Ilya Sutskever – We're moving from the age of scaling to the age of research

Dwarkesh Patel (Host) 00:00.000

Yeah.

Ilya Sutskever (Co-founder and Chief Scientist) 00:01.400

The second thing that got a lot of traction is pre-training. Specifically, the recipe of pre-training. I think the current the way people do RL now is maybe um un is undoing the conceptual imprint of pre-training. But pre-training had the property. You do more pre-training and

Ilya Sutskever (Co-founder and Chief Scientist) 00:21.160

the model gets better at everything more or less uniformly.

Dwarkesh Patel (Host) 00:24.880

Yeah.

Ilya Sutskever (Co-founder and Chief Scientist) 00:25.640

General AI. pre-training gives AGI. But the thing that happened with AGI and pre-training is that in some sense they overshot the target. Because by the kind if you think about the term AGI, you will realize and especially in the context of pre-training, you will realize that a

Ilya Sutskever (Co-founder and Chief Scientist) 00:48.600

human being is not an AGI. Because a human being, yes, there is definitely a foundation of skills. A human being A human being lacks a huge amount of knowledge. Instead, we rely on continual learning. We rely on continual learning. And so then when you think about, okay, so

Ilya Sutskever (Co-founder and Chief Scientist) 01:11.160

let's suppose that we achieve success and we produce a safe super some kind of safe super intelligence. The question is, but how do you define it? Where on the curve of continual learning is it going to be? I will produce like um a super intelligent 15-year-old that's very eager

Ilya Sutskever (Co-founder and Chief Scientist) 01:26.440

to go you say, "Okay, I'm going to" They don't know very much at all. The great student, very eager. You go and be a programmer. You go and be a doctor. Go and learn. So you could imagine that the deployment itself will involve some kind of a learning trial and error period.

Ilya Sutskever (Co-founder and Chief Scientist) 01:43.280

It's a process as opposed to you drop the finish thing.

Dwarkesh Patel (Host) 01:47.880

Okay, I I I I see. So you're you're suggesting that the thing you're pointing out with super intelligence is not some finished mind which knows how to do every single job in the economy because the way say the original I think Open AI charter or whatever defines AGI is like it

Dwarkesh Patel (Host) 02:08.920

can do every single job that a every single thing a human can do. You're proposing instead a mind which can learn to do any single every single job.

Ilya Sutskever (Co-founder and Chief Scientist) 02:18.160

Yes.

Dwarkesh Patel (Host) 02:18.640

And that is super intelligence. And then but once you have the learning algorithm it gets deployed into the world the same way a human labor or might join an organization.

Dwarkesh Patel (Host) 02:29.560

And it seems like one of these two things might happen. Maybe neither of these happens. One, this super efficient learning algorithm becomes superhuman, becomes as good as you and potentially even better at the task of ML research. And as a result, the algorithm itself becomes

Dwarkesh Patel (Host) 02:51.000

more and more superhuman. The other is, even if that doesn't happen, if you have a single model, I mean, this is explicit your vision. If you have a single model or instances of a model which are deployed through the economy, doing different jobs, learning how to do those jobs,

Dwarkesh Patel (Host) 03:05.040

continually learning on the job, picking up all the skills that any human could pick up, but actually picking them all up at the same time and then amalgamating the learnings. You basically have a model which functionally becomes super intelligent even without any sort of

Dwarkesh Patel (Host) 03:19.820

recursive self-improvement in software. Right? Because you now have one model that can do every single job in the economy and humans can't merge our minds in the same way. And so do you expect some sort of like intelligence explosion

Dwarkesh Patel (Host) 03:31.460

from broad deployment?

Ilya Sutskever (Co-founder and Chief Scientist) 03:32.900

I think that it is likely that we will have rapid economic growth. I think the broad deployment like there are two arguments you could make which are conflicting. One is that look, if indeed you get once indeed you get to a point where you have an AI that can learn to do things

Ilya Sutskever (Co-founder and Chief Scientist) 04:01.900

quickly and you have many of them, then they will then there will be a strong force to deploy them in the economy unless there will be some kind of a regulation that stops it, which by the way there might be. But I think the idea of very rapid economic growth for some time, I

Ilya Sutskever (Co-founder and Chief Scientist) 04:23.660

think it's very possible from broad deployment. The other question is how rapid it's going to be? So I think this is hard to know because on the one hand, you have this very efficient worker. On the other hand, there is the world is just really big and there's a lot of stuff.

Ilya Sutskever (Co-founder and Chief Scientist) 04:40.500

And that stuff moves at a different speed. But then on the other hand, now the AI could you know, so I think very rapid economic growth is possible. And we will see like all kinds of things like different countries with different rules and the ones which have the friendlier

Ilya Sutskever (Co-founder and Chief Scientist) 04:55.020

rules, the economic growth will be faster. Hard to predict.

Dwarkesh Patel (Host) 04:58.540

Some people in our audience like to read the transcripts instead of listening to the episode. And so we put a ton of effort into making the transcripts read like they are standalone essays. The problem is that If you just transcribe a conversation verbatim using speech to text

Dwarkesh Patel (Host) 05:13.460

model, it'll be full of all kinds of fits and starts and confusing phrasing. We mentioned this problem to Label Box and they asked if they could take a stab. Working with them on this is probably the reason that I'm most excited to recommend Label Box to people. It wasn't just

Dwarkesh Patel (Host) 05:27.460

oh hey tell us what kind of data you need and we'll go get it. They walked us through the entire process from helping us identify what kind of data we needed in the first place to assembling a team of expert aligners to generate it. Even after After we got all the data back,

Dwarkesh Patel (Host) 05:40.540

Labelbox stayed involved. They helped us choose the right base model and set up auto QA on the model's output so that we could tweak and refine it. And now we have a new transcriber tool that we can use for all our episodes moving forward. This is just one example of how

Dwarkesh Patel (Host) 05:55.940

Labelbox meets their customers at the ideas level and partners with them through their entire journey. If you want to learn more or if you want to try out the transcriber tool yourself, go to labelbox.com/dwarkash.