Tim Scarfe: evaluation

Transcript context

…to work pretty well. And then your you know, maybe some other piece would be like, yeah. Moles are kind of superhuman at predicting the results of experiments because they've read so many papers and, predicting experiments and and sort of synthesizing things from different fields. And, again, maybe this is something like, you know, it's possible that we're not seeing that good performance here just because we haven't quite elicited the models to do it, and it it's, like, not a thing that they've seen humans do, but they actually sort of have the capability in there. Yeah. So maybe you can make much faster progress if you can you can do a bunch of iteration. You don't actually have to run the experiments. Then, you know, models are much better at predicting what will and won't work. And then you can when you do run experiments, you can sort of, run a bunch more of them because you can optimize the code with your, like, you know, very fast coding models. Know? And then you sort of like, as you do a few more rounds of this, you get to a point where train on a bunch more things that are good, high quality task proxies for what you want, and you get enough generalization to the things that you can't, directly train against. I I think intelligence is is not capability. I think it's the capability to acquire capabilities. I mean, we are in different parts of the phylogenetic tree, I guess, that respect. But what do you think is the kind of the gap in my interpretation? Because I'm not I'm personally not worried about I I don't think the models today are intelligent at all. I mean, obviously, your your position is difficult for for me to grasp, but I don't know. What what's what's the difference, do you think? May maybe at least some of it is just this, like, probabilistic thinking about the world where, like, I'm not that you know, I I'm, like, uncertain about what intelligence is, and I have enough probability on, like, you know, moles have it to to be thinking about, like, what would happen if, you know, if that's true. But you you know, it seems like you clearly think it's more likely than than I do so that that, you know, we could just talk about that difference. Yeah. Like, moles have this jagged frontier. There are things that they're much worse at than humans, you know, some kind of, like, generalization and and, sample efficiency, and there are things that they're much better at. You you know, kind of like speed and cost, and it's like, maybe you can you can kinda use these to compensate for the for the the others to to some extent. Like, if you're not good at designing your code nicely, maybe you just have to rewrite it from scratch every time. But maybe that's fine if you're a model and you can output tokens like nobody's business. Yeah. Some combination of thinking that the the spikiness you you know, it is evidence that we should interpret a given level of capabilities as you know, because we know models have so much knowledge, we're like, oh, yeah. This is less impressive in terms of sort of, like, reasoning or inference or something. But it is also true that they do have a ton of knowledge, and they will sort of continue having a ton of knowledge about things. Maybe maybe there's some question about, like, how far can you get on being, in some sense, not, you know, not very good at sample efficient learning, but just extremely knowledgeable, and how much do you sort of run into, you know, think things where you now need need new knowledge and you can't sort of produce it in some incremental way or you you can't generalize,…

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