Exploring How To Fail Interpretability Research
Exploring How To Fail Interpretability Research reveals several interesting facts.
- This is a talk I gave to my MATS 9.0 training scholars about the big picture of mech interp - as of Oct 2025, what had changed?
- Neel Nanda discusses mechanistic
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Been Kim (Google Brain) https://simons.berkeley.edu/talks/tba-90 Emerging Challenges in Deep Learning. 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 '25 session, An Introduction to Mechanistic ... How can we use the language of causality to understand and edit the internal mechanisms of AI models? Atticus Geiger ...
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