Exploring the Impact of Activation Patching on Model Performance

Lots of people in the AI safety space worry about models being able to make deliberate, multi-step plans. But can we already see this in existing neural nets? In this episode, I talk with Erik Jenner about his work looking at internal look-ahead within chess-playing neural networks.

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The transcript: https://axrp.net/episode/2024/12/12/episode-38_3-erik-jenner-learned-look-ahead.html

FAR.AI: https://far.ai/

FAR.AI on X (aka Twitter): https://x.com/farairesearch

FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch

The Alignment Workshop: https://www.alignment-workshop.com/

Topics we discuss, and timestamps:

00:57 - How chess neural nets look into the future

04:29 - The dataset and basic methodology

05:23 - Testing for branching futures?

07:57 - Which experiments demonstrate what

10:43 - How the ablation experiments work

12:38 - Effect sizes

15:23 - X-risk relevance

18:08 - Follow-up work

21:29 - How much planning does the network do?

Research we mention:

Evidence of Learned Look-Ahead in a Chess-Playing Neural Network: https://arxiv.org/abs/2406.00877

Understanding the learned look-ahead behavior of chess neural networks (a development of the follow-up research Erik mentioned): https://openreview.net/forum?id=Tl8EzmgsEp

Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT: https://arxiv.org/abs/2310.07582

Episode art by Hamish Doodles: hamishdoodles.com