.
functional integration The provision of different support
functions as a single system through a single, consistent interface.
fuzzification A process that converts an accurate number into a
fuzzy description, such as converting from an exact age into
categories such as young and old.
fuzzy logic A logically consistent way of reasoning that can cope
with uncertain or partial information. Fuzzy logic is characteristic
of human thinking and expert systems.
fuzzy set A set theory approach in which set membership is less
precise than having objects strictly in or out of the set.
genetic algorithm A software program that learns in an
evolutionary manner, similar to the way biological systems evolve.
geographic information systems (GIS) An information system
capable of integrating, editing, analyzing, sharing, and displaying
geographically referenced information.
Gini index A metric that is used in economics to measure the
diversity of the population. The same concept can be used to
determine the purity of a specific class as a result of a decision to
branch along a particular attribute/variable.
global positioning systems (GPS) Wireless devices that use
satellites to enable users to detect the position on earth of items
(e.g., cars or people) the devices are attached to, with reasonable
precision.
goal seeking A prescriptive analytics method where first a goal (a
target/desired value) is set, and then the satisfying set of input
variable values is identified.
Google Assistant An upcoming virtual personal assistant for use
in several of Google’s products.
grain A definition of the highest level of detail that is supported in
a data warehouse.
graphic processing unit (GPU) It is the part of a computer that
normally processes/renders graphical outputs; nowadays, it is also
being used for efficient processing of deep learning algorithms.
graphical user interface (GUI) An interactive, user-friendly
interface in which, by using icons and similar objects, the user can
control communication with a computer.
group decision making A situation in which people make
decisions together.
group decision support system (GDSS) An interactive
computer-based system that facilitates the solution of
semistructured and unstructured problems by a group of decision
makers.
group support system (GSS) Information system, specifically
DSS, that supports the collaborative work of groups.
group work Any work being performed by more than one person.
groupthink Continual reinforcement of an idea by group members
in a meeting.
groupware Computerized technologies and methods that aim to
support people working in groups.
groupwork Any work being performed by more than one person.
Hadoop An open-source framework for processing, storing, and
analyzing massive amounts of distributed, unstructured data.
Hadoop Distributed File System (HDFS) A distributed file
management system that lends itself well to processing large
volumes of unstructured data (i.e., Big Data).
heterogeneous ensembles These combine the outcomes of two
or more different types of models such as decision trees, artificial
neural networks, logistic regression, support vector machines, and
others.
Glossary 211
heuristic programming The use of heuristics in problem solving.
heuristics Informal, judgmental knowledge of an application area
that constitutes the rules of good judgment in the field. Heuristics
also encompasses the knowledge of how to solve problems
efficiently and effectively, how to plan steps in solving a complex
problem, how to improve performance, and so forth.
hidden layer The middle layer of an artificial neural network that
has three or more layers.
Hive Hadoop-based data warehousing like framework originally
developed by Facebook.
homogeneous ensembles combine the outcomes of two or more
of the same type of models such as decision trees.
hub One or more Web pages that provide a collection of links to
authoritative pages.
hybrid (integrated) computer system Different but integrated
computer support systems used together in one decision-making
situation. hyperlink-induced topic search (HITS) The most popular
publicly known and referenced algorithm in Web mining used to
discover hubs and authorities.
hyperplane A geometric concept commonly used to describe the
separation surface between different classes of things within a
multidimensional space.
hypothesis-driven data mining A form of data mining that begins
with a proposition by the user, who then seeks to validate the
truthfulness of the proposition. IBM Watson It is an extraordinary
computer system—a novel combination of advanced hardware,
software, and machine-learning algorithms—designed to answer
questions posed in natural human language.
IBM SPSS Modeler A very popular, commercially available,
comprehensive data, text, and Web mining software suite developed
by SPSS (formerly Clementine).
idea generation The process by which people generate ideas,
usually supported by software (e.g., developing alternative solutions
to a problem). Also known as brainstorming.
ImageNet This is an ongoing research project that provides
researchers with a large database of images, each linked to a set of
synonym words (known as synset) from WordNet (a word hierarchy
database).
implementation phase A phase that involves putting a
recommended solution to work, not necessarily implementing a
computer system.
inference engine The part of an expert system that actually
performs the reasoning function.
influence diagram A graphical representation of a given
mathematical model.
information Data organized in a meaningful way.
information fusion (or simply, fusion) A type of heterogeneous
model ensembles that combines different types of prediction
models using a weighted average, where the weights are determined
from the individual models’ predictive accuracies.
information gain The splitting mechanism used in ID3 (a popular
decision-tree algorithm).
information overload An excessive amount of information being
provided, making processing and absorbing tasks very difficult for
the individual.
intelligence A degree of reasoning and learned behavior, usually
task or problem-solving oriented.
intelligence phase A phase where the decision maker examines
reality and identifies and defines the problem.
intelligent agent An autonomous, small computer program that
acts upon changing environments as directed by stored knowledge.
intelligent database A database management system ex- hibiting artificial intelligence features that assist the user or designer;
often includes ES and intelligent agents. interactivity A
characteristic of software agents that allows them to interact
(communicate and/or collaborate) with each other without having
to rely on human intervention.
intermediate result variable A variable used in modeling to
identify intermediate outcomes.
Internet of Things (IoT) The technological phenomenon of
connecting a variety of devices in the physical world to each other
and to the computing systems via the Internet.
Internet of Things ecosystem All components that enable
organizations to use IoT; includes the “things,” connections,
features, procedures, analytics, data, and security.
Internet telephony See Voice over IP (VoIP).
interval data Variables that can be measured on interval scales.
inverse document frequency A common and very useful
transformation of indices in a term-by-document matrix that
reflects both the specificity of words (document frequencies) as well
as the overall frequencies of their occurrences (term frequencies).
iterative design A systematic process for system development that
is used in management support systems (MSS). Iterative design
involves producing a first version of MSS, revising it, producing a
second design version, and so on.
Keras An open-source neural network library written in Python that
functions as a high-level application programming interface (API)
and is able to run on top of various deep learning frameworks
including Theano and TensorFlow.
kernel trick In machine learning, a method for using a linear
classifier algorithm to solve a nonlinear problem by mapping the
original nonlinear observations onto a higher- dimensional space,
where the linear classifier is subsequently used; this makes a linear
classification in the new space equivalent to a nonlinear
classification in the original space.
kernel type In kernel trick, a type of transformation algorithm used
to represent data items in a Euclidean space. The most commonly
used kernel type is the radial basis function. key performance
indicator (KPI) Measure of performance against a strategic
objective and goal.
k-fold cross-validation A popular accuracy assessment technique
for prediction models where the complete data set is randomly split
212 Glossary
into k mutually exclusive subsets of approximately equal size. The
classification model is trained and tested k times. Each time it is
trained on all but one fold and then tested on the remaining single
fold. The cross-validation estimate of the overall accuracy of a
model is calculated by simply averaging the k individual accuracy
measures.
k-nearest neighbor (k-NN) A prediction method for
classification as well as regression-type prediction problems where
the prediction is made based on the similarity to k neighbors.
KNIME An open-source, free-of-charge, platform-agnostic
analytics software tool (available at www.knime.org).
knowledge Understanding, awareness, or familiarity acquired
through education or experience; anything that has been learned,
perceived, discovered, inferred, or understood; the ability to use
information. In a knowledge management system, knowledge is
information in action.
knowledge acquisition The extraction and formulation of
knowledge derived from various sources, especially from experts.
knowledge audit The process of identifying the knowledge an
organization has, who has it, and how it flows (or does not) through
the enterprise.
knowledge base A collection of facts, rules, and procedures
organized into schemas. A knowledge base is the assembly of all the
information and knowledge about a specific field of interest.
knowledge discovery in databases (KDD) A machinelearning
process that performs rule induction or a related procedure to
establish knowledge from large databases.
knowledge engineer An artificial intelligence specialist responsible
for the technical side of developing an expert system. The
knowledge engineer works closely with the domain expert to
capture the expert’s knowledge in a knowledge base.
knowledge engineering The engineering discipline in which
knowledge is integrated into computer systems to solve complex
problems that normally require a high level of human expertise.
knowledge management system (KMS) A system that facilitates
knowledge management by ensuring knowledge flow from the
person(s) who knows to the person(s) who needs to know
throughout the organization; knowledge evolves and grows during
the process.
knowledge management The active management of the
expertise in an organization. It involves collecting, categorizing, and
disseminating knowledge.
knowledge repository The actual storage location of knowledge in
a knowledge management system. A knowledge repository is similar
in nature to a database but is generally text oriented.
knowledge rules A collection of if-then rules that represents the
deep knowledge about a specific problem. knowledge-based
economy The modern, global economy, which is driven by what
people and organizations know rather than only by capital and
labor. An economy based on intellectual assets.
knowledge-based system (KBS) Typically, a rule-based system
for providing expertise. A KBS is identical to an expert system,
except that the source of expertise may include documented
knowledge.
knowledge-refining system A system that is capable of analyzing
its own performance, learning, and improving itself for future
consultations.
Kohonen self-organizing feature map (SOM) A type of neural
network model for machine learning. leaky knowledge See explicit
knowledge.
learning A process of self-improvement where the new knowledge
is obtained through a process by using what is already known. learning algorithm The training procedure used by an artificial
neural network.
learning organization An organization that is capable of learning
from its past experience, implying the existence of an organizational
memory and a means to save, represent, and share it through its
personnel.
learning rate A parameter for learning in neural networks. It
determines the portion of the existing discrepancy that must be
offset.
linear programming (LP) A mathematical modeling technique
used to represent and solve constraint optimization problems.
link analysis The linkage among many objects of interest is
discovered automatically, such as the link between Web pages and
referential relationships among groups of academic publication
authors.
literature mining A popular application area for text mining where
a large collection of literature (articles, abstracts, book excerpts, and
commentaries) in a specific area is processed using semiautomated
methods in order to discover novel patterns.
long short-term memory (or LSTM) networks A variation of
recurrent neural networks that are known as the most effective
sequence modeling techniques and are the foundation of many
practical applications.
machine learning Teaching computers to learn from examples
and large amounts of data.
machine vision Technology and methods used to provide image-
based automated inspection and analysis for applications such as
robot guides, process controls, automated vehicles, and inspections.
management science (MS) The application of a scientific
approach and mathematical models to the analysis and s olution of
managerial decision situations (e.g., problems, opportunities). Also
known as operations research (OR).
management support system (MSS) A system that applies any
type of decision support tool or technique to managerial decision
making.
MapReduce A technique to distribute the processing of very large
multistructured data files across a large cluster of machines.
mathematical (quantitative) model A system of symbols and
expressions that represent a real situation.
mathematical programming A family of analytic tools designed
to help solve managerial problems in which the decision maker
Glossary 213
must allocate scarce resources among competing activities to
optimize a measurable goal.
mental model The mechanisms or images through which a human
mind performs sense-making in decision making.
metadata Data about data. In a data warehouse, metadata describe
the contents of a data warehouse and the manner of its use. middleware Software that links application modules from different
computer languages and platforms.
mobile agent An intelligent software agent that moves across
different system architectures and platforms or from one Internet
site to another, retrieving and sending information.
mobility The degree to which agents travel through a computer
network.
model base management system (MBMS) Software for
establishing, updating, combining, and so on (e.g., managing) a DSS
model base.
model base A collection of preprogrammed quantitative models
(e.g., statistical, financial, optimization) organized as a single unit.
model mart A small, generally departmental repository of
knowledge created by using knowledge-discovery techniques on
past decision instances. Model marts are similar to data marts. See
model warehouse.
model warehouse A large, generally enterprise-wide repository of
knowledge created by using knowledge discovery techniques on
past decision instances. Model warehouses are similar to data
warehouses. See model mart.
momentum A learning parameter in backpropagation neural
networks.
