Adam Marblestone: belief

Transcript context

…But those are things we learned because we built them, not because we interpreted them from seeing the weights. The analogous thing to connectome is like seeing the weights. What I think we should do is we should describe the brain more in that language of things like architectures, learning rules, initializations, rather than trying to find the Golden Gate Bridge circuit and saying exactly how this neuron actually… That’s going to be some incredibly complicated learned pattern. Konrad Kording and Tim Lillicrap have this paper from a while ago, maybe five years ago, called “What does it mean to understand a neural network?” What they say is basically that you could imagine you train a neural network to compute the digits of pi or something. It’s like some crazy pattern. You also train that thing to predict the most complicated thing you find, predict stock prices, basically predict really complex systems, computationally complete systems. I could train a neural network to do cellular automata or whatever crazy thing. It’s like, we’re never going to be able to fully capture that with interpretability, I think. It’s just going to just be doing really complicated computations internally. But we can still say that the way it got that way is that it had an architecture and we gave it this training data and it had this loss function. So I want to describe the brain in the same way. And I think that this framework that I’ve been kind of laying out is that we need to understand the cortex and how it embodies a learning algorithm. I don’t need to understand how it computes “Golden Gate Bridge.” But if you can see all the neurons, if you have the connectome, why does that teach you what the learning algorithm is?…

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