Andrej Karpathy

So it takes these videos now and makes those predictions.

And so you're sort of just like putting more and more power into the neural network processing.

And at the end of it, the eventual sort of goal is to have most of the software potentially be in the 2.0 land because it works significantly better.

Humans are just not very good at writing software, basically.

I would say by far in the industry, if you're talking about the industry and what is the technology of what we have available, everything is supervised learning.

So you need a data set of input, desired output, and you need lots of it.

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.