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The AI Value Playbook

You're reading from   The AI Value Playbook How to make AI work in the real world

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835461754
Length 440 pages
Edition 1st Edition
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Author (1):
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Lisa Weaver-Lambert Lisa Weaver-Lambert
Author Profile Icon Lisa Weaver-Lambert
Lisa Weaver-Lambert
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Table of Contents (43) Chapters Close

1. In Praise of The AI Value Playbook FREE CHAPTER 2. Chapter 1: Introduction
3. Chapter 2: Overview of AI Concepts and Technology Stack
4. Chapter 3: Sam Liang
5. Chapter 4: Amr Awadallah
6. Chapter 5: Philipp Heltewig
7. Chapter 6: Miao Song
8. Chapter 7: Ruben Ortega
9. Chapter 8: Joshua Rubin
10. Chapter 9: Nadine Thomson
11. Chapter 10: Sarvarth Misra
12. Chapter 11: Edward Fine
13. Chapter 12: Sanjeevan Bala
14. Chapter 13: Nathalie Gaveau
15. Chapter 14: Phil Harvey
16. Chapter 15: Elizabeth Ajayi
17. Chapter 16: Louis DiCesari
18. Chapter 17: Vickey Rodrigues
19. Chapter 18: Sean McDonald
20. Chapter 19: Julie Gray
21. Chapter 20: Peter Jackson
22. Chapter 21: Mark Beckwith
23. Chapter 22: Kshira Saagar
24. Chapter 23: Joe Romata
25. Chapter 24: Tomasz Ullman
26. Chapter 25: Oz Krakowski
27. Case Study
28. Chapter 26: LLMs and RAG Enable Hyper-Personalized Education for Healthcare Technicians 29. Case Study
30. Chapter 27: AI Personalization Increases Engagement in Nascent Tech Communities 31. Case Study
32. Chapter 28: AI-Powered Virtual Agent Augments Service Efficiency 33. Case Study
34. Chapter 29: Generative AI Creates a Paradigm Shift in Innovation Processes 35. Case Study
36. Chapter 30: Unlocking Profit Potential – Leveraging Enterprise Data for Customer Profitability 37. Case Study
38. Chapter 31: Minimizing Customer Churn with AI 39. Case Study
40. Chapter 32: Enhancing Marketing Strategies Through the Power of LLMs 41. Case Study
42. Chapter 33: Multimodal LLMs Redefine Software Development and Customer Innovation 43. Chapter 34: Where We’ve Got To and What’s Next

Applied AI Architect

When you look at what’s next in terms of synthetic generation, synthetic lead-generated data simulation, causal ML, and graphical methods, there’s a raft of new tooling that is more about fitting together models of knowledge than it is specifically statistical models of data. I think we’re moving from the data age into a new form of knowledge age.

LinkedIn: philipdavidharvey

Phil Harvey is an Applied AI Architect and Leader with 23 years of experience in AI. He is embarking on the next stage of his career and prior to this he was the Principal Program Manager at Microsoft. He is the co-author of the book Data: A Guide to Humans.

LISA: How did you get into AI?

PHIL: I was interested in computers from a young age, so I traveled long distances every day to study computer science because my local school didn’t offer it. Then, despite my career counselor saying it was a very bad idea, I did a degree in artificial intelligence...

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