Andrej Karpathy

And there are three properties of it that you need.

You need it to be very large.

You need it to be accurate, no mistakes.

And you need it to be diverse.

You don't want to just have a lot of correct examples of one thing.

You need to really cover the space of possibility as much as you can.

And the more you can cover the space of possible inputs, the better the algorithm will work at the end.

Now, once you have really good data sets that you're collecting, curating, and cleaning, you can train your neural net

on top of that.

So a lot of the work goes into cleaning those data sets now.

As you pointed out, it's probably, it could be, the question is, how do you achieve a ton of, if you want to basically predict in 3D, you need data in 3D to back that up.

So in this video, we have eight videos coming from all the cameras of the system.

And this is what they saw.

And this is the truth of what actually was around.

There was this car, there was this car, this car.

These are the lane line markings.

This is the geometry of the road.

There's a traffic light in this three-dimensional position.

You need the ground truth.

And so the big question that the team was solving, of course, is how do you arrive at that ground truth?