Baris Gultekin: uncertainty

Transcript context

…So I think that's a great first-pass answer. If I think, though, even just about my own ability to search through my own stuff, my own Gmail, my own Google Docs, One of the intuitions I have pretty strongly is if I were to give you full access to my Gmail and give you full access to my Google Docs, you couldn't search through it nearly as well as I can. And that's despite the fact that you're clearly smarter than me. So I'm like, there seems to be something about the fact that I have had this like pre-training on this corpus that allows me to even just search through it a lot better. Because if nothing else, like one of the intuitions is I know when I found what I'm looking for. You might not know, you could do 100 searches in my Google Drive and never be quite confident you got the absolute best document for whatever the question is. Whereas if it's my Google Drive and I created all those documents, when I hit that document, that is the one that's yes, this is the one. Now, yes, I remember this now. This is the one. So I have that sort of confidence that... I've got to the answer, if nothing else, I think, which strikes me as really hard. And I've seen this when I try to give my own, just whatever, give Claude access to search my drive. It also struggles in that way. It doesn't know how many times the search or if it's found the right thing or sometimes satisfied too easily, whatever. So I still feel like there's something there where you could expect that a model that really had a sort of more in the weights familiarity with the data could do a better job of navigating it. And then maybe it just comes down to upgrade cycles are terrible for this kind of thing. And yeah, as you said, like you want to keep taking advantage of better and better models. This potentially, Dwarkesh has obviously influenced the discourse recently with kind of focus on continual learning. So maybe you need either a new architecture that's more suited to that or some sort of new training paradigm that would be more suited to it. I guess maybe one way to phrase this is, if that were to flip, if you imagine a world a year from now where it's no longer the case that the best approach is to pick the best models and leave them as they are, but tune them through the search as you described, and it instead becomes one of these things where they actually do have this deeper familiarity with all the enterprise's data, what would have changed to flip us from one paradigm to the other? I was trying to kind of think about how humans do this. We'd go into an environment that we don't know, and then we'll go do a bunch of searches, and then we'll read to create some kind of knowledge. And then as you build out that knowledge, there's intuition that comes with it. So you don't need to keep referring back to it, and then somehow that turns into intuition. Right now, I think what these models are doing is the first part. I'll go pick the information. As the context windows of these models also keep getting better, I can stuff more and more information in these models and then get an answer. What intuition is not really understood, so I don't really know how that changes the dynamic. What would change if a model is trained with your data? You clearly need much less data to steer it to a certain direction. You'll have much more consistency in the responses. I don't think you can ever get away from feeding it information, up-to-date information, and so forth. But what I would imagine happen is First of all, that model that you want to do certain tasks doesn't have to write a poem in French. So you'll benefit from using the weights more efficiently for the tasks that you want to do. And therefore, perhaps, again, you may not need as large a model, so you get benefits from more optimizations to reduce the cost, increase the speed, and so forth. Yeah, I think that many small models paradigm is also one that I'm pretty bullish on for quite a few reasons, one being just, I think we stand a lot better chance of staying in control of the meta if we have a lot of like narrow AIs doing their jobs as opposed to, you know, a relatively smaller number of giant AIs like running things for us. The pull of that is obviously pretty strong, but the, I do worry that we're racing into having such general AIs that can do sort of anything. before we've really thought through what the ultimate consequences of that are going to be. And the narrowness, safety through narrowness and maintaining control through narrowness, I think is an underdeveloped paradigm.…

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