Monte Carlo simulation A simulation technique that relies on
change/probability distribution to represent the uncertainty in the
modeling of the decision problem.
multiagent system A system with multiple cooperating software
agents.
multidimensional analysis (modeling) A modeling method that
involves data analysis in several dimensions.
multidimensional database A database in which the data are
organized specifically to support easy and quick multidimensional
analysis.
multidimensional OLAP (MOLAP) OLAP implemented via a
specialized multidimensional database (or data store) that
summarizes transactions into multidimensional views ahead of time.
multidimensionality The ability to organize, present, and analyze
data by several dimensions, such as sales by region, by product, by
salesperson, and by time (four dimensions).
multiple goals Having more than just one goal to consider in an
optimization problem.
mutation A genetic operator that causes a random change in a
potential solution.
naïve Bayes A simple probability-based classification method
derived from the well-known Bayes’ theorem. It is one of the
machine-learning techniques applicable to classificationtype
prediction problems.
narrow (weak) AI A form of AI specifically designed to be focused
on a narrow task and to seem very intelligent at it.
natural language processing Technology that allows people to
communicate with a computer in their native language. neural
(computing) networks A computer design aimed at building
intelligent computers that operate in a manner modeled on the
functioning of the human brain.
neural computing An experimental computer design aimed at
building intelligent computers that operate in a manner modeled on
the functioning of the human brain. See artificial neural network
(ANN).
neural network See artificial neural network (ANN).
neuron A cell (i.e., processing element) of a biological or artificial
neural network.
nominal data A type of data that contains measurements of simple
codes assigned to objects as labels, which are not measurements.
For example, the variable marital status can be generally categorized
as (1) single, (2) married, and (3) divorced.
nominal group technique (NGT) A simple brainstorming
process for nonelectronic meetings.
normative model A model that prescribes how a system should
operate.
NoSQL (a.k.s. not only SQL) A new paradigm to store and
process large volumes of unstructured, semistructured, and
multistructured data. nucleus The central processing portion of a
neuron.
numeric data A type of data that represent the numeric values of
specific variables. Examples of numerically valued variables include
age, number of children, total household income (in U.S. dollars),
travel distance (in miles), and temperature (in Fahrenheit degrees).
object A person, place, or thing about which information is
collected, processed, or stored.
object-oriented model base management system (OOMBMS)
An MBMS constructed in an object-oriented environment.
online (electronic) workspace Online screens that allow people
to share documents, files, project plans, calendars, and so on in the
same online place, though not necessarily at the same time.
online analytical processing (OLAP) An information system that
enables the user, while at a PC, to query the system, conduct an
analysis, and so on. The result is generated in seconds.
online transaction processing (OLTP) A transaction system that
is primarily responsible for capturing and storing data related to day-
to-day business functions.
online workspace A place where participants can collaborate while
working in different time.
operational data store (ODS) A type of database often used as an
interim area for a data warehouse, especially for customer
information files.
214 Glossary
operational models Models that represent problems for the
operational level of management.
operational plan A plan that translates an organization’s strategic
objectives and goals into a set of well-defined tactics and initiatives,
resource requirements, and expected results. optimal solution The
best possible solution to a problem. optimization The process of identifying the best possible solution
to a problem.
ordinal data Data that contain codes assigned to objects or events
as labels that also represent the rank order among them. For
example, the variable credit score can be generally categorized as (1)
low, (2) medium, and (3) high.
organizational agent An agent that executes tasks on behalf of a
business process or computer application.
organizational culture The aggregate attitudes in an organization
concerning a certain issue (e.g., technology, computers, DSS).
organizational knowledge base An organization’s knowledge
repository.
organizational learning The process of capturing knowledge and
making it available enterprise-wide. organizational memory That
which an organization knows.
ossified case A case that has been analyzed and has no further
value.
PageRank A link analysis algorithm, named after Larry Page—one
of the two founders of Google as a research project at Stanford
University in 1996, and used by the Google Web search engine.
paradigmatic case A case that is unique and that can be
maintained to derive new knowledge for the future.
parallel processing An advanced computer processing technique
that allows a computer to perform multiple processes at once, in
parallel.
parallelism In a group support system, a process gain in which
everyone in a group can work simultaneously (e.g., in brainstorming,
voting, ranking).
parameter Numeric constants used in mathematical modeling.
part-of-speech tagging The process of marking up the words in a
text as corresponding to a particular part of speech (such as nouns,
verbs, adjectives, adverbs, etc.) based on a word’s definition and
context of its use.
patents A right granted for exclusive royalty or copyright for novel
inventions that would not have been obvious improvements of a
known technology.
pattern recognition A technique of matching an external pattern
to a pattern stored in a computer’s memory (i.e., the process of
classifying data into predetermined categories). Pattern recognition
is used in inference engines, image processing, neural computing,
and speech recognition.
perceptron An early neural network structure that uses no hidden
layer.
performance measurement system A system that assists
managers in tracking the implementations of business strategy by
comparing actual results against strategic goals and objectives.
perpetual analytics An analytics practice that continuously
evaluates every incoming data point (i.e., observation) against all
prior observations to identify patterns/ anomalies.
personal agent An agent that performs tasks on behalf of
individual users.
physical integration The seamless integration of several systems
into one functioning system.
Pig A Hadoop-based query language developed by Yahoo!.
polysemes Words also called homonyms, they are syntactically
identical words (i.e., spelled exactly the same) with different
meanings (e.g., bow can mean “to bend forward,” “the front of the
ship,” “the weapon that shoots arrows,” or “a kind of tied ribbon”).
pooling In CNN, it refers to the process of consolidating the
elements in the input matrix in order to produce a smaller output
matrix, while maintaining the important features.
portal A gateway to Web sites. Portals can be public (e.g., Yahoo!)
or private (e.g., corporate portals).
practice approach An approach toward knowledge management
that focuses on building the social environments or communities of
practice necessary to facilitate the sharing of tacit understanding.
prediction The act of telling about the future.
predictive analysis Use of tools that help determine the probable
future outcome for an event or the likelihood of a situation
occurring. These tools also identify relationships and patterns.
predictive analytics A business analytical approach toward
forecasting (e.g., demand, problems, opportunities) that is used
instead of simply reporting data as they occur.
prescriptive analytics A branch of business analytics that deals
with finding the best possible solution alternative for a given
problem.
principle of choice The criterion for making a choice among
alternatives.
privacy Right to be left alone and to be free from unreasonable
personal intrusions. private agent An agent that works for only one
person.
problem ownership The jurisdiction (authority) to solve a
problem.
problem solving A process in which one starts from an initial state
and proceeds to search through a problem space to identify a
desired goal.
process approach An approach to knowledge management that
attempts to codify organizational knowledge through formalized
controls, processes, and technologies.
process gain In a group support system, improvements in the
effectiveness of the activities of a meeting.
process loss In a group support system, degradation in the
effectiveness of the activities of a meeting.
processing element (PE) A neuron in a neural network.
production rules The most popular form of knowledge
representation for expert systems where atomic pieces of
knowledge are represented using simple if-then structures.
Glossary 215
prototyping In system development, a strategy in which a scaled-
down system or portion of a system is constructed in a short time,
tested, and improved in several iterations. public agent An agent
that serves any user.
quantitative model Mathematical models that rely on
numeric/quantifiable measures.
quantitative software package A preprogrammed (sometimes
called ready-made) model or optimization system. These packages
sometimes serve as building blocks for other quantitative models.
query facility The (database) mechanism that accepts requests for
data, accesses them, manipulates them, and queries them.
radio-frequency identification (RFID) A form of wireless
communication between tags (integrated circuit with an antenna)
and readers (also called an interrogator) to uniquely identify an
object.
random forests First introduced by Breiman (2000) as a
modification to the simple bagging algorithm, it uses bootstrapped
samples of data and a randomly selected subset of variables to build
a number of decision trees, and then combines their output via the
simple voting.
rapid application development (RAD) A development
methodology that adjusts a system development life cycle so that
parts of the system can be developed quickly, thereby enabling users
to obtain some functionality as soon as possible. RAD includes
methods of phased development, prototyping, and throwaway
prototyping.
RapidMiner A popular, open-source, free-of-charge data mining
software suite that employs a graphically enhanced user interface, a
rather large number of algorithms, and a variety of data visualization
features.
ratio data Continuous data where both differences and ratios are
interpretable. The distinguishing feature of a ratio scale is the
possession of a nonarbitrary zero value. reality mining Data
mining of location-based data.
real-time data warehousing The process of loading and providing
data via a data warehouse as they become available.
real-time expert system An expert system designed for online
dynamic decision support. It has a strict limit on response time; in
other words, the system always produces a response by the time it
is needed.
recommendation system (agent) A computer system that can
suggest new items to a user based on his or her revealed preference.
It may be content based or use collaborative filtering to suggest
items that match the preference of the user. An example is
Amazon.com’s “Customers who bought this item also bought . .
.” feature.
recommendation systems Systems that recommend products and
services to individuals based on what they know about the
individuals’ preferences recurrent neural networks (RNNs) The
type of neural networks that have memory and can apply that
memory to determine the future outputs.
regression A data mining method for real-world prediction
problems where the predicted values (i.e., the output variable or
dependent variable) are numeric (e.g., predicting the temperature
for tomorrow as 68°F).
reinforcement learning A sub-area of machine learning that is
concerned with learning-by-doing-and-measuring to maximize
some notion of long-term reward. Reinforcement learning differs
from supervised learning in that correct input/output pairs are
never presented to the algorithm.
relational database A database whose records are organized into
tables that can be processed by either relational algebra or relational
calculus.
relational model base management system (RMBMS) A
relational approach (as in relational databases) to the design and
development of a model base management system.
relational OLAP (ROLAP) The implementation of an OLAP
database on top of an existing relational database.
report Any communication artifact prepared with the specific
intention of conveying information in a presentable form.
representation learning A type of machine learning in which the
emphasis is on learning and discovering features/ variables by the
system in addition to mapping of those features to the output/target
variable.
reproduction The creation of new generations of improved
solutions with the use of a genetic algorithm.
result (outcome) variable A variable that expresses the result of a
decision (e.g., one concerning profit), usually one of the goals of a
decision-making problem.
revenue management systems Decision-making systems used to
make optimal price decisions in order to maximize revenue, based
upon previous demand history as well as forecasts of demand at
various pricing levels and other considerations.
RFID A generic technology that refers to the use of radiofrequency
waves to identify objects. risk A probabilistic or stochastic decision
situation.
risk analysis Use of mathematical modeling to assess the nature of
risk (variability) for a decision situation.
robo advisors Virtual personal assistants that contain professional
knowledge so they can advise people in several fields, such as in
finance and investment.
robot Electromechanical device that is guided by a computer
program to perform physical and mental activities.
rule-based system A system in which knowledge is represented
completely in terms of rules (e.g., a system based on production
rules). SAS Enterprise Miner A comprehensive, commercial data mining
software tool developed by SAS Institute.
satisficing A process by which one seeks a solution that will satisfy
a set of constraints. In contrast to optimization, which seeks the
best possible solution, satisficing simply seeks a solution that will
work well enough.
216 Glossary
scenario A statement of assumptions and configurations
concerning the operating environment of a particular system at a
particular time.
scene recognition Activity performed by a computer vision that
enables recognition of objects, scenery, and photos.
scorecard A visual display that is used to chart progress against
strategic and tactical goals and targets.
screen sharing Software that enables group members, even in
different locations, to work on the same document, which is shown
on the PC screen of each participant.
search engine A program that finds and lists Web sites or pages
(designated by URLs) that match some user-selected criteria.
search engine optimization (SEO) The intentional activity of
affecting the visibility of an e-commerce site or a Web site in a
search engine’s natural (unpaid or organic) search results.
self-organizing A neural network architecture that uses
unsupervised learning.
semantic Web An extension of the current Web, in which
information is given well-defined meanings, better enabling
computers and people to work in cooperation.
semantic Web services An XML-based technology that allows
semantic information to be represented in Web services.
semistructured problem A category of decision problems where
the decision process has some structure to it but still requires
subjective analysis and an iterative approach.
