Sergey Levine

Ultimately, if this stuff is successful, it should be a lot bigger.

And it should have that ability to learn continuously.

It should have the

understanding of the physical world, the common sense, the ability to go in and pull in more information if it needs it.

Like, if I ask you, like, hey, tonight, like, you know, can you make me this type of salad?

Okay, you should, like, figure out what that entails, like, look it up, go and buy the ingredients.

So there's a lot that goes into this.

It requires common sense.

It requires understanding that there are certain edge cases you need to handle intelligently, cases where you need to think harder.

It requires the ability to improve continuously.

It requires understanding safety, being reliable at the right time, being able to fix your mistakes when you do make those mistakes.

So there's a lot more that goes into this.

But the principles there are you need to leverage prior knowledge and you need to have the right representations.

I think it's something where –

It's not going to be a case where we develop everything in the laboratory and then it's done, and then come 20, 30-something, you get a robot in a box.

I think it'll be the same as what we've seen with AI assistance, that once we reach some basic level of competence where the robot is delivering something useful, it'll go out there in the world.

The cool thing is that once it's out there in the world, it can collect experience and leverage that experience to get better.

To me, like what I tend to think about a lot in terms of timelines is not the date when it will be done but the date when it will – when like the flywheel starts basically.

So when does the flywheel start?

I think that could be very soon.