Adam Marblestone [https://twitter.com/AdamMarblestone] is CEO of Convergent Research [https://www.convergentresearch.org/]. He’s had a very interesting past life: he was a research scientist at Google Deepmind on their neuroscience team and has worked on everything from brain-computer interfaces to quantum computing to nanotech and even formal mathematics. In this episode, we discuss how the brain learns so much from so little, what the AI field can learn from neuroscience, and the answer to Ilya’s question: how does the genome encode abstract reward functions? Turns out, they’re all the same question. Watch on YouTube [https://youtu.be/_9V_Hbe-N1A]; read the transcript [https://www.dwarkesh.com/p/adam-marblestone]. Sponsors * Gemini 3 Pro [https://gemini.google.com] recently helped me run an experiment to test multi-agent scaling: basically, if you have a fixed budget of compute, what is the optimal way to split it up across agents? Gemini was my colleague throughout the process — honestly, I couldn’t have investigated this question without it. Try Gemini 3 Pro today gemini.google.com [https://gemini.google.com] * Labelbox [https://labelbox.com/dwarkesh] helps you train agents to do economically-valuable, real-world tasks. Labelbox’s network of subject-matter experts ensures you get hyper-realistic RL environments, and their custom tooling lets you generate the highest-quality training data possible from those environments. Learn more at labelbox.com/dwarkesh [https://labelbox.com/dwarkesh] To sponsor a future episode, visit dwarkesh.com/advertise [https://www.dwarkesh.com/advertise]. Timestamps (00:00:00) – The brain’s secret sauce is the reward functions, not the architecture (00:22:20) – Amortized inference and what the genome actually stores (00:42:42) – Model-based vs model-free RL in the brain (00:50:31) – Is biological hardware a limitation or an advantage? (01:03:59) – Why a map of the human brain is important (01:23:28) – What value will automating math have? (01:38:18) – Architecture of the brain Further reading Intro to Brain-Like-AGI Safety [https://www.lesswrong.com/s/HzcM2dkCq7fwXBej8] - Steven Byrnes’s theory of the learning vs steering subsystem; referenced throughout the episode. A Brief History of Intelligence [https://www.abriefhistoryofintelligence.com/book] - Great book by Max Bennett on connections between neuroscience and AI Adam’s blog [https://longitudinal.blog/], and Convergent Research’s blog on essential technologies [https://www.essentialtechnology.blog/]. A Tutorial on Energy-Based Learning [http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf] by Yann LeCun What Does It Mean to Understand a Neural Network? [https://arxiv.org/abs/1907.06374] - Kording & Lillicrap E11 Bio [https://www.e11.bio/] and their brain connectomics approach Sam Gershman on what dopamine is doing in the brain [https://gershmanlab.com/pubs/GershmanUchida19.pdf] Gwern’s proposal [https://www.reddit.com/r/reinforcementlearning/comments/9pwy2f/wbe_and_drl_a_middle_way_of_imitation_learning/] on training models on the brain’s hidden states Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe [https://www.dwarkesh.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]