DeepMind on Model Chain of Thought and Interpretability
GoogleDeepMind · x · 2026-07-11
Google DeepMind released a podcast episode discussing how a model's chain of thought acts like scratchpad paper to help understand reasoning.
Hosted by @fryrsquared with researcher @NeelNanda5, the episode focuses on interpretability—specifically, how to reverse-engineer how neural networks learn and "think." The outline explicitly covers:
- The motivation behind studying interpretability
- The basic concepts of mechanistic interpretability
- The role of chain of thought in understanding a model's internal processes
Overall, it is a research-oriented interview/podcast.
Related event: DeepMind Discusses Chain of Thought and Mechanistic Interpretability(4 posts)→
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