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…Yeah. So I'm inclined to agree. I've I've long dreamed about there being a docker for language models. Right? You you know, in docker, you can kind of freeze dry a state of, you know, like, let's say Linux operating system with an application with the security updates. You have these kind of immutable layers. And the composability that we often talk about could actually happen at the architectural level, and we could do dynamic model merging between different layers and and whatnot. That would be very, very exciting. So, know but also, just to come back to what you said before, I've never really heard this before. You're distinguishing, like, forgetting and learning. Right? When when we were talking about, you know, catastrophic forgetting and continual learning. Can you just sketch out that distinction a bit more? So the way I think about it, you know, you have a neural network, and it has all of these weights inside of it. Right? Anytime you update those weights, you are pushing some weights out. And presumably, you are pushing some correct answers that you've previously, you know, trained, and they are getting pushed out. And the benefit, I think fundamentally, of symbolic systems is that doesn't happen. Symbolic systems are deterministic. When you get the right answer, you can be sure you have the right answer. You stash it away into your library of correct solutions. This is this is the problem with continuous structures, but this is also actually now why I think, you know, it's important to draw from the brain, which is this similar thing happens actually with the brain. I believe the brain is much more composable than neural networks biologically, but I think there's no reason why we can't, you know, we we won't be able to figure this out. Right? Like, again, it could be it's it's as easy as we end up freezing experts. Again, like, the freezing of the layers, I think this is an an underexplored, area, and I think we're going to go through basically this RLS curve. And then I think this is the next S curve, is figuring out how to make language models composable. I get actually, even more fundamentally, like, the ideal system would be we have a set of data. Our language model is bad at a certain thing. We can just give it this data, and then all of a sudden, it keeps all of its knowledge and then also gets really good at this new thing. We we are not there yet, And that to me is like a fundamental missing part of general intelligence. Yeah. Completely agree. So it it sounds like we have very, very similar intuitions. And and Charlie talks about this as well. Mean, in his measure of intelligence paper, it was actually about the measure of intelligence. He's never really spoken about the machinations of intelligence. He talks about it just casually. He says, you know, we need the spell key priors and we need to so those are like the basis functions and we need to do library learning and library transfer and we do some kind of like, you know, symbolic compositional process, you know, to adapt to novelty. And he's kind of sketched out the the mechanics of it, but he's never actually formally spoken about it. I assume that's what he's building at at at his company. But there was a famous guy called Jerry Fodor in 1988. He had this connectionism critique. He had this beautiful paper. And he was basically saying that symbolic systems have systematicity and productivity. And systematicity is this compositional thing. It's that, you know, it's the ability to generalize between Mary loves John and Mary loves Jane. Right? So you so you have semantics. You have these kind of like, you know, the the symbolic relations. And they have certain computational properties, like you can do a variable binding and and quantification over potentially infinite domains. Like, we intuitively understand that symbolic things have very interesting properties. And then what we're trying to do is, like, we know neural networks are really good, and we want to somehow graft this capability onto neural networks.…

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