Stuart Russell

So, you know, they've got the classic merging onto the freeway problem where you're kind of racing a vehicle that's already on the freeway and you're going to pull ahead of them or you're going to let them go first and pull in behind.

And you get this sort of uncertainty about who's going first.

So all those kinds of things,

mean that you need a decision-making architecture that's very different from either a rule-based system or it seems to me kind of an end-to-end neural network system.

So just as AlphaGo is pretty good when it doesn't do any look ahead, but it's way, way, way, way better when it does, I think the same is going to be true for driving.

You can have a driving system that's pretty good

when it doesn't do any look ahead, but that's not good enough.

You know, and we've already seen multiple deaths caused by poorly designed machine learning algorithms that don't really understand what they're doing.

You can't pretend that you're invisible, right?

You're the invisible car.

It doesn't work that way.

Let's put it that way.

You have to display a certain amount of resoluteness.

You can't be too tentative.

The solutions then become...

Pretty complicated, right?

You get into game theoretic analyses.

And so at Berkeley now, we're working a lot on this kind of interaction between machines and humans.

And that's exciting.

Yep.