Tim Scarfe: belief

Transcript context

…er training data that people sort of haven't bothered to get yet, but you don't need a huge number of data points because you're not instilling this whole new capability. You're just, like, eliciting. Okay. Actually, use your knowledge of all of the papers you've read in all of these different fields to, like, you know, iterate through these, like, no. These ideas aren't promising. These ones are. Yeah. I again, I think this is 1 of the things that I more think of as this being measured a reasonable amount within you know, just, like, do this 8 hour ML task in a novel domain, you know, with this weird weird constraint or something. Like, it it does seem to me like you have to do some amount of being like, okay. Which things are promising to think about? How would I know if this is, you know, making progress? You know, how should I allocate my time? You know, I've got some limited time resources. How should I allocate my time time to what's most promising? Like, you have to be able be doing some of that. And I think, you know, relative to humans, moles are doing more at you know, more of just like, well, they're quick to implement things or they implement it better or they, you know, they implement more things, and they can then get to to test them or something. But I I think it would be sort of surprising if there's none of that. And if you are I think if you are seeing performance on, you know, long verifiable tasks that are very hard, then in the middle of those tasks, like, where you don't directly have a signal, you you are doing this, you know, nonverifiable task thing of, like, choosing what to spend your time on and and choosing what approach to pursue and deciding whether that was actually working and, you know you know, in terms of you you could sort of put some metric on you know, make 1000000000 dollars or or or something like that. You know, you'd be like, oh, this is actually a verifiable task because there's, like, a number at the end, but that can still involve a whole load of things that look more like what you're describing and less like sort of dumb hill climbing. Folks, we we I think we've run out of time, but, it's been such an honor to have you both on. May may maybe just in closing, could could you just both say, like, what what is the the single biggest inference that, you know, people out there should be making from the research that you're doing? And and thank you both so much for coming on. It's been an honor. To me, the biggest thing is AI. It might really, you know, totally transform the world, economically and and and socially. And I don't think that, you know, it's certain exactly how that how that'll look, but I think the the rate of progress,…

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