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Trust and Validation in AI episode artwork

Nov 1, 2023 43 min

Trust and Validation in AI

from Cloud Native Compass Hosted by Rawkode Academy

JJ Asgar joins to discuss IBM watsonx.ai and why enterprises distrust today's black-box AI. The conversation covers training data provenance, The Pile, Hugging Face, hallucinations, copyright, and the human problem of verifying AI outputs.

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Trust and Validation in AI

0:00 43:11

Chapters

  1. 0:00 Introductions
  2. 0:07 Host Introductions and Episode Topic
  3. 0:21 AI in Practice: Kubernetes Sonnet
  4. 1:21 Introducing the Guest, JJ Asgar
  5. 1:34 JJ's Role and Introduction to IBM WatsonX.ai
  6. 2:14 What is watsonx?
  7. 3:48 Why Enterprises Distrust Current AI (The Black Box Problem)
  8. 5:30 AI for the Enterprise
  9. 6:00 WatsonX: The Trusted Enterprise AI Solution (Napster vs iTunes)
  10. 7:00 AI as a Yes Man
  11. 7:08 Developer Concerns: Bias and Data Augmentation
  12. 8:24 Understanding AI Models and Foundational Data
  13. 9:16 Open Source AI: Hugging Face and The Pile
  14. 11:14 The Pile's Data Quality & Legal Issues (Copyright/Licensing)
  15. 11:20 The Wikipedia AI Challenge
  16. 12:21 Copyright, Patents, and Industry Fragility
  17. 14:34 Why Public Data (Wikipedia) Isn't Ideal for Training
  18. 15:33 Practical Enterprise Use Case: The AI Librarian
  19. 17:26 The Human Problem of Verification and Hallucinations
  20. 19:05 AI Needs Trust
  21. 20:08 IBM's Data Cleaning and Trust Strategy
  22. 21:45 IBM's B2B API Approach ("Red Solo Cups of AI")
  23. 23:11 Specific Use Case: Kubernetes Monitoring
  24. 26:51 The Challenge of Proving Data Origin and Trust
  25. 27:20 People Problem in AI
  26. 29:20 Ethical Dilemmas
  27. 30:38 Ethical Dilemmas and AI Transparency
  28. 35:56 The "Printing Press" Analogy and Global Impact
  29. 37:59 Reflections and Hope for the Future
  30. 40:20 Final Thoughts
  31. 40:41 Guest Final Thoughts & Contact Information
  32. 42:15 Hosts' Wrap-up and Outro

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This episode was published on November 1, 2023.

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