Kenneth Stanley: belief

Transcript context

…Part of what's important is just to point out that we've identified a really interesting phenomenon, but we don't have answers to all these questions yet. And sort of part of what we're doing is saying, let's go find out the answers to these things. It's possible that some of them help. It's possible some of them don't help. I think 1 thing just to think about, just to observe upfront is that the difference that you see visually is so dramatic and stark. Anybody who looks at the figure of the 2 different versions of the skull, it's hard to believe, although we don't have the evidence yet, that there's something that's going to fix this that's just real simple, like a grok. I imagine grokking does something good, but it's hard to believe it ends up making something that looks like the CPPN version of the skull from Pick Breeder. That's hard to believe. But nevertheless, it's possible that things like rocking and all kinds of other things that we do, mixture of experts, maybe convolution helps in some ways, although that's not in the LLMs, but it could be in other networks helping. They're all just questions that are not answered. But I think 1 thing to think about with respect to things like grokking is just, even if you're trying to rationalize and save your faith in how these things work right now, and think, Oh, well, it's okay, grokking will fix this. Just think about this, like wouldn't it be nice if you didn't have to do grokking? What grokking basically means is that you get this absolutely horrible entangled mess and you clean it up later, like once you start figuring things out, start deleting all the excess and redundancy, all the fracture gets fixed. Wouldn't it be nice if you don't have to do that and it's just good in the first place? What we've seen because of the PickBreeder CPPN is that that actually is possible. Without this example, without this weird example from PickBreeder, almost no 1 would believe or buy that that's even possible. It's like, well, I mean, of course I'd love miracles to happen every day, but it's just not gonna happen. But the thing is that it did happen and it happens consistently. So it raises this question is that even if grokking is helping, is there a better way or maybe a complementary way? Because maybe both factor into why human brains get to where they get to. So these are unanswered questions. This is not to say that grokking doesn't help. We need to check and find out the degree to which it helps. But 1 other kind of interesting thing to think about is that if you think about the efficiency of training, how expensive it is, like we're building, spending billions, hundreds of billions of dollars, creating these giant data centers to do giant kinds of training runs. Like, if it's true that there's an incredibly more efficient way of doing things. So for example, it could be that, yes, SGD with a brute force will do basically almost anything at least in terms of being able to get the training data. incredibly more efficient way of doing things. So for example, it could be that, yes, SGD with a brute force will do basically almost anything at least in terms of being able to get the training data. Obviously, are implications for generalization from what we're observing here. But at least if you cover almost all of human knowledge, you could do a lot of useful stuff. And so you just brute force it into the system and you get this FER representation which sucks, but it still works. But the thing that this raises is the question is like, but it's really, really hard if you're in a point in search space where all the regularities are broken to get to the next point in terms of higher level intelligence. You might be able to do it, but the representation is not doing you any favors in doing it efficiently. Because all the degrees of freedom that you have are the wrong ones. And so you're constantly overcoming and making up for and overcoming and making up for how much cheaper would this whole thing be if we just did these things in a way that actually worked the way we're seeing in these CPPNs. If it was actually always getting the right dimensions or close to the right dimensions that actually align with the modular decomposition of the way the world actually works, then this could be multiple 10x, 100x, more efficient in many ways. So even if like you still believe we're going to get there with just SGD, this could at least have implications from an efficiency point of view. I suspect it has more implications than just that because when we go beyond efficiency to things like creativity, what you're seeing is that the dimensions that have been discovered in the skull for example, align with new skulls, imagining new things in the world. You wouldn't be able to imagine these new things if you didn't have those dimensions already grokked, to speak. And so your ability to imagine something new is highly compromised And of course, like the whole thing, the conversation, the public conversation right now is turning to exactly this issue of creativity. Like people say, well, where's all the new knowledge? Like, isn't that next on the menu? We're acing all these tests and it's amazing math Olympiad, beating the PhD level people. The people don't get PhDs because we want them to score well on tests. They get PhDs because we expect them to invent something. They're supposed to come up with new knowledge. Where is this going to happen? And so this suggests that there could be serious implications here because the ability to invent something new requires that as a prerequisite, understand the underlying dimensions of reality. Even if you're going to break those dimensions like some really innovative people do intentionally, like break a rule. It's because they know the rules before they break the rules that it's interesting. If they just don't know anything and don't understand how the world works at all, they're not going to be brilliant and have these amazing insights. And so I think there's really big implications here in terms of both efficiency, creativity, generalization and continual learning. , they're not going to be brilliant and have these amazing insights. And so I think there's really big implications here in terms of both efficiency, creativity, generalization and continual learning. Because what I said about if you want to keep learning, it depends on how you represent the world, how efficient that's going to be. So like, don't think we can just say, grokking might fix it and like everything's going to be fine.…

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