Chris Lattner

So do you know of any algorithms that are good at searching very complicated spaces for-?

Don't tell me you're going to turn this into a machine learning problem.

So then you turn it into a machine learning problem and then you have a space of genetic algorithms and reinforcement learning and like all these- But can you include that into the stack, into the modular stack?

So you start from simple and predictable models.

And so you can have full control, and you can have coarse-grained knobs that nudge the system so you don't have to do this.

But if you really care about getting the last ounce out of a problem, then you can use additional tools.

And there, the cool thing is you don't want to do this every time you run a model.

You want to figure out the right answer and then cache it.

Once you do that, you can say, okay, cool, I can get up and running very quickly.

I can get good execution out of my system.

I can decide if something's important, and if it's important, I can go throw a bunch of machines at it and do a big expensive search over the space using whatever technique I feel like.

It's really up to the problem.

And then when I get the right answer, cool, I can just start using it.

And so you can get out of this, this trade-off between, okay, am I going to like spend forever doing a thing or do I get up and running quickly?

And as a quality result, like these, these are actually not in contention with each other if the system's designed to scale.

So, I mean, if you just look at the Python problem, right, you can say, how do I make Python faster?

And there's been a lot of people that have been working on the, okay, I'm going to make Python 2x faster, 10x faster, or something like that.

And there have been a ton of projects in that vein.

Mojo started from the, what can the hardware do?

What is the limit of physics?