(gentle music) - So the magic of deep learning is that it gets better with data. You said there's a huge inflow of data, but the thing about driving, the really valuable data to learn from is the edge cases. So how do you, I mean, I've heard you talk somewhere about autopilot disengagement being an important moment of time to use.

Is there other edge cases, or perhaps can you speak to those edge cases, what aspects of them might be valuable, or if you have other ideas, how to discover more and more and more edge cases in driving? - Well, there's a lot of things that are learned. There are certainly edge cases where, I say somebody's on autopilot and they take over, and then, okay, that's a trigger that goes to a system that says, okay, did they take over for convenience, or did they take over because the autopilot wasn't working properly?

There's also, like let's say we're trying to figure out what is the optimal spline for traversing an intersection. Then the ones where there are no interventions are the right ones. So you then say, okay, when it looks like this, do the following. And then you get the optimal spline for navigating a complex intersection.

- So that's for, so there's kind of the common case. You're trying to capture a huge amount of samples of a particular intersection, when things went right, and then there's the edge case where, as you said, not for convenience, but something didn't go exactly right. - Somebody took over, somebody asserted manual control from autopilot.

And really, like the way to look at this is view all input as error. If the user had to do input, if there's something, all input is error. - That's a powerful line to think of it that way. (upbeat music) (upbeat music) (upbeat music) (upbeat music) (upbeat music) (upbeat music)