Compute and data underpin most measured AI progress

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 15:01

   right right right right right this is like this is sort of the zoomed out view epoch takes with with everything is a lot of this we think the engine of AI progress is heavily mediated by raw inputs like GPUs, the training compute, like training data. Maybe those are the big ones. And then there's this like factor of the labs come up with algorithmic, like innovations, like research breakthroughs. But we really think like a lot of it is coming from the scale up in the compute and the scale up in the training data.

2. 15:33

   So until you see the data and compute supply chain ramp significantly or discontinuously, to be more precise, you're not likely to see discontinuities on the model side. I think that's right. A big thing we're keeping our eye on, to be clear, is are there AI abilities emerging that could cause a discontinuity on the model side? So this is why people talk about recursive self-improvement, or sometimes they call this a software-only intelligence explosion.

3. 16:10

   And the point of these concepts is, well, sure, right now there's a slow, comparatively slow process for building more GPUs and collecting more human training data and whatnot. But if an AI system got really good on the algorithmic innovation side, could it do more with less or do more with a fixed compute budget? And that could change the dynamics here. So the feedback loop might no longer run through the physical world. It's not clear we see evidence of this yet. I'd say, on balance, we don't really see evidence of this yet. But it's the sort of thing that has spooked the people inside the labs, the AI companies, I mean, saying like, gosh, this could be right around the corner. That's like a bit of the fear. And if that happens, then the pace we're used to, we might get that discontinuity. Have you developed a particular benchmark to

4. 17:09

   identify and track this or is it more the way you look at the benchmarks that we've been talking about, the statistical model that we've been talking about for an indication that, you know, there's some kind of, you know, exponential in the curve and that's, you know, probably one of the likely causes, you know, for that kind of exponential or just continuity? All of the above. We want as many tools in our arsenal as we can for this, so that this STATSI-like acceleration detector is definitely a good tool. But we also sort of get, you know, down in the weeds with some of our specific evaluations and benchmarks saying, I'll give you an example. This is some forthcoming work we have that I'm happy to preview. We take a recent AI research breakthrough or innovation from humans, and we take AI systems. We don't connect

5. 18:13

   them to the internet, and we choose AI systems that were developed and released just before this recent, whatever the recent human innovation is. And we basically say, look, here's the metric that that innovation, the number that it makes go up, it improves this sort of efficiency, whatever, some metric. And we say, AI system, your goal is to improve this metric as much as you can, but we don't tell it the innovation. And so we see, is it capable of replicating that human innovation that was highly relevant for AI research and development. And so far, we don't see them able to do this. There's this nebulous idea called research taste, which is like, where's the good idea? Like, what experiment should I do next? Where should I go hunting for big improvements more than the mundane improvements of just more compute, more data?

6. 19:06

   Where should I look for a breakthrough? And AI systems don't seem great at doing this, at least in an AI R&D context. But they're getting a lot better at a lot of things that are adjacent to this area. So we really do think this is an important, sometimes I almost call it a tripwire. The future is foggy, some foggy landscape. We want to put a tripwire out there so that if AI ever does cross some threshold where it's able to do rapid improvements in AI algorithms themselves, then we want that tripwire to sound. So we go, okay, gosh, watch out. Like maybe something, maybe we're about to see a big improvement, a big, you know, uptick in AI capabilities. And how does this relate to AI's performance on some of the math challenges?