Model-free and model-based RL in brain

Adam Marblestone is CEO of Convergent Research. 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; read the transcript.

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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 - Steven Byrnes’s theory of the learning vs steering subsystem; referenced throughout the episode.

A Brief History of Intelligence - Great book by Max Bennett on connections between neuroscience and AI

Adam’s blog, and Convergent Research’s blog on essential technologies.

A Tutorial on Energy-Based Learning by Yann LeCun

What Does It Mean to Understand a Neural Network? - Kording & Lillicrap

E11 Bio and their brain connectomics approach

Sam Gershman on what dopamine is doing in the brain

Gwern’s proposal on training models on the brain’s hidden states