Chris Lattner

And so that's super, super exciting.

Yeah.

So one of the really interesting, um, tensions.

So there's a whole bunch of stuff that goes into that.

I'll pick a random walk through it.

Uh, if you, if you go back and replay the history of machine learning, right.

I mean the brief, the brief, the most recent history of machine learning, because this is, as you know, very deep.

I, I knew Lex when he had an AI podcast.

Right.

Yeah.

Um, so, uh, so if you look at just TensorFlow and PyGeorge, which is pretty recent history in the big picture, right.

But, uh,

Intense flow is all about graphs.

PyTorch, I think, pretty unarguably ended up winning.

And why did it win?

Mostly because of usability.

And the usability of PyTorch is, I think, huge.

And I think, again, that's a huge testament to the power of taking abstract, theoretical, technical concepts and bringing it to the masses.

Now, the challenge with what the TensorFlow versus the PyTorch design points was that TensorFlow is kind of difficult to use for researchers, but it was actually pretty good for deployment.

PyTorch is really good for researchers.