Sergey Levine

Yeah.

And its perception is in service to fulfilling that purpose.

And that is like a really great focusing factor.

We know that for people this really matters.

Like literally what you see is affected by what you're trying to do.

Like there's been no shortage of psychology experiments showing that people have like almost a shocking degree of tunnel vision where they will like literally not see things right in front of their eyes if it's not relevant to what they're trying to achieve.

Yeah.

And that is tremendously powerful.

There must be a reason why people do that because certainly if you're out in the jungle, seeing more is better than seeing less.

So if you have that powerful focusing mechanism, it must be darn important for getting you to achieve your goal.

And I think robots will have that focusing mechanism because they're trying to achieve a goal.

Well, let me put it this way.

Like let's say that I gave you lots of videotapes or lots of recordings of different sporting events and gave you a year to just watch sports.

And then after that year, I told you, okay, now your job, you're going to be playing tennis.

Okay, that's pretty dumb, right?

Whereas if I told you first, you're going to be playing tennis, and then I let you study up, right?

Now you really know what you're looking for.

So I think that actually...

There's a very real challenge here.

I don't want to understate the challenge, but I do think that there's also a lot of potential for foundation models that are embodied, that learn from interaction, from controlling robotic systems, to actually be better at absorbing the other data sources because they know what they're trying to do.