The Real Surprise of the Last 3 Years in AI – Dario Amodei

00:00So, we talked 3 years ago. I'm curious in your view, what has been the biggest update of the last 3 years? What has been the biggest difference between what it felt like last 3 years versus now? >> Yeah, I would say actually the underlying technology, like the exponential of the technology, has has gone, broadly speaking, I would say about about as I expected it to go. I mean, there's like plus or minus, you know, a couple. There's plus or minus a year or two here, there's plus or minus a year or two there. I don't know that I would predict the specific direction of code. Um, but but actually when I look at the exponential, it it is roughly what I expected in terms of the march of the models from like, you know, smart

00:39high school student to smart college student to like, you know, beginning to do PhD and professional stuff and in the case of code reaching beyond that. So, you know, the frontier is a little bit uneven. It's roughly what I expected. I will tell you though what the most surprising thing has been. The most surprising thing has been the lack of public recognition of how close we are to the end of the exponential. To me, it is absolutely wild that, you know, you have people, you know, within the bubble and outside the bubble. You know, but but you have people talking about these these, you know, just the same tired old hot button political issues and like, you know, or or or around us we're like

01:19near the end of the exponential. I I I want to understand what that exponential looks like right now because the first question I asked you when we recorded 3 years ago was, you know, what's up with scaling? How what is it work? Um, I have a similar question now, but I feel like it's a more complicated question because at least from the public's point of view Yes. >> 3 years ago there were these, you know, well-known public trends where across many orders of magnitude of computer you could see how the loss improves. And now we have RL scaling and there's no publicly known scaling law for it. It's not even clear what exactly the story is of is it supposed to be teaching the model skills? Is it supposed to be

01:54teaching metalearning? Um, what is the scaling hypothesis at this point? >> Yeah, so so I have actually the same hypothesis that I had even all the way back in 2017. So, in 2017, I think I talked about it last time, but I wrote a doc called the the big blob of compute hypothesis. And and you know, it it wasn't about the scaling of language models in particular. When I when I wrote it, GPT-1 had had just come out, right? So, that was you know, one among many things, right? There was back in those days, there was robotics, people trying to work on reasoning as a separate thing from language models. There was scaling of the kind of RL that happened that you know, kind of happened in AlphaGo, and you know, that that

02:34happened at Dota at OpenAI, and you know, people remember StarCraft at DeepMind, you know, the AlphaStar. Um, so it was written as a more general document. And and the specific thing I said was the following, that and you know, it's it's very you know, Rich Sutton put out the bitter lesson a couple years later, but you know, the the hypothesis is basically the same. So, so what it says is all the cleverness, all the techniques, all all the kind of we need a new method to to do something like that, doesn't matter very much. There only a few things that matter, and I think I listed seven of them. One is like how much raw compute you have. The other is the quantity of data that you have. Then the third is kind of the

03:18quality and distribution of data, right? It needs to be a broad broad distribution of data. The fourth is I think how long you train for. Um, the fifth is you need an objective function that can scale to the moon. So, the pre-training objective function is one such objective function, right? An another objective function is, you know, the the kind of RL objective function that says like you have a goal, you're going to go out and reach the goal. Within that of course, there's objective rewards like, you know, like you see in math and coding, and there's more subjective rewards like you see in RL from human feedback or kind of higher order higher order versions of that. And and then the sixth and seventh were

03:58things around kind of like normalization or conditioning like you know just getting the numerical stability so that kind of the big blob of compute flows in this laminar way instead of instead of running into problems. So that was the hypothesis and this hypothesis I still hold. I I don't think I've seen very much that is not in line with that hypothesis. And so the pre-training scaling laws were one example of what of of of of kind of what we see there. And indeed those have continued going like you know you know I think I think now it's been it's been widely reported like you know we feel good about pre-training like pre-training is continuing to give us gains. What has changed is that now we're also seeing the same thing for RL

04:44right so we're seeing a pre-training phase and then we're seeing like an RL phase on top of that. And with RL it's it's actually just the same like you know even even other companies have have published like you know in some of their in some of their releases have published things that say look you know we train the model on math contests you know AIME or or the kind of other things and you know how well how well the model does is log linear and how long we've trained it. And we see that as well and it's not just math contests it's a wide variety of RL tasks. And so we're seeing the same scaling in RL that we saw for pre-training. If you enjoyed this clip you can watch the full episode here and

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