AI for People and Business

AI for People and Business

by Alex Castrounis
Released July 2019
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781492036579

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Book description

If you're an executive, manager, or anyone interested in leveraging AI within your organization, this is your guide. You'll understand exactly what AI is, learn how to identify AI opportunities, and develop and execute a successful AI vision and strategy. Alex Castrounis,founder and CEO of Why of AI, Northwestern University Adjunct, advisor, and former IndyCar engineer and data scientist, examines the value of AI and shows you how to develop an AI vision and strategy that benefits both people and business.

AI is exciting, powerful, and game changing--but too many AI initiatives end in failure. With this book, you'll explore the risks, considerations, trade-offs, and constraints for pursuing an AI initiative. You'll learn how to create better human experiences and greater business success through winning AI solutions and human-centered products.

  • Use the book's AIPB Framework to conduct end-to-end, goal-driven innovation and value creation with AI
  • Define a goal-aligned AI vision and strategy for stakeholders, including businesses, customers, and users
  • Leverage AI successfully by focusing on concepts such as scientific innovation and AI readiness and maturity
  • Understand the importance of executive leadership for pursuing AI initiatives

"A must read for business executives and managers interested in learning about AI and unlocking its benefits. Alex Castrounis has simplified complex topics so that anyone can begin to leverage AI within their organization." - Dan Park, GM & Director, Uber

"Alex Castrounis has been at the forefront of helping organizations understand the promise of AI and leverage its benefits, while avoiding the many pitfalls that can derail success. In this essential book, he shares his expertise with the rest of us." - Dean Wampler, Ph.D., VP, Fast Data Engineering at Lightbend

