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Interpretability: Understanding how AI models think
Adam Shai - Building the Science of Interpretability
Lecture 25: Interpretability
Guide Labs: Why AI Interpretability Has to Start at Training Time
Strange Geometric Shapes Found Inside AIs — Tom McGrath
The Dark Matter of AI [Mechanistic Interpretability]
Interpretable vs Explainable Machine Learning
Mechanistic Interpretability explained | Chris Olah and Lex Fridman
The Utility of Interpretability — Emmanuel Amiesen
Chenhao Tan - Automating Mechanistic Interpretability [Alignment Workshop]
Stanford CS25: V5 I On the Biology of a Large Language Model, Josh Batson of Anthropic
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Last Updated: September 16, 2026
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Summary
MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: Peter Szolovits View the complete course: ... A surprising fact about modern large language models is that nobody really knows how they work internally. At Anthropic, the ... How can we reverse engineer what a neural network is doing? In this IASEAI ' Christoph Molnar is one of the main people to know in the space of What's happening inside an AI model as it thinks? Why are AI models sycophantic, and why do they hallucinate? Are AI models ... Adam Shai presented “Building the Science of Tom McGrath is co-founder and Chief Scientist at Goodfire, and a former Google DeepMind researcher. He joins Tim Scarfe to ask ... Take your personal data back with Incogni! Use code WELCHLABS at the link below and get 60% off an annual plan: ... Lex Fridman Podcast full episode: youtube.com/watch?v=ugvHCXCOmm4 Thank you for listening ❤ our ... Emmanuel Amiesen is lead author of “Circuit Tracing: Revealing Computational Graphs in Language Models” ... Chenhao Tan demonstrates an automated mechanistic May 13, 2025 Large language models do many things, and it's not clear from black-box interactions how they do them. We will ...