Contents

Chapters in this book

1. Frontmatter I
2. Dedication V
3. Preface VII
4. Contents XIII
5. Part I Innovation
6. 1 Innovation Dynamics: AI’s Transformative Potential 3
7. 2 Innovation Culture: Igniting AI Creativity in Organizations 14
8. Part II Computation
9. 3 Algorithms: The Logic of Computing 27
10. 4 Architecture: How Computers Work 35
11. Part III Machine Learning
12. 5 Machine Learning: Learning from Data 47
13. 6 Classification: Predicting Categories (Case Study) 58
14. 7 Regression: Predicting Continuous Values (Case Study) 68
15. Part IV Deep Learning
16. 8 Deep Learning: The Big Picture 83
17. 9 Deep Learning: Under the Hood 92
18. Part V Generative AI
19. 10 Foundation Models: Large Language Model Basics 107
20. 11 Foundation Models: How Large Language Models Become Smart 121
21. 12 Expert Models: Grounding AI in Trusted Knowledge 133
22. 13 Agentic AI: From Knowledge to Action 149
23. Part VI Risk
24. 14 System Failures: Why and How AI Makes Mistakes 161
25. 15 Catastrophic Accidents: How to Identify and Address Major Failures 169
26. 16 Risk Across the Action Boundary: From Passive to Agentic Systems 180
27. Bibliography
28. List of Figures 191
29. List of Tables 197
30. Index 199