SEMMA An alternative process for data mining projects proposed
by the SAS Institute. The acronym “SEMMA” stands for “sample,
explore, modify, model, and assess.” sensitivity analysis A study
of the effect of a change in one or more input variables on a
proposed solution.
sensitivity analysis simulation The process to investigate the
effect of varying a fixed input or a distribution parameter for a
simulated input over a specified set of values.
sensor Electronic device that automatically collects data about
events or changes in its environment. sentiment A settled opinion
reflective of one’s feelings.
sentiment analysis The technique used to detect favorable and
unfavorable opinions toward specific products and services using a
large number of textual data sources (customer feedback in the form
of Web postings).
SentiWordNet An extension of WordNet to be used for sentiment
identification. See WordNet.
sequence discovery The identification of associations over time. sequence mining A pattern discovery method where relationships
among the things are examined in terms of their order of occurrence
to identify associations over time.
shopbot Robot that helps with online shopping by collecting
shopping information (search) and conducting price and capability
comparisons.
sigmoid (logical activation) function An S-shaped transfer
function in the range of 0 to 1.
simple split Data are partitioned into two mutually exclusive
subsets called a training set and a test set (or holdout set). It is common
to designate two-thirds of the data as the training set and the
remaining one-third as the test set. simulation An imitation of
reality in computers.
singular value decomposition (SVD) Closely related to principal
components analysis, reduces the overall dimensionality of the
input matrix (number of input documents by number of extracted
terms) to a lower dimensional space, where each consecutive
dimension represents the largest degree of variability (between
words and documents). Siri Virtual intelligent personal assistant
from Apple Computer.
Six Sigma A performance management methodology aimed at
reducing the number of defects in a business process to as close to
zero defects per million opportunities (DPMO) as possible.
smart appliances Appliances with sensors or smart sensors that
occupy smart homes and can be controlled from a distance.
smart cities Cities where many smart things are connected and
controlled, including transportation, government services,
emergency services, medical services, educational systems, utilities,
and possibly homes and public buildings.
smart factory A flexible system that can self-optimize performance
across a broader network and self-adapt to and learn from new
conditions.
smart homes Homes where the appliances, security,
entertainment, and other components are automated,
interconnected (frequently wirelessly), and centrally controlled (e.g.,
via smartphone apps).
smart sensors Sensors with add-on microprocessing capability and
possibly other features to best support IoT by processing the
collected data.
social analytics The monitoring, analyzing, measuring, and
interpreting digital interactions and relationships of people, topics,
ideas, and content.
social media The online platforms and tools that people use to
share opinions, experiences, insights, perceptions, and various
media, including photos, videos, or music, with each other. The
enabling technologies of social interactions among people in which
they create, share, and exchange information, ideas, and opinions in
virtual communities and networks.
social media analytics The systematic and scientific way to
consume the vast amount of content created by Webbased social
media outlets, tools, and techniques for the betterment of an
organization’s competitiveness.
social network analysis (SNA) The mapping and measuring of
relationships and information flows among people, groups,
organizations, computers, and other information- or knowledge-
processing entities. The nodes in the network are the people and
groups, whereas the links show relationships or flows between the
nodes.
social robots An autonomous robot that interacts and
communicates with humans or other autonomous physical agents
by following social behaviors and rules attached to its role.
Glossary 217
software agent A piece of autonomous software that persists to
accomplish the task it is designed for (by its owner).
software-as-a-service (SaaS) Software that is rented instead of
sold.
Spark An open-source engine developed specifically for handling
large-scale data processing for analytics.
speech (voice) understanding Computer systems that attempt to
understand words or phrases of human speech, i.e., the natural
language spoken by people.
speech analytics A growing field of science that allows users to
analyze and extract information from both live and recorded
conversations.
stacking (a.k.a. stacked generalization or super learner) A part of
heterogeneous ensemble methods where a two-step modeling
process is used—first the individual prediction models of different
types are built and then a meta-model (a model of the individual
models) is built.
staff assistant An individual who acts as an assistant to a manager.
static models A model that captures a snapshot of the system,
ignoring its dynamic features.
status report A report that provides the most current information
on the status of an item (e.g., orders, expenses, production quantity).
stemming A process of reducing words to their respective root
forms in order to better represent them in a text mining project.
stochastic gradient boosting First created by Jerry Friedman at
Stanford University in 2001, this is a popular boosting algorithm
that uses prediction residuals/errors to guide the gradual
development of the future decision trees.
stop words Words that are filtered out prior to or after processing
of natural language data (i.e., text).
story A case with rich information and episodes. Lessons may be
derived from this kind of case in a case base.
strategic goal A quantified objective that has a designated time
period.
strategic models Models that represent problems for the strategic
level (i.e., executive level) of management.
strategic objective A broad statement or general course of action
that prescribes targeted directions for an organization. strategic
theme A collection of related strategic objectives used to simplify
the construction of a strategic map.
strategic vision A picture or mental image of what the organization
should look like in the future.
strategy map A visual display that delineates the relationships
among the key organizational objectives for all four balanced
scorecard perspectives.
stream analytics A term commonly used for extracting actionable
information from continuously flowing/streaming data sources.
strong (general) AI A form of AI capable of all and any cognitive
functions that a human may have and is in essence no different from
a real human mind.
structured problem A decision situation where a specific set of
steps can be followed to make a straightforward decision.
structured query language (SQL) A data definition and
management language for relational databases. SQL front ends
most relational DBMS.
summation function A mechanism to add all the inputs coming
into a particular neuron.
supervised learning A method of training artificial neural
networks in which sample cases are shown to the network as input,
and the weights are adjusted to minimize the error in the outputs.
support The measure of how often products and/or services
appear together in the same transaction; that is, the proportion of
transactions in the data set that contain all of the products and/or
services mentioned in a specific rule.
support vector machines (SVM) A family of generalized linear
models, which achieve a classification or regression decision based
on the value of the linear combination of input features.
swarm intelligence Collective behavior of a decentralized, self-
organized system, natural or artificial.
synapse The connection (where the weights are) between
processing elements in a neural network.
synchronous (real-time) Occurring at the same time.
system architecture The logical and physical design of a system.
system development lifecycle (SDLC) A systematic process for
the effective construction of large information systems.
systems dynamics Macro-level simulation models in which
aggregate values and trends are considered. The objective is to study
the overall behavior of a system over time, rather than the behavior
of each individual participant or player in the system.
tacit knowledge Knowledge that is usually in the domain of
subjective, cognitive, and experiential learning. It is highly personal
and difficult to formalize.
tactical models Models that represent problems for the tactical
level (i.e., midlevel) of management. teleconferencing The use of electronic communication that allows
two or more people at different locations to have a simultaneous
conference.
TensorFlow A popular open-source deep learning framework
originally developed by the Google Brain Group in 2011 as
DistBelief, and further developed into TensorFlow in 2015.
term–document matrix (TDM) A frequency matrix created from
digitized and organized documents (the corpus) where the columns
represent the terms while rows represent the individual documents.
text analytics A broader concept that includes information
retrieval (e.g., searching and identifying relevant documents for a
given set of key terms) as well as information extraction, data
mining, and Web mining.
text mining The application of data mining to nonstructured or
less structured text files. It entails the generation of meaningful
numeric indices from the unstructured text and then processing
those indices using various data mining algorithms.
218 Glossary
Theano This was developed by the Deep Learning Group at the
University of Montreal in 2007 as a Python library to define,
optimize, and evaluate mathematical expressions involving
multidimensional arrays (i.e., tensors) on CPU or GPU platforms.
theory of certainty factors A theory designed to help incorporate
uncertainty into the representation of knowledge (in terms of
production rules) for expert systems.
threshold value A hurdle value for the output of a neuron to trigger
the next level of neurons. If an output value is smaller than the
threshold value, it will not be passed to the next level of neurons.
tokenizing Categorizing a block of text (token) according to the
function it performs.
topology The way in which neurons are organized in a neural
network.
Torch An open-source scientific computing framework for
implementing machine-learning algorithms using GPUs.
tort liability In common law jurisdictions, this is where a wrongful
act creates an obligation to pay damages to another.
transformation (transfer) function In a neural network, the
function that sums and transforms inputs before a neuron fires. It
shows the relationship between the internal activation level and the
output of a neuron.
trend analysis The collecting of information and attempting to
spot a pattern, or trend, in the information.
Turing Test Test to determine whether computers are intelligent
when a human interviewer questions a human and a machine and is
unable to determine which is which.
uncertainty A decision situation where there is a complete lack of
information about what the parameter values are or what the future
state of nature will be. uncontrollable variable (parameter) A factor that affects the
result of a decision but is not under the control of the decision
maker. These variables can be internal (e.g., related to technology
or to policies) or external (e.g., related to legal issues or to climate).
uncontrollable variable A mathematical modeling variable that
has to be taken as given—not allowing changes/ modifications.
universal basic income (UBI) A proposal to give every citizen a
minimum amount of income to ensure no one goes hungry despite
the massive loss of jobs that is likely to occur.
unstructured data Data that do not have a predetermined format
and are stored in the form of textual documents.
unstructured problem A decision setting where the steps are not
entirely fixed or structured, but may require subjective
considerations.
unsupervised learning A method of training artificial neural
networks in which only input stimuli are shown to the network,
which is self-organizing.
user interface The component of a computer system that allows
bidirectional communication between the system and its user.
user interface management system (UIMS) The DSS
component that handles all interaction between users and the
system.
user-developed MSS An MSS developed by one user or by a few
users in one department, including decision makers and
professionals (i.e., knowledge workers—financial analysts, tax
analysts, engineers) who build or use computers to solve problems
or enhance their productivity.
utility (on-demand) computing Unlimited computing power and
storage capacity that, like electricity, water, and telephone services,
can be obtained on demand, used, and reallocated for any
application and that are billed on a payper-use basis.
vendor-managed inventory (VMI) The practice of retailers
making suppliers responsible for determining when to order and
how much to order.
video teleconferencing (videoconferencing) Virtual meeting in
which participants in one location can see participants at other
locations on a large screen or a desktop computer.
virtual (Internet) community A group of people with similar
interests who interact with one another using the Internet.
virtual meeting An online meeting whose members are in different
locations, possibly in different countries.
virtual personal assistant (VPA) A chatbot that assists individuals
by searching for information for them, answering questions, and
executing simple tasks. Most well known is Alexa from
Amazon.com.
virtual team A team whose members are in different places while
in a meeting together. virtual worlds Artificial worlds created by computer systems in
which the user has the impression of being immersed.
visual analytics The combination of visualization and predictive
analytics.
visual interactive modeling (VIM) A visual model representation
technique that allows for user and other system interactions.
visual interactive modeling (VIM) See visual interactive
simulation (VIS).
visual interactive simulation (VIS) A visual/animated simulation
environment that allows for the end user to interact with the model
parameters while the mode is running.
visual recognition The addition of some form of computer
intelligence and decision making to digitized visual information
received from a machine sensor such as a camera.
voice (speech) recognition Translation of human voice into
individual words and sentences that are understandable by a
computer.
voice of customer (VOC) Applications that focus on “who and
how” questions by gathering and reporting direct feedback from
site visitors, by benchmarking against other sites and offline
channels, and by supporting predictive modeling of future visitor
behavior.
voice-over IP (VoIP) Communication systems that transmit voice
calls over Internet Protocol (IP)–based networks. Also known as
Internet telephony.
voice portal A Web site, usually a portal, that has an audio interface.
Glossary 219
voice synthesis The technology by which computers convert text
to voice (i.e., speak).
Web 2.0 The popular term for advanced Internet technology and
applications, including blogs, wikis, RSS, and social bookmarking.
