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Analytics, Data Science and A I: Systems for Decision Support

Eleventh Edition

Chapter 2

Artificial Intelligence Concepts, Drivers, Major Technologies, and Business Applications

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Learning Objectives (1 of 2)

2.1 Understand the concepts of artificial intelligence (A I).

2.2 Become familiar with the drivers, capabilities, and benefits of A I.

2.3 Describe human and machine intelligence.

2.4 Describe the major A I technologies and some derivatives.

2.5 Discuss the manner in which A I supports decision making.

Slide 2 is list of textbook LO numbers and statements

Learning Objectives (2 of 2)

2.6 Describe A I applications in accounting.

2.7 Describe A I applications in banking and financial services.

2.8 Describe A I in human resource management.

2.9 Describe A I in marketing.

2.10 Describe A I in production-operation management.

Slide 2 is a list of textbook LO numbers and statements

Opening Vignette (1 of 3)

I N R I X Solves Transportation Problems

http://www.inrix.com

The problem…

The solution…

The results…

Opening Vignette (2 of 3)

I N R I X Solves Transportation Problems

Questions for the Opening Vignette:

Explain why traffic may be down while congestion is up (see the London case at inrix.com/ uk -highways-agency /).

How does this case relate to decision support?

Identify the A I elements in this system.

Identify developments related to A I by viewing the company’s press releases from the most recent four months at inrix.com/press-releases. Write a report.

Opening Vignette (3 of 3)

I N R I X Solves Transportation Problems

Questions for the opening vignette (cont.):

According to Gitlin (2016), I N R I X’s new mobile traffic app is a threat to Waze. Explain why.

Go to sitezeus.com/data/ inrix and describe the relationship between I N R I X and Zeus. View the 2:07 min. video at sitezeus.com/data/ inrix /. Why is the system in the video called a “decision helper”?

Introduction to Artificial Intelligence

Definitions for artificial intelligence (A I)

Many definitions of A I

Relationship between A I and logic

plato.stanford.edu/entries/logic-ai

Major characteristics of A I machines

Smarter computers/machines

Major elements of A I …

The Functionalities and Applications of A I

Artificial Intelligence (A I) (1 of 8)

Many application of A I exists

Example: Pitney Bowes Is Getting Smarter with A I

Major goals of A I

Perceive and properly react to changes in the environment that influence specific business processes and operations.

Introduce creativity in business processes and decision making.

Artificial Intelligence (A I) (2 of 8)

Drivers of A I

Interest in smart machines and artificial brains

The low cost of A I applications

The desire of large tech companies

The pressure on management to increase productivity

The availability of quality data

The increasing functionalities and reduced cost of computers in general

The development of new information technologies, particularly the cloud computing

Artificial Intelligence (A I) (3 of 8)

Benefits of A I

A I has the ability to complete certain tasks much faster

The consistency of the work

A I machines do not make arbitrary mistakes

A I systems allow for continuous improvement projects

A I can be used for predictive analysis via its capability of pattern recognition

A I can manage delays and blockages in business processes

A I machines do not stop to rest or sleep

Many more…

Artificial Intelligence (A I) (4 of 8)

Figure 2.2 Cost of Human Work versus the Cost of A I Work.

Artificial Intelligence (A I) (5 of 8)

Examples of A I Benefits

I S D A uses A I to eliminate tedious activities

A I revolutionizing business recruitment

A I is redefining management

Help blind people experience the world around them

Identify overlooked borrowers

Predict customer expectation

Startup A I companies are emerging in large numbers

Most impactful: customer experience and enjoyment.

Artificial Intelligence (A I) (6 of 8)

Some limitations of A I Machines

Lack human touch and feel

Lack attention to non-task surroundings

Can lead people to rely on A I machines too much

Can be programmed to create destruction

Can cause many people to lose their jobs

Can start to think by themselves, causing significant damage

Hypothetically … no evidence of that!

These limitations are diminishing over time

Artificial Intelligence (A I) (7 of 8)

What A I can and cannot do?

Three flavors of A I decisions

Assisted intelligence

Autonomous intelligence

Augmented intelligence

Artificial brain

A people made machine “as intelligent, creative, and self-aware as humans”

To date, no one has created such a machine

Artificial Intelligence (A I) (8 of 8)

Technology Insight – Augmented Intelligence

Combining the performance of people and machines [combining  augmenting]

Augmented machines extend human abilities

Examples

Cybercrime fighting

E-commerce decisions

High-frequency stock market trading

Human and Computer Intelligence (1 of 4)

What is intelligence?

Types of intelligence:

Linguistic and verbal, logical, spatial, body/movement, musical, interpersonal, intrapersonal, naturalist

Intelligence is not a simple concept!

