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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Chapters
- 0:00 Introductions
- 0:07 Host Introductions and Episode Topic
- 0:21 AI in Practice: Kubernetes Sonnet
- 1:21 Introducing the Guest, JJ Asgar
- 1:34 JJ's Role and Introduction to IBM WatsonX.ai
- 2:14 What is watsonx?
- 3:48 Why Enterprises Distrust Current AI (The Black Box Problem)
- 5:30 AI for the Enterprise
- 6:00 WatsonX: The Trusted Enterprise AI Solution (Napster vs iTunes)
- 7:00 AI as a Yes Man
- 7:08 Developer Concerns: Bias and Data Augmentation
- 8:24 Understanding AI Models and Foundational Data
- 9:16 Open Source AI: Hugging Face and The Pile
- 11:14 The Pile's Data Quality & Legal Issues (Copyright/Licensing)
- 11:20 The Wikipedia AI Challenge
- 12:21 Copyright, Patents, and Industry Fragility
- 14:34 Why Public Data (Wikipedia) Isn't Ideal for Training
- 15:33 Practical Enterprise Use Case: The AI Librarian
- 17:26 The Human Problem of Verification and Hallucinations
- 19:05 AI Needs Trust
- 20:08 IBM's Data Cleaning and Trust Strategy
- 21:45 IBM's B2B API Approach ("Red Solo Cups of AI")
- 23:11 Specific Use Case: Kubernetes Monitoring
- 26:51 The Challenge of Proving Data Origin and Trust
- 27:20 People Problem in AI
- 29:20 Ethical Dilemmas
- 30:38 Ethical Dilemmas and AI Transparency
- 35:56 The "Printing Press" Analogy and Global Impact
- 37:59 Reflections and Hope for the Future
- 40:20 Final Thoughts
- 40:41 Guest Final Thoughts & Contact Information
- 42:15 Hosts' Wrap-up and Outro
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Frequently asked questions
How long is this episode of Cloud Native Compass?
This episode is 43 minutes long.
When was this Cloud Native Compass episode published?
This episode was published on November 1, 2023.
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