One of the most significant differences between Web 2.0 and the
traditional World Wide Web is greater collaboration among Internet
users and other users, content providers, and enterprises.
Web analytics The application of business analytics activities to
Web-based processes, including e-commerce.
Web content mining The extraction of useful information from
Web pages.
Web crawlers An application used to read through the content of
a Web site automatically.
Web mining The discovery and analysis of interesting and useful
information from the Web, about the Web, and usually through
Web-based tools.
Web services An architecture that enables assembly of distributed
applications from software services and ties them together.
Web structure mining The development of useful information
from the links included in Web documents. Web usage mining The extraction of useful information from the
data being generated through Web page visits, transactions, and so
on.
Weka A popular, free-of-charge, open-source suite of
machinelearning software written in Java, developed at the
University of Waikato.
what-if analysis It is an experimental process that helps determine
what will happen to the solution/output if an input variable, an
assumption, or a parameter value is changed. wiki A piece of server software available in a Web site that allows
users to freely create and edit Web page content using any Web
browser.
wikilog A Web log (blog) that allows people to participate as peers;
anyone can add, delete, or change content.
word2vec A two-layer neural network that gets a large text corpus
as the input and converts each word in the corpus to a numeric
vector of any given size, typically ranging from 100 to 1000.
WordNet A popular general-purpose lexicon created at Princeton
University.
Note: ‘A’, ‘f’ and ‘t’ refer to application cases, figures and tables respectively
A Activation function, 325 Actuator system, 595f, 596 AdaBoost algorithm, 298–299 Adidas, robotics, 586 Advanced analytics, 453 Affinity analysis, 232 Agrobot, 594 AI. See Artificial Intelligence (AI) Akita chatbot, 663 Alexa (Amazon), 672–673
defined, 673 Echo, 21, 24, 673f, 674
enterprise, 674 skills, 674 smart
home system, 682 voice interface
and speakers, 674 AlexNet, CNN, 353, 353f, 355 Algorithms
AI, 96, 601, 678 Apriori, 234–235
association rules, 234
backpropagation, 336–337, 361
boosting, 298–299 clustering, 231
data mining, 245, 274 decision
tree, 227–228, 492 genetic, 226 k-
means, 232 kNN, 274, 276 linear/nonlinear, 271
MART, 300 nearest
neighbor, 275
predictive, 122, 126 SGD, 336
Alibaba Group (Alibaba.com), 643, 761–762, 762A–763A
Alternative Data, 49, 517A–518A Amazon (Amazon.com), 33, 741
AI, 95, 107 Alexa. See Alexa (Amazon) apps,
21 for business, 62A cloud computing, 557
Elastic Beanstalk, 563 human touch, 677A
IaaS, 559–560 recommendation systems,
657–658 Smart Assistant Shopping Bots,
679–680 Ambari (project), 526 Ambient computing (intelligence), 758–759 Analysis ToolPak tool, 149 Analytical decision modeling with decision
tables/trees, 490–492 goals/goal seeking, 486–
487, 489 mathematical models, 469–471
mathematical programming optimization, 477–485 model-based, 462–
463 sensitivity analysis, 487–
488 with spreadsheets, 473–
476 what-if analysis, 488–489 Analytics, 4, 8, 22
accelerators, 64 advanced, 453 and
AI, 59–63 application, 32A, 33A,
34A, 35A, 328A–330A, 399A–401A, 419A–422A Big
Data, 24, 37–38 business. See Business
analytics (BA), sta- tistical modeling for
cognitive, 374 data science, 36–37
decision/normative, 35 descriptive, 32,
140 ecosystem, 63–65, 64f future of,
759f in healthcare, 43–46 image, 49–50
impact on, 758 in-memory. See In-
memory analytics location-based. See
Location-based analytics
organizational design, 743 overview, 30–32
predictive, 4–5, 33, 126–127 prescriptive, 4–
5, 34–35, 461–462 ready, 122 in retail value
chain, 46–47, 47f, 48t, 49 smarter
commerce, 390f solution providers, 550–
551 sports, 38–43, 156 stream. See Stream
analytics and text mining, 392–395, 393f
traffic congestions, 346A–348A types, 31f
user organizations, 64 video, 91 visual. See
Visual analytics web technologies, 441–442 Analytics as a Service (AaaS), 564 Android, 91, 581 ANN. See Artificial neural network (ANN) Apache Spark™
architecture of, 538–539, 538f in-
memory analytics and, 537–543 on Quick
Start (QS), 539–543 Apple CarPlay, 735 Siri, 366, 372, 675, 760
Application programming interface (API), 369–370
Applications of AI in accounting, 99–101, 100A in financial
services, 101–104, 104A in HRM, 105–
106, 106A in marketing, advertising, and
CRM, 107–110 in
POM, 110–112 Apriori algorithm, 234–235, 235f AR. See Augmented reality (AR) Architecture file, 369 Area under the ROC curve, 223, 224f Arithmetic mean, 140–141 Artificial brain, 82 Artificial Intelligence (AI), 4, 24, 315 analytics
and, 59–63 applications. See Applications of AI
benefits, 52, 79–81 and blockchain, 62–63
brainstorming, 628 business analytics and, 738–
739 capabilities, 55, 81, 86 characteristics, 77
CRM, 642 dangers of, 753–755 decision-making
process, 95–99 definitions, 76–77 development,
601 drivers, 79 Dystopia, 753 elements, 77
examples, 78, 80 functionalities and applications,
77f, 78 future prediction, 757f goals, 78 human
intelligence, 84–85, 85t impacts, 56–58, 58A–
59A, 758 innovation and, 9 and IoT, 61 lab
scientists, 602 landscape of, 52–55, 53f legal
implications of robots and, 603–605 limitations, 81 measuring, 85–86
narrow vs general, 54–55 overview, 52
research in China, 761 Schrage’s
models, 99 security lines at airports,
54A Spark collaboration platform, 638
swarm. See Swarm AI team
collaboration, 637–638 technologies.
See Technologies of AI Turing Test, 85, 85f Utopia, 753–754
vignette, 74–76 vs cognitive computing,
372–374, 373f in WildTrack, 333A
Artificial neural network (ANN), 255, 315
architectures, 259–261 backpropagation,
336–338, 337f black box of, 340–341
development tools, 339 elements of, 330
Hopfield network, 260–261, 260f
Kohonen’s SOM, 259–260, 260f
overfitting, 338, 338f software, 339
supervised learning, 335, 336f transfer
function, 331–332, 332f vignette, 252–255
vs biological neural networks, 256–258 vs
SVM, 273 See also Neural networks Artificial neuron, 256–257, 257f
multiple-input, 327f single-input,
325f Assisted intelligence, 55, 81
785 Association rule learning method, 207, 414 Association rule mining method, 232–234 Asynchronous communication, 617 Attributes, 226 Augmented intelligence, 5, 55–56, 82 Augmented reality (AR), 95 Authoritative pages, 432 Automated data collection systems, 121 Automated decision-making, 97–98 Automatic sensitivity analysis, 488 Automatic summarization, 402 Automation business
process, 653 defined,
584 See also Robotics Autonomous AI, 55, 81 Autonomous robots, 91 Autonomous vehicles computer
centers in cars, 598 deep learning,
598 defined, 704 development,
598–599, 714–715 flying cars, 717
implementation issues in, 717
maps, 598 mobile phones, 598
self-driving cars, 599–600, 715f
Waymo and, 715A wireless internet, 598
Autonomy, 584 Average pooling function, 352 Avro system, 526 Axons (neuron), 256
B Back-office business analytics, 39 Backpropagation (back-error propagation),
336–338, 337f Bagging ensemble method, 296–298, 297f Baidu, Inc., 762 Balanced scorecard–type reports, 165 Banking services AI in, 101–
103 association rule mining,
233 chatbots, 668 data
mining, 208–209 Bayes/Bayesian classifiers, 226, 279–281 Bayesian networks (BN), 287–293
construction, 288–293 work
process, 287–288
Index 221
Bayes theorem, 278–279 BI. See Business intelligence (BI) systems Bias (predictive analytics), 295 Bias-variance trade-off, 295 Big Data analytics, 24, 37–38
and AI, 60–61 application, 517A–518A, 522A,
531A–532A, 538A, 547A, 551A–552A
business problems addressed by, 521–522
conceptual architecture for, 517f critical
success factors, 520f and DW, 532–537
definition of, 513–517 fundamentals of,
519–522 in Gulf Air, 566 Hadoop, 524–527, 533–534
hurdles, 510–511 and IoT,
63 MapReduce, 523–524,
524f NoSQL, 528–529 and stream analytics, 543–549
technologies, 523–532 value
proposition, 516–517 variability,
516 variety, 515 velocity, 515–516
vendors and platforms, 549–551
veracity, 516 vignette, 510–513
volume, 514–515 Biological neural networks, 256, 257f vs
artificial neural networks, 256–258 Black-box syndrome, 224, 340–341 BlueCava technology, 734 BN. See
Bayesian networks (BN)
boardofinnovation.com, 634
Bolivian chatbot (BO.T), 663 Boosting ensemble method, 298–299, 298f Bootstrapping process, 223 Bot. See Chatbots Box-and-whiskers plot/box plot, 143–144,
144f, 149f Brainstorming process AI
supports, 628 computer-
supported, 627 defined,
627 for generating ideas,
627 GSS, 628–629 online
services, 627–628 Brand management, sentiment analysis, 423 Break-even point, goal seeking, 489 Bridge, 450 Brokers and traders, data mining, 209 Browser-native technologies, 169 Business analytics (BA), Cloud computing
AaaS, 564 cloud deployment models, 563
cloud infrastructure application, 565 cloud
platform providers, 563–564 DaaS, 558–559 IaaS, 559–560 PaaS, 559
representative analytics, 564–565
SaaS, 559 Snowflake, 566–567
technologies, 560 vignette, 118–121 Business analytics (BA), statistical modeling for,
139 application, 150A–151A arithmetic mean,
140–141 box-and-whiskers plot/box plot,
143–144, 144f, 149f charts and graphs, 171–175,
174f, 175f descriptive statistics, 139
kurtosis, 146 mean absolute deviation,
143 measures of centrality, 140 measures
of dispersion, 142 median, 141 mode,
141–142 quartiles, 143 range, 142 shape
of distribution, 145–146, 145f skewness,
145–146 standard deviation, 143 variance,
142–143 Business intelligence (BI) systems, 16,
22–23, 139 architecture,
25, 26f definition, 25 DW
and, 27 evolution of, 26f history, 25
multimedia exercise, 28–29 origin
and drivers of, 26–27 planning
and alignment, 29–30 providers,
551–553 Business performance management (BPM),
25, 165 Business process automation, 653 Business reporting, 164
balanced scorecard, 165 dashboard, 165
FEMA, 165A–166A functions, 163–
164 in managerial decision making,
164f metric management reports, 165 Business Scenario Investigations (BSI) videos,
28
C Caffe/Caffe2 (learning framework), 368–369 Calculated risk, 472 Candidate generation method, 235 Capacities, LP model, 479 Care-E Robot, 593–594 Case-based reasoning, 226 Categorical data, 125, 206 Central Electric Cooperative (CEC), 591 Centrality, 450 Central processing unit (CPU), 343, 369, 596 Certainty, decision making, 471 Chatbots (Chat robot), 21, 94, 106, 661
application, 664, 669A benefits, 663 chatting
with, 662f components and use, 662
constructing, 682 defined, 660 disadvantages
and limitations, 681 drivers of, 661
enterprise. See Enterprise chatbots evolution,
660–661 managing mutual funds using AI,
678 person-machine interaction process, 662, 662f
platform providers, 670–671 as
professional advisors, 676–680 quality of,
681 representative, 663–664 revolution,
648 smart assistant shopping bots, 679–
680 technology issues, 680 vignette, 649–
650 virtual assistants under attack, 681 China, AI research, 761 U.S.
