Google DeepMind@GoogleDeepMind · XAI 摘要
Google DeepMind 最新播客中，主持人 @fryrsquared 与 @NeelNanda5 探讨了可解释性——逆向工程神经网络学习与思维的科学。核心话题包括：模型链式推理（chain of thought）如同草稿纸，可窥见其推理过程；机制可解释性（mechanistic interpretability）；链式推理监控；可解释性技术；模型安全审计；以及可解释性研究的未来方向。播客提供了完整时间戳。
A model's chain of thought acts like a scratch pad， offering a window into its reasoning. 📝
On the latest episode of our podcast， host @fryrsquared sits down with @NeelNanda5 to explore interpretability - the science of reverse engineering how neural networks learn and think.
Timecodes： 00：00 Introduction 02：41 Motivation for interpretability research 04：01 Mechanistic interpretability 08：14 Chain of thought monitoring 18：14 Interpretability techniques 35：00 Auditing models for safety 48：53 What comes next for interpretability