AI for People and Business. A Framework for Better Human Experiences and Business Success

Spis treści książki

• Preface
  • The Motivation Behind This Framework and Book
  • Navigating This Book
  • OReilly 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
      • Maslows 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
  • SpatialTemporal 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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