and, 764 Choice phase of decision-making, 10, 13 CI. See Collective intelligence (CI) Citrix Workspace Cloud, 621 Classification
in data mining, 205–207, 220–222 matrix,
221 Naïve Bayes method. See Naïve Bayes method nonlinear, 270
problem, 12, 221
techniques, 226 text
mining, 413 Click
map, 443 Click paths, 443 Clickstream analysis, 441 Cliques and social circles, 451
Cloaking technique, 438 Cloud-based technologies, 7 Cloud computing model
application, 561A–562A and business
analytics, 557–567 cloud deployment,
563 cloud infrastructure application,
565 cloud platform providers, 563–564
defined, 557 support system, 558f
technologies, 560 Cloudera (cloudera.com), 550 Clusters/Clustering, 228, 394
cluster analysis, 228, 230–232
coefficient, 451 data mining, 207,
228, 230–232 k-means algorithm,
232, 232f optimal number, 231
query-specific, 414
scatter/gather, 414 text mining,
413–414 CNN. See Convolutional neural networks
(CNN) Cobots. See Collaborative robots (Cobots) Cognitive analytics, 374 Cognitive computing system, 94, 315,
370–381, 761 attributes, 372
cognitive search, 374–375, 375f
framework, 371f vs AI, 372–374,
373f work process, 371–372 Cognitive limits, 8 Cognitive search method, 374–375, 375f Cohesion, 451 Collaboration process, 7–8 AI support,
637–638 business value from, 632
groupware for, 619 human-machine in
cognitive jobs, 641 social, 622
software, 622–623
tools, 623 vignette,
611–613 Collaborative filtering, 658 Collaborative intelligence, 632
See also Collective intelligence (CI) Collaborative networks and hubs, 622 Collaborative robots (Cobots), 587,
597, 642 Collaborative workflow, 621 Collaborative workspace, 621 Collective intelligence (CI)
application, 630A–631A benefits, 629
business value, 632 and collaborative
intelligence, 629–632 computerized
support, 629 defined, 629 types, 629 work
and life, 631–632 Computer-based information system (CBIS),
16, 334, 729, 740, 744 Computer ethics, 737 Computer hardware and software. See
Hardware; Software Computerized decision support framework,
9–22 BI/analytics/data science, 22, 22f
semistructured problems, 14, 16
structured decisions, 14–16 types of
control, 14–15, 15f unstructured
decisions, 15–16 Computer operations, 678 Computer-supported brainstorming, 627 Computer vision, 90
222 Index
Compute Unified Device Architecture (CUDA), 368
Concept linking, 394 Conditional probability, 279, 289f Condition-based maintenance, 209 Confidence gap, 498 Confidence metric, 234 Confusion matrix, 221, 221f Connectionist models, 256 Connection weights, 331 Constant Error Carousel (CEC), 362 Constitutional Law, robots, 605 Constraints, 477, 479 Consultation environment, 653, 654f Consumer Electronic Show (CES), 705–706 Content-based filtering, 658 Content groupings, 444 Contingency table, 221 Continuous data, 126 Continuous distributions, 497, 497t Controller/CPU, robots, 596 Conversion statistics, 444–445 Convolutional neural networks (CNN),
349–360 Caffe/Caffe2, 368–369 for
extracting features, 351f face recognition
technology, 356A–357A function, 349–351
image processing, 353–355 input matrix,
350, 350f pooling layer, 349, 352–353 for
relation extraction, 359f text processing,
357–360 unit, 349f Convolution kernel, 350, 350f Convolution layer, 349 Corpus, 394 Correlation vs regression, 151 Coworking space, 621 Credibility assessment. See Deception detection Critical event processing, 545 Cross-Industry Standard Process for Data
Mining (CRISP-DM), 211, 211f business understanding, 212 data
preparation, 213–214 data understanding,
212–213 deployment, 217 model
building, 214 standardized
methodologies, 217–218, 219f testing and evaluation,
217 Crowdsourcing process, 295
application, 636A for decision
support, 633–636 defined, 633
essentials of, 633 examples, 633
for marketing, 636 for problem-
solving, 634–635 process, 634,
635f role in decision making,
635 types of, 633–634 Customer relationship management (CRM),
4, 28, 39 AI in, 108 application,
109A customer experiences and,
108 data mining, 208 Customer–robot interactions, 601 Cybersecurity,
547–548
D Dashboards, 7, 183f application,
184A, 185A–186A best
practices, 187 characteristics,
186–187 design, 184–185, 188
guided analytics, 188 information
level, 188 KPI, 187 metrics, 187
rank alerts, 188 -type reports,
165 user comments, 188
validation methods, 187 Data analysis, technologies for, 7–9 Data as a Service (DaaS), 558–559 Database management system (DBMS), 18 Data collection, issues in, 11 Data/datum application, 127A–129A,
133A–138A labelers, 601–602 management, 8 nature of,
121–124 preprocessing, 129–132, 130f,
132t, 213 processing, 653 quality, 121
readiness level of, 123–124 reduction, 131
science, 22, 36–37 scientists, 36, 525
scrubbing, 129 security, 123 taxonomy of,
125–127, 125f See also specific data Data-in-motion analytics. See Stream analytics Data management subsystem, 18–19 Data mart (DM), 27 Data mining, 7, 24, 33 accuracy metrics,
221–222, 222t application, 199A–200A,
203A–204A, 208–211, 210A–211A associations, 207
in cancer research, 214–216 categories,
205 characteristics and objectives, 201–
202 classification, 205–207, 220–221
clustering, 207, 228, 230–232 concepts,
198–199 CRISP-DM. See Cross-Industry Standard Process for Data Mining (CRISP-DM) defined, 201, 392 in
healthcare industry, 229A–230A
methods, 220–235 of multiple
disciplines, 202f myths and blunders,
242–246, 244t patterns, 202–203, 205 Data mining (Continued) prediction used
in, 205, 239A–242A, 243A–244A
software tools, 236–238, 239f
taxonomy, 206f in text
analytics, 393f vignette, 195–
198 vs statistics, 208 vs text
mining, 392–394 Data-oriented DSS, 29 Data sources, 24 for business
applications, 213 reliability, 123
Data stream mining, 546 Data visualization, 166, 208
application, 169A–171A in BI and
analytics, 176–177, 176f history,
167–169, 167f, 168f Data warehouse/warehousing (DW), 7, 23
BI and, 27 Big Data and, 532–537
business value, 534 coexistence of
Hadoop and, 536–537 concept, 28f
interactive BI tools, 534–535 managing,
8 performance, 534 real-time, 24 right-
time, 24 Da Vinci Surgical System, robotics, 592 DBN. See Deep belief network (DBN) Deception detection, 404, 404A–406A, 405f,
405t Decision analysis with decision
tables, 490–492 with decision
trees, 492 defined, 490 Decision-making process, 5–6, 10f AI support
for, 95–99 automated, 97–98 under certainty,
471 data and analysis, 7 example, 11A–12A
external/internal environments, 6–7
forecasting, 465 group. See Group decision-
making process model-based. See Model-based
decision-making organizational. See Organizational
decision-making phases of, 9–10 under risk (risk
analysis), 472 role of
crowdsourcing, 635 under
uncertainty, 472 vignette, 3–4
zones, 471f Decision/normative analytics, 35 Decision rooms, 625 Decision support system (DSS) application,
16–18, 20A categories of models, 467t
characteristics and capabilities, 16–18, 17f
components, 18, 19f definition and concept,
14 Keen and Scott-Morton’s definition, 22
knowledge-based modeling, 467–468
mathematical models for, 469–470
mathematical programming optimization, 477–485
resources and links, 66–67 with
spreadsheets. See Spreadsheets
technologies for, 7–9 Decision tables, 490–492, 491t Decision trees, 205–206, 226–228, 492
bagging-type ensembles for, 297f boosting-
type ensembles for, 298f Decision variables, 469, 469f, 470t Deep belief network (DBN), 344 Deep Blue (chess program), 375 Deep feedforward networks, 343–344, 344f Deep learning (DL) technology, 88–89
AI-based learning methods, 321f
application, 323A–325A computer
frameworks. See Libraries (software) overview,
320–322 vignette, 316–
319 Deep neural networks, 343–345 classification-
type, 345f deep feedforward networks, 343–
344, 344f hidden layers vs neurons, 345
random weights in MLP, 344–345 DeepQA architecture, 376–377, 377f Dell’s Idea Storm (ideastorm.com), 633 Delta, 336 Dendrites, 256–257 Density, 450 Dependent variables, 469 Deployment of intelligent systems,
737–740 connectivity and integration,
739 decision-making, 745 impact on
managers, 744–745 implementation
issues, 738–739 management and
implementation, 738 security protection,
739 Descriptive analytics, 32, 36, 453 Descriptive statistics, 140, 146 Design phase of decision-making, 9–10,
12–13 Development environment, 653, 654f Dimensional reduction process, 131 Direct searches, 443 Discrete data, 125 Discrete distributions, 497, 497t
Index 223
Discrete event simulation, 498, 498A–499A Dispersion method, 142 Distance metric, 275–276, 276f Distant supervision method, 359 DL. See Deep learning (DL) technology DM. See Data mart (DM) Document indexer, 434f, 435 Document matcher/ranker, 434f, 436 Downloads, 443 Driverless cars. See Autonomous vehicles Dropbox.com, 619 Dynamic models, 467 Dynamic networks, 361 Dystopia (pessimistic approach), 753
E Echo, 674 EDW. See Enterprise data warehouses (EDW) EEE (exposure, experience, and exploration)
approach, 3 Effector, 595f, 596 EIS. See Executive information system (EIS)
Ensemble modeling, 293–303 application, 304–
306 bagging, 296–298, 297f boosting, 298–299,
298f complexity, 302 information fusion, 300–
301, 302f pros and cons of, 303t RF model, 299
SGB, 299–300 stacking, 300, 301f taxonomy for,
296f transparency, 303 types, 295–296 Enterprise chatbots application, 666A, 667A
examples of, 665 Facebook’s chatbots,
666 financial services, 668 improving
customer experience, 665 industry-specific
bots, 671 inside enterprises, 669–670
interest of, 664 knowledge for, 671
messaging services, 66A, 666 personal
assistants in, 671 platforms, 669 service
industries, 668–669 See also Chatbots Enterprise data warehouses (EDW), 27 Enterprise resource planning (ERP) systems, 23 Entertainment industry, data mining, 210 Entropy, 228 Environmental scanning and analysis, 465 Equivariance, 351 ES. See Expert system (ES) ESRI (esri.com), 569 Euclidian distance, 231 Evidence, Bayes theorem, 279 Executive information system (EIS), 23, 25 Expert, 651 Expertise, 651–652 Expert system (ES), 23 application, 655A,
656A–657A architecture of, 654f areas
for applications, 653 benefits of, 652
characteristics and benefits of, 652
classical type of, 655–656 components
of, 653–654 concepts, 650–652 new
generation of, 656 and recommenders,
650–658 structure and process of, 653 VisiRule, 656A–657A
Exsys Corvid ((Exsys.com), 654, 655A
ExtendSim (extendsim.com), 501 eZ talks
meetings, 627
F Fabio (robot), 591
Facebook, 320, 622, 760–761 Caffe2,
369 chatbots, 666 ethical issues, 735
proponent, 754 Rapleaf, 734 weakly
supervised training, 355 Facial
recognition technology, 91 Federal
Emergency Management Agency (FEMA), 165A–166A
Feedforward-multilayered perceptron (MLP), 330, 335 random weights, 344–345
-type deep networks, 343–344, 344f Filter, 350 Financial markets, sentiment analysis, 423 Financial robo advisors application, 677A
evolution, 676 managing mutual funds
using AI, 678 medical and health, 678–
