Andrej Karpathy

For example, we're not writing the assembly code because we have compilers, right?

Like compilers will take my highlight language in C and write the assembly code.

Yeah.

So we're abstracting ourselves very, very slowly.

And there's this what I call autonomy slider of like more and more stuff is automated of the stuff that can be automated at any point in time.

And we're doing a bit less and less and raising ourselves in the layer of abstraction over the automation.

Yeah, maybe the way I would put it is humans don't use reinforcement learning is maybe what I, as I've said it all.

I think they do something different, which is, yeah, you experience.

So reinforcement learning is a lot worse than I think the average person thinks.

Reinforcement learning is terrible.

It just so happens that everything that we had before is much worse.

Because previously, we were just imitating people, so it has all these issues.

So in reinforcement learning, say you're working with, you're solving a math problem.

This is very simple.

You're given a math problem, and you're trying to find a solution.

Now, in reinforcement learning, you will try lots of things in parallel first.

So you're given a problem, you try hundreds of things,

different attempts.

And these attempts can be complex, right?

They can be like, oh, let me try this, let me try that, this didn't work, that didn't work, et cetera.