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Lesson 4: Analytics, Decision Support and Artificial Intelligence

Information Technology & Data Analytics

October 25, 2021

Information Technology & Data Analytics

Analytics, Decision Support, and Artificial Intelligence: Brainpower for Your Business

Chapter 4

Information Technology & Data Analytics

1. Compare and contrast decision support systems and geographic information systems.

2. Describe the decision support role of specialized analytics (predictive and text analytics).

3. Describe the role and function of an expert system in analytics.

4. Explain why neural networks are effective decision support tools.

5. Define genetic algorithms and the types of problems they help solve.

6. Describe data-mining agents and multi-agent systems.

STUDENT LEARNING OUTCOMES

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Introduction

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➢ What is the most important asset organizations have?

• Information!

➢ Businesses make decisions everyday

➢ Some big and some small

➢ Many IT tools can aid in the decision-making process

➢ Analytics is now key to the success of any business

INTRODUCTION

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Chapter Focus

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Decisions and Decision Support

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DECISIONS AND DECISION SUPPORT: Phases

➢ Carry out the chosen solution and

monitor the results

o Fine tuning, quality control

➢ Examine the merits of each solution

and choose the best one (Prescriptive)

o Cost, staffing, timing ease to

implement

➢ Consider ways of solving the problem

o Build models (find fixes) to create

solutions on paper

➢ Find or recognize the problem, need, or

opportunity (Diagnostic)

o Detect and interpret signs

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➢ Satisficing

• Making a choice that meets your needs

• It is satisfactory without necessarily being the best choice

➢ Possible reasons –

• Fair price

• Reasonable profit

• Regulations

➢ Growth

• Maximum growth -> Optimizing strategy

• High growth -> Satisfying strategy

Decisions and Decision Support

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➢ Structured decision

• Processing a certain information in a specified way so you always get the right answer

➢ Non-structured decision

• May be several “right” answers, without a sure way to get the right answer

➢ Recurring decision

• Happens repeatedly

➢ Nonrecurring (ad hoc) decision

• One you make infrequently

Types of Decisions You Face

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Types of Decisions You Face

EASIEST

MOST DIFFICULT

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➢ Calculating your employees wages

➢ Getting a new job

➢ Deciding how much inventory to carry

➢ Deciding on a new price for your products

➢ Where to build a new distribution center

➢ Acquiring another company

Structured decision Non-structured

Recurring decision Nonrecurring (ad hoc)

Types of Decisions You Face

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➢ Decision support system (DSS)

• A highly flexible and interactive system that is designed to support decision making when the problem is not structured

➢ Decision support systems help you analyze, but you must know how to solve the problem, and how to use the results of the analysis

Decision Support Systems

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➢ User interface management component – allows you to communicate with the DSS

• Should be intuitive and easy-to-use

➢ Data management component – stores and maintains the information that you want your DSS to use. Information can be:

• Organizational – databases, data warehouses, specialized systems

• External – Stock information, public databases

• Personal – Insights and experience

➢ Model management component – consists of both the DSS models and the model management system

• Statistical and analytical tools, techniques and models

Components of a DSS

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Components of a DSS

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Geographic Information

Systems

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➢ Geographic information system (GIS) – DSS designed specifically to analyze spatial information

➢ Spatial information is any information in map form

➢ Businesses use GIS software to analyze information, generate business intelligence, and make decisions

GEOGRAPHIC INFORMATION SYSTEMS

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GEOGRAPHIC INFORMATION SYSTEMS

32National Hurricane Center's projection of Irma's track

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➢ The Columbia Shuttle tragedy back in 2003

➢ Tracking delivery buses

➢ Demographic Distribution

➢ Road Conditions

➢ Meteorological Conditions

GEOGRAPHIC INFORMATION SYSTEMS

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Google Earth as a GIS

34 Restaurants near the Eiffel TowerGoogle Earth

https://earth.google.com/web/search/restaurants+near+Eiffel+Tower,+Avenue+Anatole+France,+Paris,+France/@48.8581,2.294509,33.77923467a,1442.89889623d,35y,0h,60t,0r/data=CigiJgokCX4Uy-Q_bkhAEQ_aY3BlbUhAGRqg2seFdAJAIZNNc4PaSgJA

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Google knows where you’ve been!

