The AI Ladder. Accelerate Your Journey to AI

Spis treści książki

• Preface
  • Who This Book Is For
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments from the Authors
  • Acknowledgments from Paul Zikopoulos
• 1. What in the AI? How Did We Get Here?
  • Collecting Data in Real Time, but Understanding It in Stale Time
  • The Modality of Everything and the Data Collection Curve
  • Even Steeper: The Future of the Data Collection Curve
  • Where We Are NowHaystacks, Needles, and More Data
  • How to Displace Todays Disruptors
  • Lets Get Ready for a Climb!
• 2. The Journey to AI
  • What Is Artificial Intelligence, Anyway?
    • Types of AI
    • Data
    • Models
  • Where AI Has Been
  • What Does AI Mean for Business?
  • The Journey to AI
  • All Radically New Technologies Face Resistance
  • Where Are We Now? And Where Are We Going?
  • Moving Forward
• 3. How to Overcome AI Failures and Challenges
  • AIs Emergence in Business Today
    • Data
    • Computing Power
    • Investment
  • Early Examples of AI Success
    • Example: Vodafones TOBi Transforms the Customer Experience
    • Example: How a French Bank Built on Its Strength of Quality Customer Service
  • Early AI Failures
  • AI Challenges: Data, Talent, Trust
    • AI Challenge: Data
    • AI Challenge: Talent
      • High demand, low supply for potential employees
      • Culture inhibitors
      • Siloed people and departments
    • AI Challenge: Trust
      • Fairness
      • Explainability
      • Robustness
      • Transparency and accountability
      • Value alignment
  • Overcoming Challenges with Advanced Research and Products
  • Overcoming Challenges with the Right Partner
• 4. The AI Ladder: A Path to Organizational Transformation
  • Suitability of AI
  • Determining the Right Business Problems to Solve with AI
  • Building a Data Team
  • Putting the Budget in Place
  • Developing an Approach
  • There Is No AI Without IA
  • The AI Ladder
    • Collect
    • Organize
    • Analyze
    • Infuse
  • Simplify, Automate, and Transform
• 5. Modernize Your Information Architecture
  • A Modern Infrastructure for AI
    • All Parts Are Visible
    • Legacy Systems Are Made Accessible or Eliminated
      • Example: Network Rail uses AI to modernize its infrastructure
    • All Parts of the System Are Continuously Monitored
    • Inefficiencies Are Identified and Removed
    • New Architectures for IT
  • Data: The Fuel; Cloud: The Means
    • To the Cloud, and Beyond: Cloud as Capability
    • Fuel for the Fire
  • From Databases to Data Warehouses, Data Marts, and Data Lakes
    • Example: Wireless Carrier Architects a Solution Using Both a Data Lake and a Data Warehouse
  • Data Virtualization
    • Unifying Access to Data Through Big SQL
    • Object Storage as the Preferred Fabric
    • Open Data Stores and Open Data Formats
    • Next-Generation Databases
  • The Power of an AI Database
  • Streaming Data
  • Get the Right Tools
  • The Importance of Open Source Technologies
    • Community Thinking and Culture
    • High Code and Component Quality
  • Real Examples of Modernizing IT Infrastructure
    • Example: Siemens Looks to the Cloud to Unify Its Data Processes
    • Example: Fannie Mae Transforms with a Governed and Centralized Data Environment
  • Dont Neglect the Foundation!
• 6. Collect Your Data
  • What Needs to Happen on the Collect Rung
    • Example: EMC Develops a Data Collection Strategy
  • Start with a Data Census: Learn Whats Out There
  • Understand Data in a Business Context, and Partner with SMEs
  • Getting Beyond Transactional Data
  • The Challenges of Collecting New Sources of High-Volume Unstructured Data
  • Organizational Aspects of Data Access
    • Example: Procter & Gamble Avoid Data Silos Using a Central Data Warehouse
    • Example: eBay Eliminates Data Silos by Publishing Business Processes as APIs
  • Ownership, Stewardship, Regulatory Compliance, and Discipline
    • Example: Owens-Illinois
  • Collecting Data: You Can Win This Battle!
• 7. Organize Your Data
  • Poor Data Leads to Poor AI
  • Regulation Demands Quality Data
  • What Needs to Happen on the Organize Rung
  • Cleaning Data
  • Documenting and Cataloging Data
  • Understanding Data: The Seller Gong Show
  • Metadata for Models
  • Maintaining the Catalog
  • Governing Data
    • Enterprise Performance Management
    • Example: ANZ Banking Group Embeds Sound Data Management and Governance Policies
  • DataOps
  • Now That Your Data Is Trustworthy, on to Analysis!
• 8. Analyze Your Data
  • Why Organizations Need an End-to-End AI Lifecycle
  • Build
    • Example: Using Machine Learning, an Insurer Cuts Costs and Boosts Productivity
  • Run
  • Manage
    • Aligning Model Output with Business Metrics
    • Learning, Iterating, Learning
    • Example: Risk Management Company Gets Creative to Offset the Expense of Training Models
  • Automating the AI Lifecycle
    • AutoAI
      • Example: Wunderman Thompson delivers new prospective customers through AutoAI
      • Example: Bank uses humans, machine learning, and deep learning to measure model risk
    • NeuNetS
  • Incorporating AI into DevOps Processes
  • Emphasizing Trust and Transparency
    • Example: By Shining Light on Data Attributes, a Banks AI System Demonstrates Integrity, Fairness, Explainability, and Resiliency
    • Example: Avoiding the Black Box Dilemma
    • Avoiding the Piecemeal Approach
    • Example: SaaS Company Gleans New Insights by Applying AI to Historical Data
  • Ready to Infuse...
• 9. Infuse AI Throughout the Business
  • Customer Service
  • Financial Operations
  • Risk and Compliance
  • IT Operations
  • Business Operations
  • Themes Across All Intelligent Workflows
  • Building the Next-Generation C-Suite
• 10. Tips and Best Practices on How to Get Started
  • Manage Organization-Wide Change
    • Change in Daily Tasks
    • Change in Overall Business Processes
      • Example: Pharmacy uses automation to modernize laborious and error-prone processes
    • Change in Thinking About Data
  • Make Data a Team Sport (And Some Cool History About Car Racing)
    • Subject Matter Experts
    • Data Scientists
    • Data Operations (DataOps) Specialists
    • Data Engineers
    • Training for Career Development
  • Embrace AI Centers of Excellence
    • Example: Honda Sets Up Knowledge Hubs to Build Minimum Viable Products, Organize Training, Share Data
  • Build Ethics Into Your Process
    • Privacy
    • Safety
    • Fairness
    • Building Trust in AI
  • Choose Projects Selectively, and Embrace Failure
    • Example: Insurer Tracks Metrics to Communicate Success of Its Model
  • Beware of False Negatives
• 11. The Future of AI
  • AI Themes to Take Us Through the Next Five Years
    • Theme #1: AI Is Not a Fad
    • Theme #2: Data-Generating Sensors Will Proliferate
    • Theme #3: Data Will Be Processed at the Edge
    • Theme #4: AI Will Spread Everywhere
    • Theme #5: AI Will Disappear into the Background and Become Boring
  • Future AI Use Cases for Business
    • Cybersecurity
    • Autonomous Driving, Autonomous Everything
    • Conversational Digital Agents and Personal Assistants
    • Real Estate
    • Retail
    • Insurance
    • Customer Service
  • The Future of Work in an AI-Driven World
  • A Deeper Dive into AI and Edge Computing
    • Using the Edge and AI for Good
  • Conclusion
• Index

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