1 rich Libsyn https://www.libsyn.com 90 600 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. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast 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:&amp;nbsp;https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ &amp;nbsp; 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? &amp;nbsp; 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 &amp;nbsp; Episode art by Hamish Doodles:&amp;nbsp;hamishdoodles.com AXRP - the AI X-risk Research Podcast https://axrp.net <iframe title="Libsyn Player" style="border: none" src="//html5-player.libsyn.com/embed/episode/id/34408435/height/90/theme/custom/thumbnail/yes/direction/forward/render-playlist/no/custom-color/88AA3C/" height="90" width="600" scrolling="no" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe> https://assets.libsyn.com/secure/content/182252775