679 professional, 678 Financial services, big data, 548 Florence, 679 1-800-Flowers.com, 742, 742A–743A Flume
framework, 526 Forecasting (predictive analytics), 465,
466A–467A Foreign language reading, 402 Foreign language writing, 402 Forget/feedback gate, 362 Fourth industrial revolution, 584 Friendly Artificial Intelligence (AI), 754 Frontline Systems Inc. (solver.com), 473 Front-
office business analytics, 39
G Gartner, Inc., 751 business intelligence
platform, 177 social analytics, 446
technology trends for 2018 and 2019, 756–757
GDSS. See Group decision support system (GDSS)
General (strong) AI, 55 Generative models, 344 Genetic
algorithms, 226 Geographic information system (GIS), 173,
568 Geospatial analytics applications for
consumers, 573–574 concept, 567–571
real-time location intelligence, 572–573 See
also Location-based analytics Geospatial data, 567 Gini index, 227–228 Goal seeking, 475, 489, 490f Google, 37, 320, 339 Android Auto, 735
cloud-based speech-to-text service, 366
Google App engine, 564 Google Assistant, 675, 760 Google Cloud Platform, 621 Google Drive (drive.google.com), 619 Google Home, 21, 24, 372 Google Lens, 354–355, 355f Google Maps, 168 Google Nest, 705 GoogLeNet, 354–355, 354f Google Now, 366, 374 NLP, 760
TPU, 369 virtual assistants, 733 word2vec
project, 357–358, 358t Google’s Neural Machine Translation
(GNMT) platform, 366, 366f
GoToMeeting.com, 620 Government and
defense, data mining, 209 Government intelligence, sentiment analysis,
423–424 Graphics processing unit (GPU), 343, 368 Green Card, 663 Group communication and collaboration
collaborative hubs, 622 for decision
support, 618–619 groupware for, 619
networks and hubs, 622 products and
features, 619 social collaboration, 622–623
surface hub for business, 622 synchronous
vs asynchronous products, 619–620 virtual meeting systems,
620–622 Group decision-making process
AI and swarm, 637–640 defined, 613
direct computerized support, 623–627
other support, 626–627 process, 614f
supporting entire process, 625–627 Group decision support system (GDSS)
capabilities, 624 characteristics, 625
decision rooms, 625 defined, 624
internet-based groupware, 625 Group Support System (GSS) defined,
617, 628 group work improvement,
628–629 Groupthink, 615 Groupware
defined, 619 for group collaboration, 619
products and features, 620t ThinkTank use
(thinktank.net/case-study), 626
tools, 626 Group work
benefits and limitations, 615–616
characteristics, 613 collaboration for
decision support, 618 computerized
support, 618–619 decision-making
process, 614–615 defined, 613 GSS, 617
improvement, 628–629 supporting, 616–
619 time/place framework, 617–618,
618f types, 614 GSS. See Group Support Systems (GSS)
H Hadoop
defined, 524 and DW, 535t, 536f
pros and cons, 527 technical
components, 525–526 use cases
for, 533–534 working principle,
525 Hadoop Distributed File System (HDFS),
37, 525 Hardware data mining used
in, 209 IoT technology,
692 requirements check,
539 Hazie, chatbot, 663 HBase database, 526 HCatalog storage management, 526 Healthcare application, data mining,
209–210 Health Sciences, Big Data, 548 Health Tap, 679 Hendrick Motorsports (HMS), 611–613 Heterogeneous ensemble method,
224 Index
300–301 Hidden layer, 330, 337
vs neurons, 345 High-performance computing, 180 Histogram, 172–173 HITS. See Hyperlink-induced topic search
(HITS) Hive framework, 526 Holdout set, 222 Homeland security data
mining, 210 ES, 653
Homogeneous-type ensembles, 296 Homophily, 450 Homoscedasticity, 155 Hopfield network, 260–261, 260f Hortonworks (hortonworks.com), 550 Hubs, 432 Humana Inc., 43–46 Human-computer interaction (HCI), 372 Human-machine collaboration
in cognitive jobs, 641 and
robots, 640–642 Human-mediated machine-learning approach,
320 Human resource management (HRM), AI in,
105–106, 106A Human touch, 676 Humanyze Company, 743 Hybrid cloud, 563 Hyperlink-induced topic search (HITS), 432
I IBM, 373 on analytics, 741 cloud,
564–565 cognitive computing, 315
Deep Blue (chess program), 375
robotics, 761 Watson. See Watson,
IBM Idea generation, 624 Image analytics, 49–50
application, 50A–51A satellite
data, 49–50 ImageNet data set, 353 ImageNet Large Scale Visual Recognition
Challenge (ILSVRC), 354 Image processing technology, 90, 353–355 IMindQ, 627 Imperfect input, 399 Implementation defined, 13 phase of
decision-making, 9, 13–14 Improved search precision, 414 Improved search recall, 413 Inception, 354, 354f Industrial restructuring, 746 Industrial Revolution, 740, 746 Inference engine, 654 Inferential statistics, 140 Influence diagram, 468 Information, 163 to decision
makers, 163 extraction,
393f, 394 fusion, 296, 300–
301, 302f gain, 228 visualization, 166, 169, 177–178 (See also
Data visualization) warfare,
210 Information systems (IS), 8 Infrastructure as a Service (IaaS), 559–560
Infrastructure Services, Big Data, 550 In-memory analytics Apache Spark™
architecture, 538–539 defined, 520
Quick Start (QS), 538–543 InnoCentive Corp. (innocentive.com), 633,
636A Input gate, 362 Input/output (technology) coefficients, 479 Input/output of network, 331 INRIX corporation (inrix.com), 74–76 Instagram, 355 Institute for Operations Research and
Management Science (INFORMS), 31, 64 Insurance industry AI in, 103–
104 association rule mining,
233 data mining, 209 Integrated intelligent platforms, 5 Intelligence, 83 assisted, 55, 81
augmented/augmentation, 5, 55–56, 82
and automated decision support, 98
capabilities, 83–84 CI. See Collective
intelligence (CI) collaborative, 632
content, 83 government, 423–424 human
intelligence vs AI, 84–85, 85t swarm, 639
types, 83 Intelligence phase of decision-making, 9
classification of problems, 12 data
collection, 11 decomposition of
problems, 12 identification of problems,
10–11 problem ownership, 12 Intelligent agent (IA), 87 Intelligent bots, 661 Intelligent systems, 57–58
adoption, 740 analytics and AI,
60 in business, 739–740 ethical
issues, 735–737 future of, 760–
762, 764–765 impacts of, 730,
730f impacts on organizations.
See Organizations, intelligent systems
implementation process, 729–730, 729f on
jobs and work, 747–752, 748A–749A, 750t legal issues, 731–732 privacy issues.
See Privacy in intelligent technology
private data, 735 successful deployment. See
Deployment of intelligent systems
support from IBM and Microsoft, 63
technology trends, 756–759 vignette,
727–729 Intermediate result variables, 470 Internet, 380, 733 data visualization, 168
search engine. See Search engines Internet of Things (IoT), 4 in action, 701 AI
and, 61 applications, 701–702 benefits of, 694
Big Data and, 63 building blocks of, 693f
changing everything, 691 characteristics, 690–
691 and decision support, 696–697 defined,
689 drive marketing, 702 drivers of, 695
ecosystem, 691, 692f essentials, 689–694
French national railway system’s use, 701
hardware, 692 and managerial considerations,
717–721 opportunities, 695 platforms, 694
privacy in, 733 process of, 696f RFID and
smart sensors in, 700–701 SAS supports, 714f
sensors and, 697–701, 697A, 698, 698A–699A
strategy cycle, 720f structure of, 691
technology infrastructure, 692–693, 693f
vignette, 688–689 work process, 696 World’s
largest, 695 Internet Search Engine. See Search engines Interpersonal communication skills, 6
Interval data, 126, 206 ir.netflix.com,
658A–660A
J Jackknifing, 223 Java, 36 Job Tracker, 525 Joint distribution, 288 Jurik Research Software, Inc.
(jurikres.com), 473
K KDD (knowledge discovery in databases)
process, 218, 219f Keras (learning framework), 370 Kernel trick method, 271 Key performance indicator (KPI)
business reports, 165 dashboards,
182, 187 k-fold cross-validation, 223, 223f Kip chatbot,
663 k-means clustering algorithm, 232, 232f
k-nearest neighbor (kNN) algorithm, 274,
274f, 277A–278A KNIME tool (data mining tool), 236 Knowledge
acquisition, 93, 94f, 653
base, 653 of context, 360
data, 121, 122f and ES, 93
patterns, 217 refining subsystem, 654 representation, 653
Knowledge-based management subsystem, 21 Knowledge-based modeling, 467–468 Knowledge discovery in databases (KDD)
process, 218, 219f Knowledge management systems (KMS), 8 Kohonen’s self-organizing feature map
(SOM), 259–260, 260f KONE Elevators and Escalators Company,
3–5
L Landing page profiles, 444 Law enforcement
agencies, 198 AI, 605
and Big Data, 547–548
data mining, 210 Lazy Evaluation approach, 539 Leaf node, 227 Learning chatbots, 660 Learning process in ANN, 335–336
backpropagation, 336–338, 337f Leave-one-out method, 223, 225 Legal issues in intelligent systems, 731–732 Libraries (software), 368
Caffe, 368–369 Keras, 370 TensorFlow, 369 Theano, 369–370
Index 225
Torch, 368 Lift metric, 234 Likelihood, Bayes theorem, 279 Lindo Systems, Inc. (lindo.com), 484 Linear programming (LP)
defined, 477 modeling,
480–484 Linear regression model, 152–153, 152f
assumptions in, 154–155 Link analysis, 207 LinkedIn, 36, 622, 743 Link function, 155 Linux (linux.org), 633 Localization, 598 Location-based analytics
applications for consumers, 573–574
classification of, 568f geospatial
analytics, 567–571 location decisions,
570A multimedia exercise in analytics, 571–572 real-time location intelligence,
572–573 Logistic regression, 155–156, 156f Logistics, data mining, 209 Long short-term memory (LSTM) network,
343, 360–363, 365–367
applications, 365–367 architecture,
363f Caffe, 369
Long-term memory, 362 LP. See Linear programming (LP) Lua programming language, 368 Lumina Decision Systems (lumina.com), 501
M MA. See Medicare Advantage (MA) MAARS (Modular Advanced Armed Robotic
System), 589 Machine-learning algorithms, 126, 224, 320,
368, 427 Machine-learning techniques, 88–89, 225,
263, 273, 276, 320–321, 322f, 335 Machine translation of languages, 92,
366–367, 402 Machine vision, 90 Mahindra & Mahindra Ltd., 589 Mahout, 526 Male comorbidity networks, 555f Management control, 14–15 Management information system (MIS), 22 Manhattan distance, 231 Manufacturing data
mining, 209 ES, 653
Mapping and localization, 598 MapR (mapr.com), 550 MapReduce technique defined,
523 graphical depiction of,
524f use, 523–524 Market-basket analysis, 49, 207, 232–233 Marketing, ES, 653 Marsbees, 643 MART. See Multiple additive regression trees
(MART) algorithm Master data management, 122 Mathematical programming tools
application, 478A components
of, 469–470 defined, 477
implementation, 484–485 LP
model, 479–484 optimization,
477–485 structure of, 470 Maximum-margin classifier, 263 Max pooling function, 352, 352f McKinsey & Company management
consultants, 5 MEDi (Machine and Engineering Designing
Intelligence), 593 Medicare Advantage (MA), 46 Medicine, data mining, 210, 233 Message feature mining, 404 Meta learner, 300 Metric management reports, 165 Microsoft
Azure, 563–564 Cortana, 63, 366, 761 Enterprise Consortium, 66, 237 Excel, 146, 147f, 148f, 149, 149f Maluuba, 761 Skype Translator service, 367, 367f
SQL Server, 236–237 surface hub
for business, 622 TrueText, 367 Workspace, 621
