AI Summary
5 min read🎙️ The Voices & The Context
- The Format: A deep-dive interview between host and guest, blending technical explanations with personal anecdotes and forward-looking debate on AI.
- The Key Players:
- Guest: Vishhal Misra, Professor and Vice Dean of Computing and AI at Columbia University. Famous for pioneering work on LLM internals, including the first production RAG system with GPT-3 and viral papers mathematically modeling transformers as Bayesian updaters.
- Host: Martin Casado, a16z partner, geeky AI enthusiast who funded Misra's compute and geeks out over the research.
- The Vibe: Educational and excited—technical yet accessible, with "mind-blown" moments, banter on AI hype, and optimistic futurism.
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What you'll learn
- 1 (00:00) **🎙️ Introduction: Vishal Misra**
- 2 (01:50) **Background: Cricket Stats Querying with GPT-3**
- 3 (03:31) **LLM Matrix Abstraction**
- 4 (08:33) **In-Context Learning Mechanics**
- 5 (15:09) **Bayesian Wind Tunnel Experiments**
- 6 (21:41) **Why Transformers Do Bayesian Updating**
- 7 (23:03) **LLMs vs. Human Cognition**
+ Full timestamped outline available in the app
Show Notes
Vishal Misra returns to explain his latest research on how LLMs actually work under the hood. He walks through experiments showing that transformers update their predictions in a precise, mathematically predictable way as they process new information, explains why this still doesn't mean they're conscious, and describes what's actually required for AGI: the ability to keep learning after training and the move from pattern matching to understanding cause and effect.
Resources:
Follow Vishal Misra on X: https://x.com/vishalmisra
Follow Martin Casado on X: https://x.com/martin_casado
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