The Dark Matter of AI [Mechanistic Interpretability]
25. Interpretability
An Introduction to Mechanistic Interpretability – Neel Nanda | IASEAI 2025
Koji Hashimoto - On the interpretability of neural network solutions of PDEs in theoretical physics
Scaling interpretability
Terence Tao - Reflections on the Foundations of Interpretability workshop - IPAM at UCLA
Adam Shai - Building the Science of Interpretability
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Last Updated: September 26, 2026
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Summary
What's happening inside an AI model as it thinks? Why are AI models sycophantic, and why do they hallucinate? Are AI models ... In this video, we explore the concept of What if you were to peer inside the 'mind' of AI? You wouldn't find fully formed thoughts, just vast arrays of numbers. In this ... Recorded 01 September 2026. Max Tegmark of the Massachusetts Institute of Technology presents "Neural network ... A surprising fact about modern large language models is that nobody really knows how they work internally. At Anthropic, the ... Recorded 31 August 2026. Josh Batson of Anthropic presents " How can we use the language of causality to understand and edit the internal mechanisms of AI models? Atticus Geiger ... Take your personal data back with Incogni! Use code WELCHLABS at the link below and get 60% off an annual plan: ... MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: Peter Szolovits View the complete course: ... How can we reverse engineer what a neural network is doing? In this IASEAI '25 session, An Introduction to Mechanistic ... Recorded 18 September 2026. Koji Hashimoto of Kyoto University presents "On the Science and engineering are inseparable. Our researchers reflect on the close relationship between scientific and engineering ... Recorded 04 September 2026. Terence Tao of the University of California, Los Angeles, presents "Reflections on the Foundations ... Adam Shai presented “Building the Science of