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Spis treści

AI Value Creators eBook -- spis treści

• Preface
  • GenAI Is a Lift, Shift, Rift, or Cliff Moment
  • As You Journey into the Book
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • The Collective Thank-Yous
  • Our Personal Dedications
• 1. +AI to AI+: Generative AI and the Netscape Moment
  • What Is a Netscape Moment?
  • AI and the Magical Moment
    • But...AI Is Not Magic
  • Moving Your Business from +AI to AI+
  • Before You Do Anything, Change Your Mental Model from +AI to AI+
    • The AI Ladder, Rebooted for GenAI
  • Before You Start Your Journey, Classify the Budget and Identify How AI Is Going to Help
    • Dimension One: Spend Money to Save Money, or Spend Money to Make Money? How Will AI Help Your Business?
    • Dimension Two: Categorize How the AI Helps Your Business
    • Use an Acumen Curve to Visualize How AI Helps Your Business
    • Where to Start? Heres Our Helpful Advice
  • Become a Shifty Business: Shift Left, and Then, You Can Shift Right!
    • Every Day, We Walk by Problems That Can Be Solved or Made Better with Technology
      • Personal mobility: A fundamental human right
      • A diabetic foot ulcer and an episode of care
      • And so many more
      • Now, shift right
  • Tips for Harnessing Foundation Models and GenAI for Your Business
    • Tip 1: Act with Urgency
    • Tip 2: Be an AI Value Creator, Not Just an Occasional AI User
    • Tip 3: One Model Will Not Rule Them All, So Make a Bet on Community
    • Tip 4: Run Everywhere, Efficiently
    • Tip 5: Be Responsible Because Trust Is the Ultimate License to Operate
  • And with That, Lets Focus on the AI Part
• 2. Oh, to Be an AI Value Creator
  • AI Through the Years: The AI Time Lapse Section
    • A Quick Bit on Foundation Models
    • Going a Little Deeper: The Evolution of Large Language Models and Comparing Supervised Learning with Self-Supervised Learning
  • AI Value Creation Should Be Your Destination
    • How Do You Consume AI: Be Ye a Value Creator or a Value User?
      • AI User: Shake (embed) and bake (into the product) the AI
      • AI User: Dont fall when you make the service call (the even bigger but)
      • Fire starter: Becoming an AI Value Creator
      • The path forward: How to create value with AI
      • Look before you leap
  • Planning Your AI Future: A Future with Many GenAI Models
    • Its Time to Demystify and Apply AI
      • The componentry
        • A unified, modern data fabric with an accompanying data-as-a-product point of view
        • A development environment and an engine
        • The modality of human features
        • AI management and exploitation
        • Agents and assistants for the masses
      • The process: Cake ingredients without a recipe do not make a cake
        • Step 1: Identify the right business opportunities for AI
        • Step 2: Prepare the organization for AI
        • Step 3: Select technology and partners
        • Accept failures but do so in a safe manner
  • The Future of AI
  • Lets Get into It
• 3. Equations for AI Persuasion
  • Some Things Are Timeless
    • Tension Has Always Existed with TechnologyAlways
    • No Calculators Needed! Our Three Persuasion Equations
      • Populations are declining
      • Productivity varies around the globe
      • Debt growth with expense and access headwinds
      • Uneven GDP growth
    • Equation 1: How to Grow GDP
    • Equation 2: What Makes for AI Success?
      • Bake the layer cake: A platform that helps you master the AI success equation
        • The base: Hybrid cloud and AI tools
        • Data services need to be at your service
        • The AI and data platform: The heart of the cake
        • Im more than OK with an SDK
        • Agents and assistants empower AI for the many
    • Equation 3: Find Your BalanceNavigate the Paradox
      • Leadership is stewardship: Guiding with care
      • Drills and skills help you master the craft
      • The different facets of being open
  • One Last Piece of Advice: See AI as a Value Generator, Not a Cost Center
  • Wrapping Up
• 4. The Use Case Chapter
  • The Use Case Value Creation Curve
  • Going Horizontal Gets You the Most Vertical
    • Experimentation
    • Putting Your Data to Work
    • IT Automation
    • CodeThe Language of Computers
    • Digital Labor and AI Assistants
    • Agents
    • The Business Lens: Use CasesHorizontally Speaking
    • The Bonus (Horizontal) Use CaseSynthetic Data
  • A Smattering of Use CasesVertically Speaking
    • Agriculture
    • Accounting
    • Education
    • Healthcare
    • Insurance
    • Legal
    • Manufacturing and Production
    • Pharma
  • Endless Possibilities: More Industries Where GenAI Shines
  • The Building Blocks of AI
• 5. Live, Die, Buy, or TryMuch Will Be Decided by AI
  • LLMsThe Stuff People Forget to Tell You
    • The Knowledge Cut-Off Date
    • LLMs Can Be Masters of Making It Up as They Go
    • Footprints in the Carbon: The Climate Cost of Your AI BFF
    • Copyright and Lawsuits
    • What About Digital Essence?