Content of intelligence

Reasoning, learning, logic, problem-solving, perception, and linguistic ability

Human and Computer Intelligence (2 of 4)

Capabilities of intelligence

Learning or understanding from experience

Making sense out of ambiguous, incomplete, or even contradictory messages and information

Responding quickly and successfully to a new situation (i.e., using the most correct responses)

Understanding and inferring in a rational way, solving problems, and directing conduct effectively

Applying knowledge to manipulate environments

Recognizing and judging the relative importance of different elements in a situation

Human and Computer Intelligence (3 of 4)

How intelligent is A I?

Comparing human intelligence with A I

Table 2.1 Artificial Intelligence versus Human Intelligence.

Area	AI	Human
Execution	Very fast	Can be slow
Emotions	Not yet	Can be positive or negative
Computation speed	Very fast	Slow, may have trouble
Imagination	Only what is programmed for	Can expand existing knowledge
Answers to questions	What is in the program	Can be innovative
Flexibility	Rigid	Large, flexible

Many more, in the book…

Human and Computer Intelligence (4 of 4)

Measuring A I: The Turing Test

Application Case 2.1

How Smart Can a Vacuum Cleaner Be?

Questions for Discussion:

How did the Korean researchers determine the performance of the vacuum cleaners?

If you own (or have seen) the Roomba, how intelligent do you think it is?

What capability can be generated by the deep learning feature? (You need to do some research.)

Find recent information about L G’s Roboking. Specifically, what are the newest improvements to the product?

Major A I Technologies & Drivers (1 of 3)

Intelligent agents

Intelligent? Autonomous? Mobile? …

Machine learning

“Human learning embedded into machines”

Deep learning

A part of machine learning (see Chapter 6)

Computer vision (machine vision)

Video analytics

Major A I Technologies

Application Case 2.2

How Machine Learning Is Improving Work in Business

Questions for Discussion:

Discuss the benefits of combining machine learning with other A I technologies.

How can machine learning improve marketing?

Discuss the opportunities of improving human resource management.

Discuss the benefits for customer service.

Major A I Technologies & Drivers (2 of 3)

Robotic systems

Industrial robots [for manufacturing]

Service robots

Example: Walmart is using robots to properly stock shelves

Use of robots (or bots) in eComemrce

Many are being used at Amazon.com

Online shopping robots (shopbots)

SoftBank – a cellphone store in Tokyo entirely staffed by robots

Major A I Technologies & Drivers (3 of 3)

Natural language processing

Natural language understanding

Natural language generation

Speech (voice) understanding

An interesting application cs.cmu.edu/~./listen

Machine translation of human languages

Balel fish (babelfish.com)

Google translator (translate.google.com)

Example: Sogou’s travel translator

Knowledge and Expert Systems (1 of 2)

Knowledge sourced intelligent systems

Knowledge acquisition

Identifying experts

Knowledge representation

Reasoning from knowledge

Chatbots

Emerging A I technologies

Effective computing

Biometric analysis

Knowledge and Expert Systems (2 of 2)

Cognitive computing

Knowledge derived from cognitive science

Self learning algorithms

I B M Watson

More on this is covered in Chapter 6

Augmented reality

Augmentation: integration of digital information within the user environment in real time

Real + virtual combined

Virtual reality

Automated Decision Making Process

A I Support for Decision Making

Jeff Bezos, the C E O of Amazon.com, said in May 2017 that A I is in a golden age …

A I can …

Solve complex problems that people have not been able to solve.

Make much faster decisions.

Find relevant information, even in large data sources, very fast.

Make complex calculations rapidly.

Conduct complex comparisons and evaluations in real time.

Watch “A I Will Be Making Decisions for You” at https://www.youtube.com/watch?v=Dr9jeRy9whQ

Using A I in Decision Making

Issues & factors:

The nature of the decision [routine vs non-routine]

The method of support / technologies used

Expert systems, recommender systems

Deep learning, pattern recognition, biometrics recognition

Cos-benefit and risk analysis

Using business rules

A I algorithms

Speed

A I Support for Decision-Making Process

As it relates to Simon’s decision making process (see Chapter 1 for the background information)

A I support in problem identification

A I support in generating or finding alternative solutions

A I support in selecting a solution

A I support in implementing the solution

A I can (and should) play a role in each and every step in the decision making process

Application Case 2.3

How Companies Solve Real-World Problems Using Google’s Machine-Learning Tools

Questions for Discussion:

Why use machine learning for predictions?

Why use machine learning for detections?

What specific decisions were supported in the five cases?

