In this episode, Daniel Balsam and Tom McGrath, at Goodfire, discuss the future of mechanistic interpretability in AI models.

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In this episode, Daniel Balsam and Tom McGrath, at Goodfire, discuss the future of mechanistic interpretability in AI models. They explore the fundamental inputs like models, compute, and algorithms, and emphasize the importance of a rich empirical approach to understanding how models work. They provide insights into ongoing projects and breakthroughs, particularly in scientific domains and creative applications, as they aim to push the frontiers of AI interpretability. They also discuss the company's recent funding and their goal to advance interpretability as a critical area in AI research.

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CHAPTERS:
(00:00) About the Episode
(04:44) Introduction and Welcome
(05:22) Framing the Field of Machine Learning
(06:11) Empirical Data and Interpretability
(09:51) Challenges in Model Experimentation
(10:28) Unsupervised Learning and Interpretability
(12:12) The Role of Compute and Algorithms
(14:48) Analogies in Interpretability (Part 1)
(16:22) Sponsors: Box AI | Oracle Cloud Infrastructure (OCI)
(19:13) Analogies in Interpretability (Part 2)
(19:40) Philosophical Questions in Interpretability
(23:19) Current State and Future Directions
(32:20) The Paradigm of Interpretability (Part 1)
(34:54) Sponsors: ElevenLabs | NetSuite | Shopify
(39:32) The Paradigm of Interpretability (Part 2)
(41:43) Competing Approaches and Techniques
(48:14) Machine Learning Techniques for Better Decomposition
(57:21) Minimum Description Length and Interpretability
(59:27) Understanding Minimum Description Length
(59:56) Sparse Autoencoders and Optimization Targets
(01:03:35) Challenges in Model Reconstruction
(01:05:02) Dark Matter in Scaling Analysis
(01:06:43) Exploring Features and Interpretability
(01:19:21) Scientific Discovery and Interpretability
(01:43:52) Applications of Interpretability Techniques
(01:50:43) Good Fire's Mission and Future Directions
(01:53:46) Outro

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