Akarsh Kumar: belief

Transcript context

…And and even with that, I mean, we're not gonna get into the computational argument because Keith will go off on 1 about Turing machines. But you know, there there there are actually limitations of what you can do. So so the neural network has has to learn some fractured version of multiplication or or whatever it is because it's it's a finite amount computation. And we were just talking about grokking. And the way I understand that is as the training process progresses, they start off learning quite simplistic sort of low frequency representations. And then you train and train and train to grokking. And eventually you learn very high frequency representations. And it just so happens that many of those high frequency representations are more aligned to the natural factorization of the world. But that's basically coincidental. There is no principled way to distinguish good representations from bad representations. I tell them never use GPT to generate anything because it's it's always obvious. But what you can do is you can write something and you can discriminate with a GPT model. Right? And so so so it's good at discriminating, not good at generating. Would it be possible to have our cake and eat it? Could we build some kind of a a sort of like a bottom up algorithm that does something a little bit similar to Pick Breeder, and every step of the way it's asking a language model that's been trained on everything in the world, Does this look good? Does this look good? Would something like that work? Yeah. Like a pig breeder for AI and intelligence. Right? Yeah. I mean, like, creating an open end algorithm which solves this issue, I think, Jeff Kloon on our paper, he calls it, like, the trillion dollar question or the trillion dollar algorithm because that's basically, like, how you, if you think that the UFR, like, the unified factor representations are akin to, like, a human, then that's basically, like, creating, like, a human representation. Right? So, yeah, I mean, I guess the answer from my side is that, some algorithms should exist, and we're gonna try to find it. But right now, we're we're still, you know, just in the preliminary stages of this kinda thing. Yeah. I mean, it it doesn't seem like there's any principle that says you can't do this, algorithmically. Mhmm. Presumably, it's all algorithmic. So hopefully, this is just a a nudge to have us start looking into it. You can go on your merry way down the path that we're going down and just make things bigger and have more and more data and ignore this, but then you're at risk of disruption. Because if somebody does actually take this seriously and it works, obviously it would have profound implications. I mean, just look at the 2 pictures of the underlying representations in the skull. If that actually would be translated into the world of giant LLMs, there's going to be implications for that. So I don't think we can just ignore this question. And and the question of, like, these magical types of algorithms is on the table now.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.