    • Your Expanding Surface Area of Attack
      • Data poisoning
      • Prompt injection attacks
      • Social engineering and deepfake attacks
    • Data Privacy
    • Steal Now, Crack Later
  • Good Actor Levers for All Things AI
    • FairnessPlaying Fair in the Age of AI
    • Bias Here, Bias There, Data Bias Is Everywhere
    • RobustnessEnsuring Artificial Intelligence Is Unbreakable Intelligence
    • ExplainabilityExplain the Almost Unexplainable
    • LineageTracing the Trail: Let Good Data Prevail
  • RegulationsThe Section That Wasnt Supposed to Be
    • What to RegulateOur Point of View
    • Managing the AI Lifecycle
      • What lies beneath
      • An example of an end-to-end governed process
  • Wrapping It Up
• 6. Skills That Thrill
  • Let the Skilling Begin
  • The Path to AI+ Requires Scaling Skills Across a Broad Spectrum of Roles
  • AIJob Destroyer or Job Creator?
    • Youre Only Going to Get Checkmated if You Dont Up Your Skills
    • Democratized Technology: The Job Creator
  • Levers of Clever: Unlocking a Skills Program That Lasts Forever
    • Lever 1: Start at the BeginningHire Employees Who Want to Know the Why
      • Be a Mozart of learning
    • Lever 2: Recruit Digitally Minded Talent
    • Lever 3: Take CountInventory Your Skills
      • Bevel your levels
    • Lever 4: Plan for EveryoneA Plan Without Action Is a Speech
    • Lever 5: Embrace the Learning (and Forgetting) Curves
    • Lever 6: Combine Instruction + Imitation + Collaboration
      • Living for Gagas Applausedo it for real or dont do it at all
      • Build the sandboxencourage the messy
      • Show off (and celebrate) those digital credentials!
    • Lever 7: Culture MattersBe a Skills Verb, Not a Noun
    • Lever 8: Set the Organizational Tone for AI
  • Case Study: IBMs Skills Challengethe CEO Asked; We All Responded
  • The Final Word
• 7. Where This Technology Is HeadedOne Model Will Not Rule Them All!
  • The Bigger the Better, Right? Perhaps at the Start, But That Was a Long Time Ago
  • The Rise of the Small Language Model
    • Data Curation Results in AI Salvation
      • Data quantity
      • Data quality
      • Domain specialization
    • Think About This When It Comes to Data Curation
    • Model DistillationUsing AI to Improve AI
    • Think About This When It Comes to Model Distillation
  • Where Are We Going Next? Small Language Models...Assemble!
    • Model Routing
    • Think About This When It Comes to Model Routing
    • Mixture of Experts (MoE) Architecture
    • Think About This When It Comes to MoEs
  • Agentic Systems
    • Whats Your Reaction to This Agent in Action?
    • A Little More on Agents
    • How Agents Are Built
      • ReAct (Reasoning and Action)
      • ReWOO (Reasoning WithOut Observation)
    • Risks and Limitations of Agentic Systems
    • Three Tips to Get You Started: Our Agentic Best Practices
      • 1. Activity logs
      • 2. Interruption and runtime observability
      • 3. Human supervision
    • Think About This When It Comes to AI Agents
  • Wrapping It Up
• 8. Using Your Data as a Differentiator
  • Customizing Open Source for the Enterprise: A New Way of Looking at Enterprise Data
    • The Original Eras Tour: Looking Back a Few Decades on Data Representations
      • Up to the 1980s: Expert systems
      • 1980s to ~2010: Machine learning
      • 2010 to ~2017: Deep learning
      • Today: Foundation models (aka LLMs)
  • Stand Up and Represent!...Your Data
    • Step 1: It All Starts with Trust
      • The IBM commercialin Granite you should trust
    • Step 2: Representing your Enterprise Data within an LLM
      • Introducing InstructLab
      • Dipping your toe into the InstructLab pool
      • Can you smell whats cooking? Skill and knowledge recipes
      • Harnessing the power of the community
      • A day in the life of an InstructLab contributor
    • Step 3: The Grand Finale: Deployment and Experimentation
  • The Future Is Open, Collaborative, and Customizable
• 9. Generative ComputingA New Style of Computing
  • The Building Blocks of Computing
    • TransformersMore Than Meets the AI
  • Not Back to the Future; Back to Computer Science
    • Doors Wide OpenReimagining the Possible
  • How Models Are Built in Generative Computing
    • Libraries for Adding Capabilities to a Generative Computing System
    • The Quick Compare SummaryHow You Use LLMs Today Versus Generative Computing
    • A Generative Computing RuntimeWhat Can We Program It to Do?
    • OpenAIs StrawberryA Berry Sweet Innovation
  • From Generative Computing to a Generative ComputerWhat Does All of This Mean for Hardware?
    • Experimenting with the Acceleration of AI at the IBM NorthPole
  • The Final Prompt: Wrapping It All Up
• Index