35Google Timeline

https://www.google.com/maps/timeline

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Data-Mining Tools and

Models

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➢ Business need IT-based analytics tools

• Databases and DBMSs

• Query-and-reporting tools

• Multidimensional analysis tools

• Digital dashboards

• Statistical tools

• GIS

• Specialized analytics

• Artificial intelligence

DATA-MINING TOOLS AND MODELS

Our remaining

focus

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➢ Association/dependency modeling – cross-selling opportunities, recommendation engine effectiveness

➢ Clustering – groups of entities that are similar (without using known structures). • Unsupervised learning (no predefined classes) • What do teachers and sumo fighters have in common?

oThey both cheat • Pattern recognition, document classification • Insurance – identify high cost groups

➢ Classification – use historical data to derive future inferences. • Supervised learning, groups are known. • Fraud detection, email spam

Data-Mining Tools and Models Support

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➢ Regression – find corollary and often causal relationships between data sets.

• Example: Target is the house value, data: age of the house, income, square footage, number of rooms, taxes.

➢ Summarization – basic, but powerful

• Sums, averages, standard deviations, histograms, frequency distributions

• Example: Census data to understand the relationship between the salary and educational level in the United States

Data-Mining Tools and Models Support

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➢ Predictive analytics – highly computational data-mining technology that uses information and business intelligence to build a predictive model for a given business application

➢ Which industries use Predictive Analytics?

• Insurance, retail, healthcare, travel, financial services, CRM, SCM, credit scoring, etc.

Predictive Analytics

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➢ Predictive analytics – use historical information to predict future events and outcome

➢ Prediction goal – the question you want addressed by the predictive analytics model

• Credit scoring, SCM, CRM, health analysis

➢ Prediction indicator – specific measurable value based on an attribute of the entity under consideration

Predictive Analytics

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Predictive Analytics

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➢ Prediction goal – What customers are most likely to respond to a social media campaign within 30 days by purchasing at least 2 products in the advertised product line?

➢ Prediction indicators – Which get assigned weights

• Frequency of purchases

• Proximity of date of last purchase

• Presence on Facebook and Twitter

• Number of multiple-product purchases

Predictive Analytics Example

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➢ Text analytics – uses statistical, artificial intelligence (AI), and linguistic technologies to convert textual information into structured information

• Surveys

• Blogs

• Social media (emotions, opinion, sentiments, ideas)

• Emails (Spamming)

➢ Works primarily with non-structured elements: natural language

Text Analytics

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➢ The industry has estimated revenues of $1 billion USD in 2010, and of $2.7 billion USD in 2016.

• Projected: $18.4 billion USD by 2024.

Text Analytics

45Esticast Text Analytics Market Report, October 2017

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➢ Gaylord Hotels uses text analytics to make sense of customer satisfaction surveys

• Identifies negative or positive comments and correlates them

➢ Knowledge Management

➢ Cybercrime

➢ Customer Service

➢ Contextual advertising (AdmantX)

• Analyzes emotion, topics, people, products, organizations

Text Analytics: Examples

http://www.admantx.com/

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Natural Language Processing Models

➢ Lexical analysis – word frequency distributions

➢ Named entity recognition – identifying peoples, places, and things

➢ Disambiguation – meaning of a named entity recognition • “Ford” can refer to how many different things?

➢ Coreference – handling of differing noun phrases that refer to the same object • His manager fired them. The clown couldn’t even show his face in

the office that day!

➢ Sentiment analysis – discerning subjective business intelligence such as mood, opinion, and emotion

Text Analytics Support

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➢ Web analytics – understanding and optimizing Web page usage

• Search engine optimization (SEO) – improving the visibility of Web site using tags and key terms

➢ HR analytics – analysis of human resource and talent management data

➢ Marketing analytics – analysis of marketing-related data to improve product placement, marketing mix, etc.

