25 Interpretability

MIT 6.S897 Machine Learning for Healthcare, Spring 2019 Instructor: Peter Szolovits View the complete course: ...

Machine Learning for Healthcare #MachineLearning #ArtificialIntelligence #AI #ML #DataScience #HealthcareAI #AIinHealthcare ...

How can we reverse engineer what a neural network is doing? In this IASEAI '

Interpretability

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?

A surprising fact about modern large language models is that nobody really knows how they work internally. At Anthropic, the ...

Speaker: Hanieh Arjmand, ML Researcher, Lydia.ai & Spark Tseung, Applied Data Scientist, Lydia.ai Model

With a growing interest in

Paper: Compositionality Unlocks Deep

Part 1 of a walkthrough of our paper, Progress Measures for Grokking via Mechanistic

This talk was recorded at NDC AI in Oslo, Norway. #ndcai #ndcconferences #developer #softwaredeveloper Attend the next NDC ...

Stanford AI Lab Faculty Lunch, November 7, 2025. Updated version of https://web.stanford.edu/~cgpotts/blog/interp/ 0:59 ...

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What's happening inside an AI model as it thinks? Why are AI models sycophantic, and why do they hallucinate? Are AI models ...

Forough Poursabzi, Researcher, Microsoft Research Presented at MLconf 2018 Abstract: Machine learning is increasingly used to ...

Abstract We introduce combinatorial

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Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=ugvHCXCOmm4 Thank you for listening ❤ Check out our ...