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Table of contents

  1. Preface
    1. The Motivation Behind This Framework and Book
    2. Navigating This Book
    3. O’Reilly Online Learning
    4. How to Contact Us
    5. Acknowledgments
  2. I. The AI for People and Business Framework
  3. 1. Success with AI
    1. Racing to Business Success
    2. Why Do AI Initiatives Fail?
    3. Why Do AI Initiatives Succeed?
    4. Harnessing the Power of AI for the Win
  4. 2. An Introduction to the AI for People and Business Framework
    1. A General Framework for Innovation
    2. The AIPB Benefits Pseudocomponent
    3. Existing Frameworks and the Missing Pieces of the Puzzle
    4. AIPB Benefits
      1. Why Focused
      2. People and Business Focused
      3. Unified and Holistic Focused
      4. Explainable Focused
      5. Science Focused
    5. Summary
  5. 3. AIPB Core Components
    1. An Agile Analogy
    2. Experts Component
    3. AIPB Process Categories and Recommended Methods
    4. Assessment Component
      1. AI Readiness and Maturity
    5. Methodology Component
      1. Assess
      2. Vision
      3. Strategy
      4. Deliver
      5. Optimize
    6. The Flipped Classroom
    7. Summary
  6. 4. AI and Machine Learning: A Nontechnical Overview
    1. What Is Data Science, and What Does a Data Scientist Do?
    2. Machine Learning Definition and Key Characteristics
    3. Ways Machines Learn
    4. AI Definition and Concepts
    5. AI Types
    6. Learning Like Humans
    7. AGI, Killer Robots, and the One-Trick Pony
    8. The Data Powering AI
      1. Big Data
      2. Data Structure and Format For AI Applications
      3. Data Storage and Sourcing
      4. Specific Data Sources
      5. Data Readiness and Quality (the “Right” Data)
    9. A Note on Cause and Effect
    10. Summary
  7. 5. Real-World Applications and Opportunities
    1. AI Opportunities
    2. How Can I Apply AI to Real-World Applications?
    3. Real-World Applications and Examples
      1. Predictive Analytics
      2. Personalization and Recommender Systems
      3. Computer Vision
      4. Pattern Recognition
      5. Clustering and Anomaly Detection
      6. Natural Language
      7. Time-Series and Sequence-Based Data
      8. Search, Information Extraction, Ranking, and Scoring
      9. Reinforcement Learning
      10. Hybrid, Automation, and Miscellaneous
    4. Summary
  8. II. Developing an AI Vision
  9. 6. The Importance of Why
    1. Start with Why
    2. Product Leadership and Perspective
    3. Leadership and Generating a Shared Vision and Understanding
    4. Summary
  10. 7. Defining Goals for People and Business
    1. Defining Stakeholders and Introducing Their Goals
    2. Goals by Stakeholder
      1. Goals and the Purpose of AI for Business
      2. Goals and the Purpose of AI for People
    3. Summary
  11. 8. What Makes a Product Great
    1. Importance versus Satisfaction
    2. The Four Ingredients of a Great Product
      1. Products That Just Work
      2. Ability to Meet Human Needs, Wants, and Likes
      3. Design and Usability
      4. Delight and Stickiness
    3. Netflix and the Focus on What Matters Most
    4. Lean and Agile Product Development
    5. Summary
  12. 9. AI for Better Human Experiences
    1. Experience Defined
    2. The Impact of AI on Human Experiences
    3. Experience Interfaces
    4. The Experience Economy
    5. Design Thinking
    6. Summary
  13. 10. An AI Vision Example
    1. Spatial–Temporal Sensing and Perception
    2. AI-Driven Taste
    3. Our AIPB Vision Statement
  14. III. Developing an AI Strategy
  15. 11. Scientific Innovation for AI Success
    1. AI as Science
    2. The TCPR Model
    3. A TCPR Model Analogy
      1. Time and Cost
      2. Performance
      3. Requirements
    4. A Data Dependency Analogy
    5. Summary
  16. 12. AI Readiness and Maturity
    1. AI Readiness
      1. Organizational
      2. Technological
      3. Financial
      4. Cultural
    2. AI Maturity
    3. Summary
  17. 13. AI Key Considerations
    1. AI Hype versus Reality
    2. Testing Risky Assumptions
    3. Assess Technical Feasibility
    4. Acquire, Retain, and Train Talent
    5. Build Versus Buy
    6. Mitigate Liabilities
    7. Mitigating Bias and Prioritizing Inclusion
    8. Managing Employee Expectations
    9. Managing Customer Expectations
    10. Quality Assurance
    11. Measure Success
    12. Stay Current
    13. AI in Production
    14. Summary
  18. 14. An AI Strategy Example
    1. Podcast Example Introduction
    2. AIPB Strategy Phase Recap
    3. Creating An AIPB Solution Strategy
    4. Creating an AIPB Prioritized Roadmap
      1. Aligned Goals, Initiatives, Themes, and Features
  19. IV. Final Thoughts
  20. 15. The Impact of AI on Jobs
    1. AI, Job Replacement, and the Skills Gap
    2. The Skills Gap and New Job Roles
    3. The Skills of Tomorrow
    4. The Future of Automation, Jobs, and the Economy
    5. Summary
  21. 16. The Future of AI
    1. AI and Executive Leadership
    2. What to Expect and Watch For
      1. Increased AI Understanding, Adoption, and Proliferation
      2. Advancements in Research, Software, and Hardware
      3. Advancements in Computing Architecture
      4. Technology Convergence, Integration, and Speech Dominance
      5. Societal Impact
      6. AGI, Superintelligence, and the Technological Singularity
      7. The AI Effect
    3. Summary
  22. A. AI and Machine Learning Algorithms
    1. Parametric versus Nonparametric Machine Learning
    2. How Machine Learning Models Are Learned
    3. Biological Neural Networks Overview
    4. An Introduction to ANNs
    5. An Introduction to Deep Learning
    6. Deep Learning Applications
    7. Summary
  23. B. The AI Process
    1. The GABDO Model
    2. Goals
      1. Identify Goals
      2. Identify Opportunities
      3. Create Hypothesis
    3. Acquire
      1. Identify Data
      2. Acquire Data
      3. Prepare Data
    4. Build
      1. Explore
      2. Select
      3. Train, Validate, Test
      4. Improve
    5. Deliver
      1. Present Insights
      2. Take Action
      3. Make Decisions
      4. Deploy Solutions
    6. Optimize
      1. Monitor
      2. Analyze
      3. Improve
    7. Summary
  24. C. AI in Production
    1. Production versus Development Environments
    2. Local versus Remote Development
    3. Production Scalability
    4. Learning and Solution Maintenance
  25. Bibliography
  26. Index
  27. About the Author

Product information

  • Title: AI for People and Business
  • Author(s): Alex Castrounis
  • Release date: July 2019
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492036579