Jesse Hoogland and Daniel Murfet, founders of Timaeus, introduce their mathematically rigorous approach to AI safety through "developmental interpretability" based on Singular Learning Theory. They explain how neural network loss landscapes are actually complex, jagged surfaces full of "singularities" where models can change internally without affecting external behavior—potentially masking dangerous misalignment. Using their Local Learning Coefficient measure, they've demonstrated the ability to identify critical phase changes during training in models up to 7 billion parameters, offering a complementary approach to mechanistic interpretability. This work aims to move beyond trial-and-error neural network training toward a more principled engineering discipline that could catch safety issues during training rather than after deployment.

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CHAPTERS:

(00:00) About the Episode

(04:44) Introduction and Background

(06:17) Timaeus Origins and Philosophy

(09:13) Mathematical Background and SLT

(12:27) Developmental Interpretability Approach (Part 1)

(16:09) Sponsors: Oracle Cloud Infrastructure | The AGNTCY

(18:09) Developmental Interpretability Approach (Part 2)

(19:24) Proto-Paradigm and SAEs

(24:37) Understanding Generalization

(30:15) Central Dogma Framework (Part 1)

(32:13) Sponsor: NetSuite by Oracle

(33:37) Central Dogma Framework (Part 2)

(34:35) Loss Landscape Geometry

(40:41) Degeneracies and Evidence

(47:25) Structure and Data Connection

(55:36) Essential Dynamics and Algorithms

(01:00:53) Implicit Regularization and Complexity

(01:07:19) Double Descent and Scaling

(01:09:55) Big Picture Applications

(01:17:17) Reward Hacking and Risks

(01:25:19) Future Training Vision

(01:32:01) Scaling and Next Steps

(01:36:43) Outro