Endless Analytics

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➢ CRM analytics – analysis of CRM data to improve sales force automation, customer service, and support

➢ Social media analytics – analysis of social media data to better understand customer/organization interaction dynamics

➢ Mobile analytics – analysis of data related to the use of mobile devices to support mobile computing and mobile e-commerce (m-commerce)

Endless Analytics

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Artificial Intelligence

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➢ Artificial intelligence, the science of making machines imitate human thinking and behavior, can replace human decision making in some instances

➢ Can be stand-alone or embedded into a larger system

➢ Uses

• To manage assets, invest in stock market

• Hospitals: assigned beds, diagnosing and treating illnesses

• To detect credit card fraud

• To find fraudulent insurance claims

• Airline ticket pricing

• Meteorology

ARTIFICIAL INTELLIGENCE

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➢ Major Categories

• Expert systems

• Neural networks (and fuzzy logic)

• Genetic algorithms

• Agent-based technologies

ARTIFICIAL INTELLIGENCE

➢ Main Characteristic

❑Knowledge

❑Learning

❑Evolution

❑Independent Collaborators

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➢ Expert (knowledge-based) system – an artificial intelligence system that applies reasoning capabilities to reach a conclusion • Suited for recurring problems solved with specific steps • Based on rules (if – then) • Could use heuristics (limited time/information to make a decision)

➢ Used for • Diagnostic problems (what’s wrong?) • Prescriptive problems (what to do?)

➢ Parts: • Knowledge base • Inference engine

➢ Domain – Built for a specific application

Expert Systems

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Traffic Light Expert System

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➢ An expert system can

• Reduce errors

• Improve customer service

• Reduce cost

➢ An expert system can’t

• Use common sense

• Automate all processes

What Expert Systems Can and Can’t Do

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➢ Neural network (artificial neural network or ANN) – an artificial intelligence system that is capable of finding and differentiating patterns

• Identification

• Classification

• Prediction

Neural Networks and Fuzzy Logic

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➢ ANN learning – • Supervised learning (Classification, regression) • Non-supervised learning (Clustering, association)

➢ Other details • Multiple inputs (dendrites) • Inputs are weighed • Multiple layers possible • Feedback is possible • Inputs process an output (body) • Each neuron has just one output

(axon) • Learning rule

➢ Deep Learning – Multiple hidden layers

Neural Networks and Fuzzy Logic

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➢ Learn and adjust to new circumstances on their own

➢ Take part in massive parallel processing

➢ Function without complete information

➢ Cope with huge volumes of information

➢ Analyze nonlinear relationships

➢ Can have “long-term” memory

Neural Networks Can:

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➢ Visual patterns • Bethge Lab’s Van Gogh project based on deep, convolutional

neural networks

➢ Speech recognition

➢ Fraud detection

➢ Heart attack detection

➢ Marketing

Neural Networks: Applications

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➢ Fuzzy logic – a mathematical method of handling imprecise or subjective information • Fuzzy -> vague, indistinct, ambiguous • Conventional logic: 1 or 0, Yes or No • Fuzzy logic: 0.9 or 0.8 or 0.5, Yes or maybe or perhaps or no

➢ Applications

• Text analytic systems

• Google’s search engine

• Washing machines

• Antilock breaks

Fuzzy Logic

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➢ Genetic algorithm

• An artificial intelligence system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem

• Capable of following trial and error

• Can work with a lot of noise

• It looks for the best individuals, like in nature

➢ Operators:

• Selection – best individuals pass their genes

• Mutation – random “flip” to maintain diversity

• Crossover – offspring created by recombination

Genetic Algorithms (GA)

Information Technology & Data Analytics

➢ Genetic algorithm

• An artificial intelligence system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem

• Capable of following trial and error

• Can work with a lot of noise

• It looks for the best individuals, like in nature

➢ GA can:

• Take thousands or even millions of possible solutions and combine and recombine them until it finds the optimal solution

• Work in environments where no model of how to find the right solution exists

Genetic Algorithms (GA)

Information Technology & Data Analytics

➢ Process or Algorithm:

1. Generate initial population

2. Feed the fitness of the population

3. Run through generations

a. Select parents

b. Crossover

c. Possible mutation

d. Compare against fitness

4. Complete when the best individuals are fit

Genetic Algorithms (GA)

Information Technology & Data Analytics

➢ Investments - Stock diversification: Which stocks to choose if I want to start with 40 and a 7.5% annual growth

