Andrej Karpathy

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.

I think to a very large extent, we went through a number of iterations and we learned a ton about how to create these data sets.

I'm not seeing big open problems.

Originally, when I joined, I was really not sure how this would turn out.

But by the time I left, I was much more secure in actually understanding the philosophy of how to create these data sets.

And I was pretty comfortable with where that was at the time.

Yeah, pixels, I think, are a beautiful sensor, I would say.