Succeeding with AI

Author Veljko Krunic

Release Date: 2020/03/01

Topic:

26
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Table of Contents

1. Copyright
2. Brief Table of Contents
3. Table of Contents
4. Preface
5. Acknowledgments
6. About This Book
   1. Who should read this book
   2. How this book is organized
   3. liveBook discussion forum
7. About the Author
8. About the Cover Illustration
9. Chapter 1. Introduction
   1. 1.1. Whom is this book for?
   2. 1.2. AI and the Age of Implementation
   3. 1.3. How do you make money with AI?
   4. 1.4. What matters for your project to succeed?
   5. 1.5. Machine learning from 10,000 feet
   6. 1.6. Start by understanding the possible business actions
   7. 1.7. Don’t fish for “something in the data”
   8. 1.8. AI finds correlations, not causes!
   9. 1.9. Business results must be measurable!
   10. 1.10. What is CLUE?
   11. 1.11. Overview of how to select and run AI projects
   12. 1.12. Exercises
   13. Summary
10. Chapter 2. How to use AI in your business
    1. 2.1. What do you need to know about AI?
    2. 2.2. How is AI used?
    3. 2.3. What’s new with AI?
    4. 2.4. Making money with AI
    5. 2.5. Finding domain actions
    6. 2.6. Overview of AI capabilities
    7. 2.7. Introducing unicorns
    8. 2.8. Exercises
    9. Summary
11. Chapter 3. Choosing your first AI project
    1. 3.1. Choosing the right projects for a young AI team
    2. 3.2. Prioritizing AI projects
    3. 3.3. Your first project and first research question
    4. 3.4. Pitfalls to avoid
    5. 3.5. Exercises
    6. Summary
12. Chapter 4. Linking business and technology
    1. 4.1. A project can’t be stopped midair
    2. 4.2. Linking business problems and research questions
    3. 4.3. Measuring progress on AI projects
    4. 4.4. Linking technical progress with a business metric
    5. 4.5. Organizational considerations
    6. 4.6. Exercises
    7. Summary
13. Chapter 5. What is an ML pipeline, and how does it affect an AI project?
    1. 5.1. How is an AI project different?
    2. 5.2. Why we need to analyze the ML pipeline
    3. 5.3. What’s the role of AI methods?
    4. 5.4. Balancing data, AI methods, and infrastructure
    5. 5.5. Exercises
    6. Summary
14. Chapter 6. Analyzing an ML pipeline
    1. 6.1. Why you should care about analyzing your ML pipeline
    2. 6.2. Economizing resources: The E part of CLUE
    3. 6.3. MinMax analysis: Do you have the right ML pipeline?
    4. 6.4. How to interpret MinMax analysis results
    5. 6.5. How to perform an analysis of the ML pipeline
    6. 6.6. FAQs about MinMax analysis
    7. 6.7. Exercises
    8. Summary
15. Chapter 7. Guiding an AI project to success
    1. 7.1. Improving your ML pipeline with sensitivity analysis
    2. 7.2. We’ve completed CLUE
    3. 7.3. Advanced methods for sensitivity analysis
    4. 7.4. How your AI project evolves through time
    5. 7.5. Concluding your AI project
    6. 7.6. Exercises
    7. Summary
16. Chapter 8. AI trends that may affect you
    1. 8.1. What is AI?
    2. 8.2. AI in physical systems
    3. 8.3. AI doesn’t learn causality, only correlations
    4. 8.4. Not all data is created equal
    5. 8.5. How are AI errors different from human mistakes?
    6. 8.6. AutoML is approaching
    7. 8.7. What you’ve learned isn’t limited to AI
    8. 8.8. Guiding AI to business results
    9. 8.9. Exercises
    10. Summary
17. Appendix A. Glossary of terms
18. Appendix B. Exercise solutions
    1. B.1. Answers to chapter 1 exercises
    2. B.2. Answers to chapter 2 exercises
    3. B.3. Answers to chapter 3 exercises
    4. B.4. Answers to chapter 4 exercises
    5. B.5. Answers to chapter 5 exercises
    6. B.6. Answers to chapter 6 exercises
    7. B.7. Answers to chapter 7 exercises
    8. B.8. Answers to chapter 8 exercises
19. Appendix C. Bibliography
20. Data + AI + CLUE = Profit
21. Index
22. List of Figures
23. List of Tables