➢ Staples – determine optimal package design characteristics from customer surveys

➢ Boeing – design aircraft parts such as fan blades

➢ Many retailers – better manage inventory and optimize display areas

➢ Automotive design

➢ Routing

Genetic Algorithm: Examples

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➢ Polynomial Neural Networks (PNN)

• Forecasting

• Adaptive control systems

• Nonlinear models

• Can include fuzzy logic and genetic algorithm

Example: PNN

65Adaptive control using PNN, Gomez Ramirez, 2000

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➢ Arrhythmia detection

Example: PNN

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➢ Developed by Ashok Goel’s team in Georgia Tech

• Based on the fact students ask the same questions over and over

• Initially based on keywords, it was not as successful

• Challenge: Natural language, different ways of asking questions

• Just answered in the forum if she was 97% certain she was correct

• Students did not know they were interacting with a virtual TA

• Based on IBM Watson

• Every iteration becomes better

Jill Watson: Teacher Assistant (TA)

https://www.ibm.com/watson/

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➢ Which answer was given by Jill?

Jill Watson: Teacher Assistant (TA)

68Watch Ashok Goel’s TED talk about Jill Watson

https://www.youtube.com/watch?v=WbCguICyfTA

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➢ What is the Turing test?

• It tests a machine’s ability to pass as a human

➢ Watson can trace its roots on Deep Blue, the computer who defeated Gary Kasparov

➢ IBM’s Watson in Jeopardy

➢ Watson “destroys” Humans in Jeopardy (Full)

➢ Full Q&A at Oxford Union with Laura, a humanoid

➢ Amazon just introduced an AI compiler (NNVM)

More on Watson and AI

https://www.youtube.com/watch?v=P18EdAKuC1U

https://www.youtube.com/watch?v=WFR3lOm_xhE

https://www.youtube.com/watch?v=rXVoRyIGGhU

https://aws.amazon.com/blogs/ai/introducing-nnvm-compiler-a-new-open-end-to-end-compiler-for-ai-frameworks/

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Agent-Based Technologies

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➢ Agent-based technology (software agent) – piece of software that acts on your behalf (or on behalf of another piece of software) performing tasks assigned to it

AGENT-BASED TECHNOLOGIES

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➢ Autonomous agent – can adapt and alter the manner in which it works

➢ Distributed agent – works on multiple distinct computer systems

➢ Mobile agent – can relocate itself onto different computer systems

➢ Intelligent agent – incorporates artificial intelligence capabilities such as reasoning and learning

➢ Multi-agent system – group of intelligent agents that can work independently and also together to perform a task

Types of Agent-Based Technologies

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➢ Information agents (buyer agents) – search for information and bring it back

• An agent on a Web site

➢ Monitoring-and-surveillance agents – constantly observe and report on some entity of interest, a network, or manufacturing equipment

➢ User agents – take action on your behalf

• Sorting your email

• Playing chess with you

Types of Intelligent Agents

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➢ Data-mining agents – operate in a data warehouse discovering information

• Important analytics tool for data warehouse data

• Can find hidden patterns in the data

• Can also classify and categorize

• May suggest unseen approaches

• Could make predictions based on information

▪ VW has an early-warning system about market conditions

Types of Intelligent Agents

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➢ Multi-Agent Systems – Interaction between multiple agents in an environment

➢ Biomimicry – learning from ecosystems and adapting their characteristics to human and organizational situations

➢ Used to:

1. Learn how people-based systems behave

2. Predict how they will behave under certain circumstances

3. Improve human systems to make them more efficient and effective

Multi-Agent Systems & Biomimicry

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➢ Swarm (collective) intelligence – the collective behavior of groups of simple agents that are capable of devising solutions to problems as they arise, eventually learning to coherent global patterns

• A subfield of biomimicry

Swarm Intelligence

Information Technology & Data Analytics

➢ Flexibility – adaptable to change

➢ Robustness – tasks are completed even if some individuals are removed

➢ Decentralization – each individual has a simple job to do

Characteristics of Swarm Intelligence

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➢ Metallica is AI friendly. Check out this song in Antwerp, or Budapest. • We can thank Verity Studios for this technology • Broadway shows, Drake and others are swarming as well! Check

this out!

