Nathan explores the cutting-edge field of mechanistic interpretability with Dan Balsam and Tom McGrath, co-founders of Goodfire. In this episode of The Cognitive Revolution, we delve into the science of understanding AI models' inner workings, recent breakthroughs, and the potential impact on AI safety and control. Join us for an insightful discussion on sparse autoencoders, polysemanticity, and the future of interpretable AI.

Papers

• Very accessible article on types of representations: Local vs Distributed Coding
  


• Theoretical understanding of how models might pack concepts into their representations: Toy Models of Superposition
  


• How structure in the world gives rise to structure in the latent space: The Geometry of Categorical and Hierarchical Concepts in Large Language Models
  


• Using sparse autoencoders to pull apart language model representations: Sparse Autoencoders / Towards Monosemanticity / Scaling Monosemanticity
  


• Finding & teaching concepts in superhuman systems: Acquisition of Chess Knowledge in AlphaZero / Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero
  


• Connecting microscopic learning to macroscopic phenomena: The Quantization Model of Neural Scaling
  


• Understanding at scale: Language models can explain neurons in language models
  

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CHAPTERS:

(00:00:00 ) About the Show

(00:00:22 ) About the Episode

(00:03:52 ) Introduction and Background

(00:08:43 ) State of Interpretability Research

(00:12:06 ) Key Insights in Interpretability

(00:16:53 ) Polysemanticity and Model Compression (Part 1)

(00:17:00 ) Sponsors: Oracle | Brave

(00:19:04 ) Polysemanticity and Model Compression (Part 2)

(00:22:50 ) Sparse Autoencoders Explained

(00:27:19 ) Challenges in Interpretability Research (Part 1)

(00:30:54 ) Sponsors: Omneky | Squad

(00:32:41 ) Challenges in Interpretability Research (Part 2)

(00:33:51 ) Goodfire's Vision and Mission

(00:37:08 ) Interpretability and Scientific Models

(00:43:48 ) Architecture and Interpretability Techniques

(00:50:08 ) Quantization and Model Representation

(00:54:07 ) Future of Interpretability Research

(01:01:38 ) Skepticism and Challenges in Interpretability

(01:07:51 ) Alternative Architectures and Universality

(01:13:39 ) Goodfire's Business Model and Funding

(01:18:47 ) Building the Team and Future Plans

(01:31:03 ) Hiring and Getting Involved in Interpretability

(01:51:28 ) Closing Remarks

(01:51:38 ) Outro