Adam Marblestone: belief

Transcript context

…How confident are we that we even have the right algorithmic conceptual vocabulary to think about what the brain is doing? What I mean by this is that there was one big contribution to AI from neuroscience which was this idea of the neuron in the 1950s, just this original contribution. But then it seems like a lot of what we’ve learned afterwards about what the high-level algorithm the brain is implementing, from the backprop to if there’s something analogous to backprop happening in the brain to “Oh is V1 doing something like CNNs” to TD learning and Bellman equations, actor-critic, whatever… It seems inspired by this dynamic where we come up with some idea, maybe we can make AI neural networks work this way, and then we notice that something in the brain also works that way. So why not think there’s more things like this. There may be. I think the reason that I think that we might be onto something is that the AIs we’re making based on these ideas are working surprisingly well. There’s also a bunch of just empirical stuff. Convolutional neural nets and variants of convolutional neural nets. I’m not sure what the absolute latest is, but compared to other models in computational neuroscience of what the visual system is doing, they are just more predictive. You can just score, even pretrained on cat pictures and stuff, CNNs, what is the representational similarity that they have on some arbitrary other image compared to the brain activations measured in different ways? Jim DiCarlo’s lab has this brain score and the AI model is actually… There seems to be some relevance there. Neuroscience doesn’t necessarily have something better than that. So yes, that’s just recapitulating what you’re saying, that the best computational neuroscience theories we have seem to have been invented largely as a result of AI models and finding things that work. So find backprop works and then saying, “Can we approximate backprop with cortical circuits?” or something. There’s been things like that. Now, some people totally disagree with this. György Buzsáki is a neuroscientist who has a book called The Brain from the Inside Out where he basically says all our psychology concepts, AI concepts, all this stuff is just made-up stuff. What we actually have to do is figure out what is the actual set of primitives that the brain actually uses. And our vocabulary is not going to be adequate to that. We have to start with the brain and make new vocabulary rather than saying backprop and then try to apply that to the brain or something like that. He studies a lot of oscillations and stuff in the brain as opposed to individual neurons and what they do. I don’t know. I think that there’s a case to be made for that. And from a research program design perspective, one thing we should be trying to do is just simulate a tiny worm or a tiny zebrafish, almost as biophysical or as bottom-up as possible. Like get connectome, molecules, activity and just study it as a physical dynamical system and look at what it does. But I don’t know, it just feels like AI is really good fodder for computational neuroscience. Those might actually be pretty good models. We should look at that. I both think that there should be a part of the research portfolio that is totally bottom-up and not trying to apply our vocabulary that we learn from AI onto these systems, and that there should be another big part of this that’s trying to reverse engineer it using that vocabulary or variant of that vocabulary. We should just be pursuing both. My guess is that the reverse engineering one is actually going to work-ish or something. Like we do see things like TD learning, which Sutton also invented separately. That must be a crazy feeling to just like—…

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