Paul Christiano: prediction

Transcript context

…How fast do you think the pace of algorithmic advances will be? Because if by 2040, even if scaling fails since 2012, since the beginning of the deep learning revolution, we've had so many new things by 2040, are you expecting a similar pace of increases? And if so, then if we just keep having things like this, then aren't we going to just going to get the AI sooner or later? Or sooner? Not later. Aren't we going to get the AI sooner or sooner? I'm with you on sooner or later. Yeah, I suspect progress to slow. If you held fixed how many people working in the field, I would expect progress to slow as low hanging fruit is exhausted. I think the rapid rate of progress in, say, language modeling over the last 4 years is largely sustained by, like, you start from a relatively small amount of investment, you greatly scale up the amount of investment, and that enables you to keep picking. Every time the difficulty doubles, you just double the size of the field. I think that dynamic can hold up for some time longer. Right now, if you think of it as, like, hundreds of people effectively searching for things up from, like, you know, anyway, if you think of it hundreds of people now you can maybe bring that up to like, tens of thousands of people or something. So for a while, you can just continue increasing the size of the field and search harder and harder. And there is indeed a huge amount of low hanging fruit where it wouldn't be a hard for a person to sit around and make things a couple of percent better after after year of work or whatever. So I don't know. I would probably think of it mostly in terms of how much can investment be expanded and try and guess some combination of fitting that curve and some combination of fitting the curve to historical progress, looking at how much low hanging fruit there is, getting a sense of how fast it decays. I think you probably get a lot, though. You get a bunch of orders of magnitude of total, especially if you ask how good is a GPT five scale model or GPT 4 scale model? I think you probably get like, by 2040, like, I don't know, 3 orders of magnitude of effective training compute improvement or like, a good chunk of effective training compute improvement, 4 orders of magnitude. I don't know. I don't have, like here I'm speaking from no private information about the last couple of years of efficiency improvements. And so people who are on the ground will have better senses of exactly how rapid returns are and so on. Okay, let me back up and ask a question more generally about people. Make these analogies about humans were trained by evolution and were deployed in the modern civilization. Do you buy those analogies? Is it valid to say that humans were trained by evolution rather than I mean, if you look at the protein coding size of the genome, it's like 50 megabytes or something. And then what part of that is for the brain anyways? How do you think about how much information is in? Do you think of the genome as a hyperparameters? Or how much does that inform you when you have these anchors for how much training humans get when they're just consuming information, when they're walking up and about and so on?…

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