Why compositionality matters (the brain as a reusable toolbox)

36:55really abstract things. But there is a very nice thread of physics and mathematics in this story and it is going to get kind of technical but we're in for the ride, right? Like that's that's why you guys are here. >> Yes. >> So it's it's it's really cool. Okay, the core problem is something called compositionality. Okay, it's a key in biological intelligence and it's something that AI is actually not very good at. >> Okay, and something that the authors used was, you know, if you already know how to bake bread, you can use this ability to bake a cake. >> Why? Because you can actually take parts

37:38of your learning when you baked bread, you know, how to how to mix stuff, how to turn on an oven, how an oven works, and you can take those models together and then figure out, okay, with a cake, it's like not that much different. find. There's like a little bit of frosting. There's like all this like other stuff. But you can take the Legos, the cognitive Legos that you've built when baking bread and then use that with some new stuff to figure out the ability to bake a cake from scratch. >> Okay. >> AI is not very good at this. >> Okay. >> Okay. like a AI's like in in terms of this

38:19generalized intelligence that we kind of are after >> whatever that means >> right whatever that means >> no clear definition but I know I'm >> you know what I mean like it's like it's it's it's good at certain tasks because you've trained it on certain tasks it's seen the data for those certain tasks right but as soon as you sort of bring it out of its comfort zone the performance decreases by a lot okay And for a really long time, it's been kind of a dilemma. This question, how can a neural network, right, that's defined by neurons connected to each other, so you've got these synaptic connections, you want it to be stable enough so that

39:00it doesn't forget old skills. >> Right. >> Right. But you also want it to be plastic enough. We want it to be on the move enough that it can actually rapidly assemble new skills. Okay? So, it's this dilemma between stability and plasticity. Plasticity meaning the ability to change. >> And we as as humans have really mastered that tight rope. >> We really have, right? We have >> better than every any other thing we can really see. >> Yeah. I mean, we have um millions of years of evolution to thank for that. But um in terms of AI, when whenever you have AI, there's this problem called catastrophic forgetting. Okay? And that's the idea when you you learn a new

39:42task and when you're learning a new task, it totally obliterates your ability to do some old task. So in this in this case, you've got a dog cat classifier and then you train it to, you know, figure out how birds look and then all of a sudden it can't figure out what a dog and a cat look. [laughter] Now this is an exaggeration because nowadays we have learning models that that can do much better. But at the end of the day this idea of catastrophic forgetting is still very much a problem. Right? If you want to expand to just generalized ability to solve tasks. >> Okay. >> Now in cognitive science we have another problem which is called the binding problem. And that's the idea of how does the brain dynamically bind different

40:23attributes to the same thing without requiring a single neuron for all of the specific stuff. And here's what I mean over there. You've got a a person who's looking at a rolling ball. The ball is red and it's moving to the right. >> Mhm. >> Right now, there's three attributes here that I can immediately see. There's motion. It's moving to the right. There's color. It's red. And there's form. It's a ball and it's rolling. All right. Now, the brain doesn't have a single neuron for red rolling ball to the right. Okay? Because that would be insane, >> right? >> Okay. I don't have like a single neuron

41:03for every little object and every little idea. What the brain does is somehow combine all of these ideas and bind them together into a single idea of a rolling ball that's moving to the right. Okay. >> Okay. And that's called a binding problem. >> Okay. >> Okay. It's it's a problem in neuroscience. And we don't quite know mechanistically inside the brain how these neurons are actually doing it. >> Okay. We we can you can talk about it like oh it's you know there's an idea of a red but at the end of the day like it's like what is the neuron doing cuz these are just cells in my brain >> and and it's an inc it is incredible because it's it's a highly efficient

41:44highly uh sustainable architectural system. >> Yes. Yeah. >> And like it would >> that can learn anything. >> It would be good to know how it works. >> Yes. >> For a whole number of reasons. >> Yes. Exactly. And that's what the key discovery of this paper that came out of nature is. Okay. What what they figured out is the neural basis at a systems neuroscience level of these cognitive Legos. Okay. And they used something called lowdimensional shared neural subspaces. We're going to get into what that is, but there's a lot of math involved and some very cool physics intuition, which is why I really love this paper because it reminds me of like sort of why I got into like physics and neuroscience and the beauty of that in

42:26the first place. >> So, what we're what we're really doing is we're solving the problem of this binding problem. >> Yes. >> Using very cool mathematics >> and it might actually give us insights into how to train AI better. Which makes sense because ultimately if we understand how to solve the binding problem like that's immediately going to be architecture you want to implement into any kind of artificial system that you want to replicate human intelligence. >> Yeah. Exactly. And AI is at the end of the day for a long time it's been inspired by um human cognition. So there's a reason why they're called neural networks. Right? >> Okay. So let's talk about some mathematics now. Okay. Here we go. So