Mindomo tool, 627 MineMyText.com, 565 Mobile user privacy, 733 Model-based decision-making
application, 463A–464A model categories,
467–468 prescriptive analytics, 465 of
problem and environmental analysis, 465–467
vignette, 461–462 Model base management system
(MBMS), 19 Model ensembles. See Ensemble modeling Modeling and analysis certainty, uncertainty, and
risk, 471–472 decision analysis, 490–492 goals,
486–487, 492 goal seeking analysis, 489
mathematical models for decision support, 469–470
mathematical programming optimization, 477–485 (See also Linear programming
(LP)) sensitivity analysis, 487–488
with spreadsheets, 473–476 (See also
under Spreadsheets) what-if analysis, 488–489 (See also individual
headings) Model management subsystem, 19–20 Monte Carlo simulation, 497–498 Multidimensional analysis (modeling), 468 Multi-layer perceptron, 259 Multilevel text analysis, 407f Multiple additive regression trees (MART)
algorithm, 300 Multiple goals, 486–487, 492t Multiple-input neuron, 327f Multiple regression analysis, 152 Multiplexity, 450 Mural tool, 627 Mutuality/reciprocity, 450 MYCIN expert system, 379
N Naïve Bayes method, 278–282
application, 282A–286A Bayes
classifier, 279–281 Bayes
theorem, 278–279 testing
phase, 281–282 Name Node, 525 Narrow AI, 54–55 Natural language processing (NLP), 92, 358,
760–762 concept, 397–402 defined,
398 as text analytics, 393f
Nearest neighbor method, 274–277
cross-validation, 275–277 distance
metric, 275–276, 276f kNN, 274,
274f, 277A–278A parameter
selection, 275 Nest.com, 705 Netflix recommender, 658A–660A Net input function, 325 Network, 256
architectures, 330
closure, 450
collaboration, 630
gradients, 336 structure,
330 virtualization, 560 Neural computing, 255, 257 Neural networks, 205–206, 226, 330
architectures, 259–261, 260f with
backpropagation, 336–337, 337f
biological, 256–258, 257f concepts of,
255–258 convolutional. See Convolutional
neural networks (CNN)
deep. See Deep neural networks development
process, 334–339, 334f implementations, 339 with layers and neurons, 327f, 331f in
mining industry, 258A–259A shallow, 322,
325–333 transfer functions in, 326f See also
Artificial neural network (ANN) Neurodes,
257 Neuromorphic systems, 256 Neuron, 256, 330
artificial. See Artificial neuron
backpropagation of error, 337f
hidden layers vs, 345 summation
function for, 331, 332f New Member Predictive Model (NMPM), 46 Ninesigma.com, 633 Nominal data, 125–126, 213 Nonlinear classification, 270 Normal distribution, 145 NoSQL database, 528–529, 529A–530A N-P (negative/positive) polarity classification,
424–425, 427f Nucleus, 257 Numeric data, 126, 213
O Objective function, 479 Objective function coefficients, 479 Offline campaigns, 443 Online analytical processing (OLAP) system,
7, 19, 28, 139 Online campaigns, 444 Online transaction processing (OLTP) system,
27, 163–164 Online workspaces, 619
226 Index
Oozie system, 526 Open Artificial Intelligence (AI), 754 Openshift, 564 Operational control, 15 Operational data store (ODS), 27 Operations research (OR), 23 Optical character recognition, 402 Optimal solution, 479 Optimistic approach (Utopia), 753–754 Optimization deep MLPs, 344 mathematical
programming, 477, 479–485 quadratic
modeling, 263 SEO, 436–439 Oracle Crystal Ball (oracle.com), 501 Ordinal data, 125–126, 213 Ordinary least squares (OLS) method, 153 Organizational decision-making data
and analysis, 7 external/internal
environments, 6–7 process, 5–6 Organizational knowledge base, 21 Organizations, intelligent systems, 740–746
business transformation, 741 competitive
advantage, 741–742 industrial restructuring,
746 new units and management, 741
organizational design, 743 Output gate, 362 Overfitting in ANN, 338, 338f Overstock.com,
522A
P PaaS. See Platform as a Service (PaaS) Page views, 442 Palisade Corp. (palisade.com), 501 Parallel distributed processing models, 256
Parameters, 469 Parameter sharing, 350 Part-of-speech tagging, 395, 398, 407 Patent, 603–604 Pattern recognition, 255 People Analytics, 743 Pepper robot, 590–592 Perceptron, 256 Performance function, 335 Perpetual analytics
defined, 544 vs stream
analytics, 544–545 Pessimistic approach (Dystopia), 753 Pig query language, 526 Platform as a Service (PaaS), 557, 559 Polarity identification, 426 Polarization, 747 Polyseme, 394 Pooling layer, 349, 352–353 Posterior probability, 279 Power controller, robots, 596 Power Industry, Big Data, 548 Practice of Law, robots, 604 Prediction method, 205 Predictive analytics, 4–5, 33, 126–127
forecasting, 465 logistic regression, 155 Predictive modeling, 251–255
in electrical power industry, 261A–262A
model ensembles for, 294f nearest neighbor
method. See Nearest neighbor method
training and testing of, 253f Prescriptive analytics, 4–5, 34–35
application, 466A–467A model
examples, 465 predictive analytics,
465 vignette, 461–462 Preset robots, 596–597 Pressure points, 581 PricewaterhouseCoopers (PwC), 621, 750 Privacy in intelligent technology, 732
example, 734 in IoT, 733 mobile user,
733 technology issues in, 734
violations, 735 Private cloud, 563 Probabilistic decision-making, 473 Probabilistic simulation, 497, 497t Probability distribution, 213 Problem ownership, 12 Process gains, 615 Processing element (PE), 325, 330 Process losses, 615 Production, data mining, 209 Production-operation management (POM),
110–112 Professional Certification, robots, 605 Project management, 14 Property, robots, 604 Propinquity, 450 Proximity sensors, 697 Public cloud, 563 Python, 36, 238, 370, 537, 563–564, 592
Q Qualitative data, 213 Quantitative data, 213 Quantitative models decision variables, 469
defined, 469 intermediate result variables,
470 result (outcome) variables, 469
structure of, 469f uncontrollable variables,
or parameters, 469–470
Query analyzer, 434f, 436 Query-specific clustering, 414 Question
answering, 394, 402
R Radial Basis Function (RBF) kernel, 273 Radio-frequency identification (RFID), 699 Random forest (RF) model, 299 RapidMiner software, 236, 238 Rapleaf Software Company, 734 Ratio data, 126 RDBM. See Relational database management
(RDBM) systems Real-time data analytics. See Stream analytics Real-time data warehousing, 24 Real-Time Decision Manager (RTDM), SAS,
745 Real-time location intelligence, 572–573 Recommendation/recommender system
application, 658A–660A
benefits of, 657–658
collaborative filtering, 658
content-based filtering, 658
defined, 657 process of,
656f Rectilinear distance, 231 Recurrent neural network (RNN), 343,
360–363, 361f, 365–367 Referral
Web sites, 443
Regression, 220 Regression modeling for inferential statistics,
151 application, 157A–162A correlation vs
regression, 151 linear regression, 152–155,
152f logistic regression, 155–156, 156f
recognizing good model, 153 simple vs
multiple regression, 152 time-series
forecasting, 156, 162–163, 163f Regular bots, 661 Regularization strategy, 338 Regulatory and compliance requirements, 653 Relational database management (RDBM)
systems, 23 Relation extraction, 358, 359f Remote-controlled robots, 597 Report, 163 Representation learning technique,
321, 322f Representative analytics as service offerings,
564–565 Residuals, 300 Responding Cycle, 434f Result (outcome) variables, 469, 469f, 470t Retailing industry, data mining, 209 RF. See Random forest (RF) model RFID. See Radio-frequency identification
(RFID) Ride sharing by Taxi Bot, 663 Right-time data warehousing, 24 Risk analysis, decision making, 472,
472A–473A RNN. See Recurrent neural network (RNN) Robo advisors
advice provided by, 677–678 defined,
676 financial advisors, 676 quality of
advice, 677–678 Robo Advisors 2.0,
676–677, 677A RoboCoke, 663 Robotics
Adidas, 586 Agrobot, 594 BMW, collaborative robots, 587 Care-
E Robot, 593–594 changing precision
technology, 586 Da Vinci Surgical
System, 592 in defense industry, 589
history, 584–586 illustrative
applications, 586–595 Mahindra & Mahindra Ltd., 589
MEDi, 593 overview, 584
Pepper, 590–592 San Francisco Burger Eatery, 588 Snoo (robotic crib), 593
Spyce, 588 systems, 91
Tega, 587 The Robotics Institute of America, 91 Robots, 91–92 (robo) advisors. See Robo
advisors autonomous, 91 categories of,
596–597 collaborative, 587, 597, 642
components of, 595–596, 595f as
coworkers, 641–642 on current and
future jobs, 600–603 dangers of, 753–
755 in defense industry, 589–590
effectors/rover/manipulator, 596 to
explore Mars, 643, 643f Huggable
Robot, 582f human-machine
collaboration and,
Index 227
640–641 legal implications and AI, 603–605
managers, 601 in motion, 597–600 (See also
Autonomous vehicles)
navigation/actuator system, 596
Pepper, 591f pilots and artists, 602
power controller, 596 preset, 596–
597 remote-controlled, 597
sensors, 595f, 596 social, 583
stand-alone, 597 supplementary,
597 taxation, 604 vignette, 581–
583 See also Robotics
Rockwell Intl. (arenasimulation.com), 501 Rotation estimation, 223 Rough sets method, 226 RTDM. See Real-Time Decision Manager
(RTDM), SAS Rule-based expert systems (ESs), 23
S Salesforce.com, 547, 547A San Francisco Burger Eatery, robotics, 588 SAS Institute Inc., 31
RTDM, 745 Visual Statistics, 565
Scatter/gather clustering, 414 Scene recognition, 90 Schrage’s models for AI, 99 Search engines
anatomy of, 434 application,
439A–440A defined, 433
development cycle, 434–435
optimization, 436–439
poisoning, 437 response cycle,
435–436 taxonomy, 431f Search spam, 437 Secondary node, 525 Self-driving vehicles. See Autonomous vehicles Self-organizing map, 231 Semistructured data, 125 Semistructured problems, 14 SEMMA (sample, explore, modify, model, and
assess) process, 218, 218f sensefly.com, 569 Sensitivity analysis
method, 13, 224–225, 225f, 487–488 on ANN
model, 340–341, 341f
application, 341A–342A Sensors, 91 applications and
RFID, 699 camera-based, 594
as components of robot, 595f
defined, 697, 700 and IoT,
697–699 smart, 700–701
technology, 697 vignette, 688–
689 Sensor to insight, 696 Sentiment analysis, 363A–365A
applications, 422A–424A concept, 418–
419 defined, 399 lexicon, 426–427
multistep process to, 425f polarity
identification, 426 process, 424–426
semantic orientation of documents, 428
semantic orientation of sentences and phrases, 428
training documents, 427 Sentiment detection, 424 SentiWordNet, 427
Sequence mining, 207 Server virtualization, 560 SGB. See Stochastic gradient boosting (SGB)
algorithm SGD. See Stochastic gradient descent (SGD) Shallow neural networks, 322 Shopbots, 92 ShopiiBot, 663 Shopping advisors (shopbots), 679 Short message service (SMS), 21 Short-term memory, 362 Sigmoid transfer functions, 326, 337 Simio (simio.com), 501 Simon’s process of decision-making, 9–10 Simple linear regression, 155 Simple logistic regression, 155 Simple regression analysis, 152 Simple split, 222–223, 222f Simulation models, 23 advantages,
494–495 application, 493A–494A
characteristics, 493 defined, 493
disadvantages, 495 discrete event,
498 methodology, 495–496 Monte
Carlo simulation, 497–498 pivot
grid report, 504f process, 496f, 503f
Simio interface view, 502f standard