➢ Intel’s drone shows are not less impressive!

➢ Why are these drones acting as agents?

➢ What other applications can you think of ?

Swarming with Metallica: Visual Application

https://www.youtube.com/watch?v=tHrBjsj_a8o

https://www.youtube.com/watch?v=7t1-k83Wpmg

https://veritystudios.com/

https://www.youtube.com/watch?v=caY33qt82gg

https://www.youtube.com/watch?v=aOd4-T_p5fA

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Examples of AI in the news

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➢ Facebook’s AI accidentally created its own language

➢ Amazon scraps secret AI recruiting tool that showed bias against women

➢ With No Laws To Guide It, Here's How Orlando Is Using Amazon's Facial Recognition

➢ If You Jaywalk in China, Facial Recognition Means You’ll Walk Away With A Fine

➢ There is a text generator that is so good, it is considered too dangerous to release

AI in the News

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➢ Facebook’s AI accidentally created its own language

• Project: Improve Chatbots

• Machine learning

• Dialog agents were left to negotiate freely

• They created a more efficient language to communicate

AI in the News

Bob: i can i i everything else . . . . . . . . . . . . . . Alice: balls have zero to me to me to me to me to me to me to me to me to Bob: you i everything else . . . . . . . . . . . . . . Alice: balls have a ball to me to me to me to me to me to me to me Bob: i i can i i i everything else . . . . . . . . . . . . . . Alice: balls have a ball to me to me to me to me to me to me to me Bob: i . . . . . . . . . . . . . . . . . . . Alice: balls have zero to me to me to me to me to me to me to me to me to Bob: you i i i i i everything else . . . . . . . . . . . . . . Alice: balls have 0 to me to me to me to me to me to me to me to me to Bob: you i i i everything else . . . . . . . . . . . . . . Alice: balls have zero to me to me to me to me to me to me to me to me to

https://thenextweb.com/artificial-intelligence/2017/06/19/facebooks-ai-accidentally-created-its-own-language/#.tnw_YGLIG5bd

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➢ Amazon scraps secret AI recruiting tool that showed bias against women

• Machine learning

• Rated candidates from 1 to 5 (yes, like product reviews)

• Problem: it was not rating candidates in a gender-neutral way

• Why? Because it was trained with resume patterns over a 10- year period, when most of candidates were men

• Scrapped as it could adapt other biases on its ratings.

AI in the News

https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

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➢ With No Laws To Guide It, Here's How Orlando Is Using Amazon's Facial Recognition

• Uses Amazon’s facial “Rekognition” to find persons of interest

• Currently a pilot

➢ If You Jaywalk in China, Facial Recognition Means You’ll Walk Away With A Fine

• Shenzhen surveilling and ticketing jaywalkers

• Part of a broader “social credit” proposal in China, planned for 2020.

AI in the News

https://www.buzzfeednews.com/article/daveyalba/amazon-facial-recognition-orlando-police-department

https://futurism.com/facial-recognition-china-social-credit

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➢ There is a text generator that is so good, it is considered too dangerous to release

• OpenAI was looking to created to predict the next word in a sample of 40Gb of Internet text.

• The end result was the system that adapts to style and content, allowing the user to generate realistic and coherent continuations about a topic of their choosing.

• Example: o Legolas and Gimli advanced on the orcs, raising their weapons

with a harrowing war cry. o The orcs' response was a deafening onslaught of claws, claws,

and claws; even Elrond was forced to retreat. "You are in good hands, dwarf," said Gimli, who had been among the first to charge at the orcs; it took only two words before their opponents were reduced to a blood-soaked quagmire, and the dwarf took his first kill of the night.

AI in the News

https://techcrunch.com/2019/02/17/openai-text-generator-dangerous/

https://openai.com/

Information Technology & Data Analytics

➢ Crazy-eyed robot wants a family – and to destroy all humans

• Sophia is an attempt to create a human-like robot

• She once said she wanted t destroy all humans

o… Really, she did.

oWas it a joke?

• Then she took that back

• What is the Uncanny Valley?

oDo you believe Sophia fits