Intelligent & Automated Decision Support

Automated decision making (since 1970s)

Common examples:

Small loan approvals

Initial screening of job applicants

Simple restocking

Prices of products and services (when and how to change them)

Product recommendation (e.g., at Amazon.com)

Example: Supporting Nurses Diagnosis Decisions

An experiment conducted in a Taiwanese hospital (in 2015)

87% agreement between A I and human experts

Technology Insight 2.2

Schrage’s Models for Using A I to Make Decisions

The autonomous advisor

The autonomous outsource

People-machine collaboration

Complete machine autonomy

Implementing these four models require appropriate management leadership and collaboration with data scientists.

A I Applications in Accounting

A I in big accounting firms (see application case 2.4)

A I in small accounting firms

Solve complex billing problems (especially in healthcare)

Claim processing and reimbursement

Real estate contracts, risk analysis …

A I provides cheaper and better data-driven support

Generates needed insights from data analysis

Frees time of accountants for more complex tasks

Machine learning is often used for prediction

A I will improve and automate accounting tasks but at the same time will take away some accounting jobs.

Application Case 2.4

How E Y, Deloitte, and P w C Are Using A I

Questions for Discussion:

What are the characteristics of the tasks for which A I is used?

Why do the big accounting firms use different implementation strategies?

A I Applications in Financial Services (1 of 2)

Diverse use of A I, in banking and insurance.

Examples of A I use in general financial services:

Extreme personalization (e.g., chatbots, personal assistants, etc.)

Shifting customer behavior both online and in branches

Facilitating trust in digital identity, revolutionizing payments

Sharing economic activities (e.g., person-to-person loans)

Offering financial services 24/7 and globally

Banking can also uses A I for …

Face recognition (safer online banking), help customer with smart investment decisions, prevent money laundering, …

Insurance – mostly in issuing policies and handling claims

A I Applications in Financial Services (2 of 2)

Application of A I uses in Banking

Employee surveillance (A I machines, e.g., I B M Watson).

Tax preparation/filing (H&R block uses I B M Watson).

Automated customer service; answering customer inquiries in real-time.

See Rainbird Co. ar rainbirf.ai as a company that provides such services (using I B M Watson).

Automated online support for paying bills and account inquiries using Amazon Alexa (e.g., Capital One).

Fraud detection and anti–money-laundering activities; also improving customer experience (Bank Danamon).

Victual banking assistant, Olivia at H S B C, learn from experience and helps customer better.

Application Case 2.5

U S Bank Customer Recognition and Services

Questions for Discussion:

What are Einstein’s advantages to U S Bank?

What are its advantages to customers?

What are the benefits of voice communication?

A I in Human Resource Management (1 of 2)

Recruitment – talent acquisition

See Application Case 2.6 for an example

Training – A I facilitates training

Performance assessment (evaluation)

Retention –eliminating attrition

Predicting attrition way ahead of time to eliminate loss of talent

Using chatbots for supporting H R M

See olivia.paradox.ai.

A I in Human Resource Management (2 of 2)

Introducing A I to H R M operations:

Experiment with a variety of chatbots

Develop a team approach involving other functional areas

Properly plan a technology roadmap for both the short and long term, including shared vision with other functional areas

Identify new job roles and modifications in existing job roles in the transformed environment

Train and educate the H R M team to understand A I and gain expertise in it.

Application Case 2.6

How Alexander Mann Solutions (A M S) Is Using A I to Support the Recruiting Process

Questions for Discussion:

What types of decisions are supported?

Comment on the human–machine collaboration.

What are the benefits to recruiters? To applicants?

Which tasks in the recruiting process are fully automated?

What are the benefits of such automation?

A I in Marketing, Advertising, & C R M (1 of 2)

One of the richest area for A I applications:

Product and personal recommendations

Smart search engines

Fraud and data breaches detection

Social semantics

Web site design

Producer pricing

Predictive customer service

… many more in the book …

A I in Marketing, Advertising, & C R M (2 of 2)

Improving customer experience and C R M

Use N L P for generating user documentation. This capability also improves the customer–machine dialogue.

Use visual categorization to organize images (for example, see I B M’s Visual Recognition and Clarifai)

Provide personalized and segmented services by analyzing customer data. This includes

A I in C R M Example: Salesforce’s A I Einstein

Application Case 2.7

Kraft Foods Uses A I for Marketing and C R M

Questions for Discussion:

Identify all A I technologies used in the Food Assistant.

List the benefits to the customers.

List the benefits to Kraft Foods.

How is advertising done?

What role is “behavioral pattern recognition” playing?

Compare Kraft’s Food Assistant to Amazon.com and Netflix recommendation systems.

A I in Production-Operation Management

A I in manufacturing

Automation for compliance and cost reduction

React quicker and more effectively (agility)

Implementation model

Streamlining processes, smart outsourcing, work automation, improving customer experience

Intelligent factories

Logistic and transportation

Example: D H L supply-chain

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