DeepMind Discusses Model Interpretability
GoogleDeepMind · x · 2026-07-11
A Google DeepMind podcast invited @NeelNanda5 to discuss interpretability research, focusing on "reverse engineering" how neural networks learn and think.
Key points include:
- chain of thought can act like scratch paper to help observe model reasoning
- mechanistic interpretability: studying internal mechanisms, not just inputs and outputs
- chain of thought monitoring: using chain of thought as a window for monitoring and security auditing
- Also discussed interpretability techniques, model security auditing, and future developments in this direction
Related event: DeepMind Discusses Chain of Thought and Mechanistic Interpretability(4 posts)→
More from Research
- Navier-Stokes, Riemann, P vs NP: what this week's math buzzwords mean for you — koltregaskes · 2026-09-11
- Fruit fly connectome LLM weights land on Hugging Face, transformers-compatible — ngxson · 2026-09-11
- Fruit fly brain as an LLM: connectome-driven language model demo goes live — ngxson · 2026-09-11
- Harry Collins: LLMs can't do frontier science because they can't invent new language — whoamisri · 2026-09-11
- The Waymo effect: how AI is quietly making research less collaborative — JohnHammersley · 2026-09-11
- Causal-only attention for non-generative tasks is wasteful, argues HF engineer — antoine_chaffin · 2026-09-11