Chris Lattner

Jeremy Howard.

Yes.

That guy.

Well, he's been pushing for this kind of thing.

He's wanted this for years.

And so, I mean...

the first time, so I met Jeremy pretty early on, but the first time I sat up and I'm like, this guy is ridiculous, is when I was at Google and we were bringing up TPUs and we had a whole team of people and there was this competition called Dawn Bench of who can train ImageNet fastest, right?

And Jeremy and one of his researchers crushed Google by

not through sheer force of the amazing amount of compute and the number of TPUs and stuff like that, that he just decided that progressive imagery sizing was the right way to train the model in fewer epochs faster and make the whole thing go vroom, right?

And I'm like, this guy is incredible.

And so you can say, anyways, come back to where's Mojo coming from?

Chris finally listened to Jeremy.

It's all his fault.

So a lot of AI and AI research ends up being that it has to go fast enough to get scale.

So a lot of people don't actually care about performance, particularly on the research side, until it allows them to have a bigger data set.

And so suddenly now you care about distributed compute and all these exotic HPC.

You don't actually want to know about that.

You just want to be able to do more experiments faster and do so with bigger data sets.

And so Jeremy has been really pushing the limits.

And one of the things I'll say about Jeremy, and there's many things I could say about Jeremy because I'm a fanboy of his, but it fits in his head.