Sergey Levine

That's what makes them so great.

So I don't think that actually...

Mathematically, this this like highly parallel thing where you're doing perception and proprioception and planning all at the same time is actually actually necessarily needs to look that different from a transfer, although its practical implementation will be different.

And you could imagine that the system will in parallel think about

okay, here's like my long-term memory, like here's what I've seen, you know, a decade ago.

Here's my short-term kind of spatial stuff.

Here's my semantic stuff.

Here's what I'm seeing now.

Here's what I'm planning.

And all of that can be implemented in a way that there's some, you know, very familiar kind of attentional mechanism, but in practice, all running in parallel, maybe at different rates, maybe with the more complex things running slower, the faster reactive stuff running faster.

I think there are a lot of things to this question.

I think certainly there's like a really fascinating systems problem.

I'm by no means a systems expert, but I would imagine that the right architecture in practice, especially if you want an affordable low-cost system, would be to externalize at least part of the thinking.

Uh, you know, you could imagine maybe in the future you'll have a robot that has like, uh, you know, if your internet connection is not very good, the robot is in kind of like a dumber reactive mode.

But if you have a good internet connection, then it can like be a little smarter.

Right.

It's pretty cool.

Um, but I think there is, there are also research and algorithms, things that can help here.

Um,

Like figuring out the right representations, concisely representing both your past observations, but also changes in observation, right?