Sergey Levine

And I think there are some decisions to be made.

Like the tradeoff there is the more narrow you scope the thing, the earlier you can get it out into the real world.

So but soon as in like this is something we're already exploring.

We're already trying to figure out like what are like the real things this thing could do that could allow us to start spinning the flywheel.

But I think in terms of like stuff that you would actually care about that you would want to see.

So I don't know.

But I think that single digit years is very realistic.

I'm really hoping it'll be more like one or two before something is like actually out there.

But it's hard to say.

It means that there is a robot that does a thing that you actually care about, that you want done.

And it does so competently enough to like actually do it for real, for real people that want it done.

Well, I think it's actually...

I think it's actually very close to working.

And I am 100% certain that many organizations are working on exactly this.

In fact, arguably, there is already a flywheel in the sense that, not an automated flywheel, but a human loop flywheel where everybody who's deploying an LLM is, of course, going to look at what it's doing.

And it's going to use that to then modify its behavior.

It's complex because it comes back to this question of representations and figuring out the right way to derive supervision signals and ground those supervision signals in the behavior of the system so that it actually improves on what you want.

And I don't think that's like a profoundly impossible problem.

It's just something where the details get like pretty gnarly.

and challenges with algorithms and stability become pretty complex.