Andrej Karpathy

And you have your full one minute of video from all the angles and you can run all the neural nets you want.

And they can be very efficient, massive neural nets.

There can be neural nets that can't even run in the car later at test time.

So they can be even more powerful neural nets than what you can eventually deploy.

So you can do anything you want, three-dimensional reconstruction, neural nets, anything you want just to recover that truth.

And then you supervise that truth.

Yeah, so I grew the annotation team at Tesla from basically zero to a thousand while I was there.

That was really interesting.

You know, my background as a PhD student researcher, so growing that kind of organization was pretty crazy.

But yeah, I think it's extremely interesting and part of the design process very much behind the autopilot as to where you use humans.

Humans are very good at certain kinds of annotations.

They're very good, for example, at two-dimensional annotations of images.

They're not good at annotating...

cars over time in three-dimensional space.

Very, very hard.

And so that's why we were very careful to design the tasks that are easy to do for humans versus things that should be left to the offline tracker.

Like maybe the computer will do all the triangulation and three-degree construction, but the human will say exactly these pixels of the image are a car.

Exactly these pixels are a human.

And so co-designing the data annotation pipeline was very much bread and butter was what I was doing daily.

Right.