Andrej Karpathy

There's a lot that goes there at the, at the full tail of, you know, the, the expansion of the knives that we have to be comfortable with it eventually.

Yeah.

The final problems are of that form.

I don't think those are the problems that are very common.

I think eventually they're important, but it's like really in the tail end.

Well, basically, the sensor is extremely powerful, but you still need to process that information.

And so going from brightnesses of these pixel values to, hey, here are the three-dimensional world is extremely hard.

And that's what the neural networks are fundamentally doing.

And so the difficulty really is in just doing an extremely good job of engineering the entire pipeline.

the entire data engine, having the capacity to train these neural nets, having the ability to evaluate the system and iterate on it.

So I would say just doing this in production at scale is like the hard part.

It's an execution problem.

Yeah, for the neural net specifically, just making sure everything fits into the chip on the car.

And you have a finite budget of flops that you can perform and memory bandwidth and other constraints.

And you have to make sure it flies.

And you can squeeze in as much compute as you can into the tiny...

Yeah, I'm not sure if there's too many insights.

You're trying to create a neural net that will fit in what you have available, and you're always trying to optimize it.

And we talked a lot about it on AI Day and basically the triple backflips that the team is doing to make sure it all fits and utilizes the engine.

So I think it's extremely good engineering.