report view, 503f test and validation,
495 types, 496–497 visual
interactive, 500–501 Single-input neuron, 325f Singular value decomposition (SVD), 413 Siri (Speech Interpretation and Recognition
Interface), 366, 372, 675, 760 SiriusXM Satellite Radio, 118–121 Skype Translator service (Microsoft), 367,
367f Slack workspace, 621 Smart appliance, 704–705 Smart assistant shopping bots, 679–680 Smart cities application, 708A, 711A–712A Bill
Gates’ future, 713 combining analytics and,
713 defined, 707 IBM’S cognitive buildings,
709, 709f improving transportation in, 712–
713 SAS analytics model for, 713 smart
buildings, 709 smart components and smart
factories in, 709–710
smart (digital) factories in, 710–714 technology
support for, 713 Smart factories, 710–714 characteristic, 711f
defined, 710 smart bike production in,
710–711 smart components in smart
cities and, 709–710
Smart homes and appliances
available kits for, 705 barriers
to adoption, 707 Bot, 706
components, 703–704, 704f
defined, 703 Google’s nest,
705 iHealthHome, 704 smart
appliances, 704–705 Smart sensor, 700 Smart vehicles, 714–715, 715A SMS. See
Short message service (SMS) sncf.com,
701 Snoo (robotic crib), 593 Snowflake, customer experience, 566–567
Social collaboration defined, 622 popular collaboration
software, 623 in social networks, 622 software, 622–623 tools, support
collaboration and commu- nication, 623
Social media analytics
accessibility, 452 accuracy of
text analysis, 454 best practices,
453–455 beyond brand, 454
concept, 451–452 connections,
450 defined, 451–453
distributions, 450–451 elusive
sentiment, 454 frequency, 452
impact, 453 intelligence, 454–
455 measurement, 453 powerful
influencers, 454 quality, 451
reach, 451–452 ripple effect,
454 tools, 454 updatability, 452
usability, 452–453 user
engagement, 452f Social network analysis, 446–450, 447A Social robot, 583 Softmax transfer function, 359 Software
AI, 717 ANN, 339–340 backend, 693
data mining, 209, 237t libraries. See
Libraries (software) popular
collaboration, 623 requirements check,
539 simulation, 495, 501 social
collaboration, 622–623 Tableau, 169A–
171A, 180f, 184A, 565 tools, 236–238, 239f Weka, 236
Software as a Service (SaaS), 559 Solver file, 369 SOM. See Kohonen’s self-organizing feature
map (SOM) Spamdexing, 437 Special weapons observation reconnaissance
detection system (SWORDS), 589 Speech acts, 399 Speech analytics, 398–399 Speech recognition, 402 Speech synthesis, 402 Speech (voice) understanding technology,
92 Spiders (web crawlers), 431 Split point, 227 Sports analytics, 38–43, 156 Sports, data mining, 211 Spreadsheets application, 474A,
475A decision modeling and, 473–
476 excel dynamic model, 477f
static model, 476f Spyce, robotics, 588 Sqoop
tool, 526 sstsoftware.com,
569 Stacking method, 300,
301f Stand-alone robots, 597 State unit, 362 Static model, 467 Static network, 361 Statistical modeling for business analytics. See
Business analytics (BA), statistical modeling
for
228 Index
Statistics, 139, 147f, 148f conversion, 444–445
and descriptive analytics, 139f, 140, 146
inferential, 140 statistical analysis, 226 text
analytics, 393f vs data mining, 208 Statistics-based classification techniques,
205 Stemming process, 394 Stochastic decision-making, 473 Stochastic gradient boosting (SGB) algorithm,
299–300 Stochastic gradient descent (SGD), 336 Stop words, 394 Storage virtualization, 560 Stormboard (stormboard.com), 625–626 Strategic planning, 14 Stream analytics applications of,
546 critical event processing, 545
data stream mining, 546 defined,
521, 544 e-Commerce, 546 financial services, 548 government, 548–
549 health sciences, 548 law
enforcement and cybersecurity, 547–548 mobile health care
services, 565 power industry, 548
telecommunications, 546–547 use
case of, 545f vs perpetual analytics,
544–545 Structural holes, 450 Structured data, 125, 393 Structured problems, 14 Summarization, 394 Super learner, 300 Supervised induction, 205 Supervised learning process, 205, 335, 336f Supplementary robots, 597 Supply chain management (SCM), 27 Support metric, 234 Support vector machine (SVM), 263–264
application, 264A–268A dual form, 269–270
Kernel trick, 271 mathematical formulation
of, 269 nonlinear classification, 270 primal
form, 269 process-based approach, 271–
273, 272f soft margin, 270 vs ANN, 279 Swarm AI application,
640A–641A for
predictions, 640
technology, 639 Swarm intelligence, 639 Synapse, 257 Synchronous (real-time) mode communication,
617 Syntactic ambiguity, 398
T Tableau software, 169A–171A, 180f, 184A,
565 TAN. See Tree Augmented Naïve (TAN) Bayes
method Target identification, 425–426 Taxation, robots, 604 Taxicab distance, 231 TDM. See Term-document matrix (TDM) Team collaboration AI support, 637–
638 computerized tools and
platforms, 618–619 group collaboration for decision
support,
618 spark collaboration platform, 638
time/place framework, 617–618, 618f
vignette, 611–613 Technologies of AI, 87, 87f application, 89A,
97A autonomous business decisions, 99
chatbots, 94 computer vision, 90 DL, 88–89
emerging, 94–95 examples, 88, 90–92, 98 IA,
87 knowledge and expert systems, 93, 94f
machine learning, 88 machine translation of
languages, 92 machine vision, 90 NLP, 92
recommendations, 93 robotic systems, 91
speech (voice) understanding, 92 Technology
insight ANN software, 339 augmented
intelligence, 56, 82 benefits and dysfunctions
of working in groups, 615–616 Big data
technology platform, 552–554 biological and artificial neural networks,
258 calculating descriptive statistics in Excel, 146 Chatbots’ platform providers, 670–671 Cisco
improves collaboration with AI, 638 data size,
515–516 elements of ANN, 330 Gartner, Inc.’s business intelligence platform,
177 Hadoop, demystifying facts, 527–528 LP,
479 popular search engines (August 2016),
438 predictive text mining and
sentiment analysis, 428
RFID sensors, 700 SAS Decision Manager, 745 Schrage’s
models for AI, 99 storytelling, 178 Teradata Vantage™, 552–554 text
mining, 394–395 Toyota and Nvidia Corp. (autonomous
driving), 716 Technology providers, 64 Technology trends of intelligent systems,
756–759 Tega, robotics, 587 Tencent (e-commerce company), 761 TensorBoard (visualization module), 369 TensorFlow (learning framework), 369 Tensor Processing Unit (TPU), 369 Teradata University Network (TUN), 3, 28 Teradata Vantage
application, 554A–556A
architecture, 553f data sources
integrated into, 511f Term–document matrix (TDM), 411–413, 411f Test drivers and quality inspectors, 602 Test set, 222 Text analytics, 392–394, 393f Text categorization, 413 Text mining, 24
academic applications, 407–408 biomedical
applications, 404–407 CNN for relation
extraction in, 359 combined data set, 416t
context diagram for, 410f Corpus, 410–411
defined, 392 knowledge extraction, 413–418
marketing applications, 402–403 Netflix,
395A–397A overview, 392–394 process, 410f
research literature survey with, 415A–417A
security applications, 403–404 term-document
matrix creation, 411–412 text analytics and,
393f textual data, 393f three-step/task, 411f
use application, 408A–409A Text processing using CNN, 357–360 Text proofing, 402 Text segmentation, 398 Text to speech, 402 Theano
(software), 369–370
theroboreport.com, 681 Tie strength, 451 Time-dependent vs time-independent
simulation, 497 Time on site, 442 Time/place framework, 617–618, 618f Time-series forecasting, 156, 162–163, 163f,
207–208 Tokenizing, 395 Tone Analyzer, Watson, 378 Topic tracking, 394 Topologies, 330 Torch (computing framework), 368 Tort
liability, 603 TPU. See Tensor Processing Unit (TPU) Training process, 328, 372 Training set, 222 Transaction vs analytic processing, 27–28
Transitivity, 450 Travel industry, data mining, 209 TreeAge Pro (TreeAge Software Inc.,
treeage.com), 492 Tree Augmented Naïve (TAN) Bayes method,
289, 290f Trend analysis, text mining, 414 Trial-and-
error approach, 7, 488 TrueText
(Microsoft), 367 Turing Test of AI, 85, 85f
U Uber Technologies, Inc., 727–728 Uncertainty, decision making, 471f,
472A–473A Uncontrollable variables, 469, 469f, 470t,
491t Universal basic income (UBI), 602 Unstructured data, 125, 394 Unstructured problems, 14, 16 Unsupervised learning process, 205, 344 User interface subsystem, 20–21, 654 Utopia (optimistic approach), 753 Utrip
(utrip.com), 678
V Vanishing gradient problem, 354 Variable identification, 465 Variable selection process, 131 Variance (predictive analytics), 295
vCreaTek.com LLC, 46 Vera Gold
Mark (VGM), 666A, 667A Video analytics, 91 VIM. See Visual interactive modeling (VIM) Virtual collaboration workspace, 621 Virtual digital assistants, 374 Virtual meeting systems, 620–621
collaborative workflow, 621 digital
collaborative workspace, 621 slack, 621–
622 vendors of virtual workspace, 621 Virtual personal assistant (VPA),
733, 761
Index 229
Amazon’s Alexa and Echo, 672–674
Apple’s Siri, 675 defined, 672 Google
Assistant, 675 for information search,
672 knowledge for, 675 other personal
assistants, 675 tech companies
competition, 675 Virtual teams, 615, 625 VIS. See Visual interactive simulation (VIS) Visual analytics, 176, 182f high-powered
environment, 180–181 story structure,
178–179, 180f Visual interactive modeling (VIM)
application, 501A–504A defined,
500 and DSS, 500–501 Visual interactive simulation (VIS) application,
501A–504A concept, 500 conventional
simulation inadequacies, 500 defined, 500
models and DSS, 500–501 simulation
software, 501 Viv, VPA, 675 Voice of the customer (VOC), 422–423 Voice of the employee (VOE), 423 Voice of
the market (VOM), 423
W Walnut chatbot, 663 WaterCop (watercop.com) system, 703 Watson, IBM, 583 analytics, 4–5, 21, 63,
747 application, 376A Deep Blue (chess
program), 375 DeepQA architecture,
376–377, 377f future, 377–381
Personality Insight, 378 Tone Analyzer (IBM), 378
Waymo self-driving cars, 727–728 Web analytics conversion
statistics, 444–445 dashboard,
445f defined, 430, 431f, 441
metrics, 442 technologies, 441–
442 traffic sources, 443–444
visitor profiles, 444 web site
usability, 442–443 Web content
mining, 393f, 431–433 Web crawlers, 431, 434–435, 434f Webex (Cisco), 620–621, 638 Web mining
defined, 393f, 430, 431f overview,
429–433 taxonomy, 431f Web site design, 653 Web structure mining
defined, 433 taxonomy,
431f text analytics, 393f Web usage mining, 393f, 431f,
441, 441f See also Web analytics
WeChat’s super chatbot, 666A Weight function, 325 Weka software, 236 What-if analysis, 488–489, 489f Wikipedia, 339 Wimbledon.com, 420A–422A Wireless
technology, 8 Wisdom of the crowd, 629 Word disambiguation, 393f WordNet
defined, 399 web site (wordnet.princeton.edu),
426 Word sense disambiguation, 398 word2vec
project, Google, 357–358, 358t Word
vectors/embeddings, 357, 358f, 359 World Wide
Web, 434f
Y Yahoo!, 339, 433, 437, 526, 537, 550
yourencore.com, 633 YourMd chatbot,
679 YouTube, 29 YuMi (human-robotic system), 641
Z Zoom.ai chatbot 663
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