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

AI for People and Business. A Framework for Better Human Experiences and Business Success
ebook
Autor: Alex Castrounis
ISBN: 9781492036524
stron: 316, Format: ebook
Data wydania: 2019-07-05
Księgarnia: Helion

Cena książki: 143,65 zł (poprzednio: 167,03 zł)
Oszczędzasz: 14% (-23,38 zł)

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

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, business consultant and former IndyCar engineer and race strategist, 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

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

 

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

Spis treści

AI for People and Business. A Framework for Better Human Experiences and Business Success eBook -- spis treści

  • 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

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

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