Adam Marblestone – AI is missing something fundamental about the brain
12/30/20251 hr 50 min
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
Relevant episodes: Ilya Sutskever, Richard Sutton, Andrej Karpathy
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Clips
Transcript preview
First 90 secondsDwarkesh Patel· Host0:00
The big million-dollar question that I have that, um, I've been trying to get the answer to through all these interviews with AI researchers: How does the brain do it, right? Like, we're throwing way more data at these LLMs, and they still have a small fraction of the total capabilities that a human does. So what's going on?
Adam Marblestone· Guest0:15
Yeah. I mean, this might be the quadrillion-dollar question- (laughs) ... or something like that. It's- it's- it's arguab- you could make an argument this is the most important, you know, question in science. I don't claim to know the answer. I- I also don't really think that the answer will necessarily come even from a lot of smart people thinking about it as much as they are. I- my- my overall, like, meta-level take is that we have to empower the field of neuroscience to just make neuroscience a- a more powerful, uh, field, technologically and otherwise, to actually be able to crack a question like this. But maybe the- the way that we would think about this now with, like, modern AI, neural nets, deep learning, is that there are sort of these- these cer- certain key components of that. There's the architecture. Um, there's maybe hyperparameters of the architecture. How many layers do you have or sort of properties of that architecture? There is the learning algorithm itself. How do you train it? You know, backprop, gradient descent, um, is it something else? There is how is it initialized, okay? So if we take the learning part of the system, it still may have some initialization of- of the weights. Um, and then there are also cost functions. There's like, what is it being trained to do?
Dwarkesh Patel· Host1:27
Yeah.
Adam Marblestone· Guest1:28
What's the reward signal? What are the loss functions?