Stefano Viel: belief

Transcript context

…tell us next year. The thing I really liked about Arc v 1 and 2 was that, I mean, of all, it was a grassroots community and loads of people without lots of funding could just focus on the kernel of intelligence. And now it's just ridiculously complicated and it's almost de intellectualized the field, means that loads of folks with resources are just throwing reinforcement learning algorithms at it and almost going back into kind of blindfold mode which is I think it's a fair description of a lot of machine learning. We just have really big models and we just train it to do next token prediction and we see what happens and we try and scale up. There there the thing that was so magical about the community was there were just so many great ideas and like, you know, just really, really deep thinking. It's not that simple. Like, if we have a lot of compute, it's it's still hard to make a harness that solve these games and it's still and only starting, like and training is also hard. Like, it's not as simple as just throwing computer. There are still many details that you have to get right. But for sure, as you said, I think it brought up a lot level needed to enter the competition. Like, we're seeing that many people are stuck in the kind of template solution and there are as far as we are aware, at least in the competition, not many trying kind of LM approaches because it's so computationally expensive. So it's definitely made it harder for kind of the average person to enter the competition, but I wouldn't say that it's just a matter of throwing a lot of compute at it and brute forcing it. Yeah. It it's not like most other benchmarks. Right? It it there's there's no language, there's no instructions. Does that make it quite distinct as well? I think I think that's the interesting part about ARC that it kind of tries to remove as much as possible the prior that you get from language or from human knowledge and kind of strip them to the minimum and really only test for intelligence. I think, yeah, that's a cool feature. Language is the basis of how we do a lot of thinking. And what you folks have done is you've gone to language and then gone back again. So we've got this kind of loop. So we go to language, we do some reasoning and then we might use that to do active fine tuning or like do reinforcement learning or whatever. And we've got this virtuous cycle. So he took the language away and we're kind of putting it back again. Yeah, I think the reason we're putting it back is because we're specifically focusing on language models which has been extensively trained on language. I guess if there's other approaches like…

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