In episode 40 of The Gradient Podcast, Andrey Kurenkov speaks to Catherine Olsson and Nelson Elhage.Catherine and Nelson are both members of technical staff at Anthropic, which is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Catherine and Nelson’s focus is on interpretability, and we will discuss several of their recent works in this interview. Follow The Gradient on TwitterOutline:(00:00) Intro(01:10) Catherine’s Path into AI(03:25) Nelson’s Path into AI(05:23) Overview of Anthropic(08:21) Mechanistic Interpretability(15:15) Transformer Circuits (21:30) Toy Transformer(27:25) Induction Heads(31:00) In-Context Learning(35:10) Evidence for Induction Heads Enabling In-Context Learning(39:30) What’s Next(43:10) Replicating Results(46:00) OutroLinks:AnthropicZoom In: An Introduction to CircuitsMechanistic Interpretability, Variables, and the Importance of Interpretable BasesA Mathematical Framework for Transformer CircuitsIn-context Learning and Induction Heads PySvelte Get full access to The Gradient at thegradientpub.substack.com/subscribe