AI for People and Business by Castrounis, Alex -- Read -- Imperial Library of Trantor

Index

Preface

The Motivation Behind This Framework and Book Navigating This Book O’Reilly Online Learning How to Contact Us Acknowledgments

I. The AI for People and Business Framework 1. Success with AI

Racing to Business Success Why Do AI Initiatives Fail? Why Do AI Initiatives Succeed? Harnessing the Power of AI for the Win

2. An Introduction to the AI for People and Business Framework

A General Framework for Innovation The AIPB Benefits Pseudocomponent Existing Frameworks and the Missing Pieces of the Puzzle AIPB Benefits

Why Focused People and Business Focused Unified and Holistic Focused Explainable Focused Science Focused

Summary

3. AIPB Core Components

An Agile Analogy Experts Component AIPB Process Categories and Recommended Methods Assessment Component

AI Readiness and Maturity

Methodology Component

Assess Vision Strategy Deliver Optimize

The Flipped Classroom Summary

4. AI and Machine Learning: A Nontechnical Overview

What Is Data Science, and What Does a Data Scientist Do? Machine Learning Definition and Key Characteristics Ways Machines Learn AI Definition and Concepts AI Types Learning Like Humans AGI, Killer Robots, and the One-Trick Pony The Data Powering AI

Big Data Data Structure and Format For AI Applications Data Storage and Sourcing Specific Data Sources Data Readiness and Quality (the “Right” Data)

Adequate Data Amount Adequate Data Depth Well-Balanced Data Highly Representative and Unbiased Data Complete Data Clean Data

A Note on Cause and Effect Summary

5. Real-World Applications and Opportunities

AI Opportunities How Can I Apply AI to Real-World Applications? Real-World Applications and Examples

Predictive Analytics

Regression Classification

Personalization and Recommender Systems Computer Vision Pattern Recognition Clustering and Anomaly Detection Natural Language

NLP NLG NLU

Time-Series and Sequence-Based Data Search, Information Extraction, Ranking, and Scoring Reinforcement Learning Hybrid, Automation, and Miscellaneous

Summary

II. Developing an AI Vision 6. The Importance of Why

Start with Why Product Leadership and Perspective Leadership and Generating a Shared Vision and Understanding Summary

7. Defining Goals for People and Business

Defining Stakeholders and Introducing Their Goals Goals by Stakeholder

Goals and the Purpose of AI for Business

Deep Actionable Insights Augment Human Intelligence Create New and Innovative Business Models, Products, and Services Capture new markets or expand TAMs Influence new and optimized processes Drive differentiation and competitive advantage Transform business and disrupt industries

Goals and the Purpose of AI for People

Better health and health-related outcomes Better personal safety and security Better financial performance, savings, and insights Better UX, convenience, and delight Better and easier planning and decisions Better productivity, efficiency, and enjoyment Better learning and entertainment

Summary

8. What Makes a Product Great

Importance versus Satisfaction The Four Ingredients of a Great Product

Products That Just Work Ability to Meet Human Needs, Wants, and Likes

Maslow’s Hierarchy of Needs The difference between needs, wants, and likes Human-centered over business-centered products and features

Design and Usability Delight and Stickiness

Netflix and the Focus on What Matters Most Lean and Agile Product Development Summary

9. AI for Better Human Experiences

Experience Defined The Impact of AI on Human Experiences

Better health and health-related outcomes

Physical health Mental health

Better personal safety and security Better financial performance, savings, and insights Better UX, convenience, and delight Better and easier planning and decisions Better productivity, efficiency, and enjoyment Better learning and entertainment

Experience Interfaces The Experience Economy Design Thinking Summary

10. An AI Vision Example

Spatial–Temporal Sensing and Perception AI-Driven Taste Our AIPB Vision Statement

III. Developing an AI Strategy 11. Scientific Innovation for AI Success

AI as Science The TCPR Model A TCPR Model Analogy

Time and Cost Performance Requirements

A Data Dependency Analogy Summary

12. AI Readiness and Maturity

AI Readiness

Organizational

Organizational structure, leadership, and talent Vision and strategy Adoption and alignment Sponsorship and support

Technological

Infrastructure and technologies Support and maintain Data readiness and quality (the “right” data)

Financial

Budgeting Competing investments and prioritization

Cultural

Scientific innovation and disruption Gut-to-data driven Action ready Data democratization

AI Maturity Summary

13. AI Key Considerations

AI Hype versus Reality Testing Risky Assumptions Assess Technical Feasibility Acquire, Retain, and Train Talent Build Versus Buy Mitigate Liabilities Mitigating Bias and Prioritizing Inclusion Managing Employee Expectations Managing Customer Expectations Quality Assurance Measure Success Stay Current AI in Production Summary

14. An AI Strategy Example

Podcast Example Introduction AIPB Strategy Phase Recap Creating An AIPB Solution Strategy Creating an AIPB Prioritized Roadmap

Aligned Goals, Initiatives, Themes, and Features

IV. Final Thoughts 15. The Impact of AI on Jobs

AI, Job Replacement, and the Skills Gap The Skills Gap and New Job Roles The Skills of Tomorrow The Future of Automation, Jobs, and the Economy Summary

16. The Future of AI

AI and Executive Leadership What to Expect and Watch For

Increased AI Understanding, Adoption, and Proliferation Advancements in Research, Software, and Hardware

Research Software Hardware

Advancements in Computing Architecture Technology Convergence, Integration, and Speech Dominance Societal Impact AGI, Superintelligence, and the Technological Singularity The AI Effect

Summary

A. AI and Machine Learning Algorithms

Parametric versus Nonparametric Machine Learning How Machine Learning Models Are Learned Biological Neural Networks Overview An Introduction to ANNs An Introduction to Deep Learning Deep Learning Applications Summary

B. The AI Process

The GABDO Model Goals

Identify Goals Identify Opportunities Create Hypothesis

Example

Acquire

Identify Data Acquire Data Prepare Data

Example continued

Build

Explore Select Train, Validate, Test Improve

Example continued

Deliver

Present Insights Take Action Make Decisions Deploy Solutions

Example continued

Optimize

Monitor Analyze Improve

Example continued

Summary

C. AI in Production

Production versus Development Environments Local versus Remote Development Production Scalability Learning and Solution Maintenance

Bibliography Index

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