Sergey Levine

Like, you know, your sensory stream is extremely temporally correlated, which means that the marginal information gained from each additional observation is not the same as the entirety of that observation.

because the image that I'm seeing now is very correlated to the image I saw before.

So in principle, if I want to represent it concisely, I could get away with a much more compressed representation than if I represent the images independently.

So there's a lot that can be done on the algorithm side to get this right, and that's really interesting algorithms work.

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

To be truthful, like, I haven't gotten to the systems problem because, you know, you want to implement the system once you sort of know the shape of the machine learning solution.

But I think there's a lot of cool stuff to do there.

I don't know.

But if I were to guess, I would guess that we'll actually see both.

That we'll see low-cost systems with off-board inference and more reliable systems, for example, in settings where, like if you have an outdoor robot or something where you can't rely on connectivity, that are costlier and have on-board inference.

A few things I'll say...

from a technical standpoint, that might contribute to understanding this.

While a real-time system obviously needs to be controlled in real time, often at high frequency, the amount of thinking you actually need to do for every time step might be surprisingly low.

And again, we see this in humans and animals.

When we...

plan out movements, there is definitely a real planning process that happens in the brain.

If you record from a monkey brain, you will actually find neural correlates of planning.

And there is something that happens in advance of a movement, and when that movement actually takes place, the shape of the movement correlates with what happened before the movement.

Like that's planning, right?

So that means that you put something in place and, you know, set the initial conditions of some kind of process and then unroll that process and that's the movement.