Shawn Wang: belief

Transcript context

…And also higher levels of abstraction give you other powers. And one of the things I think is really exciting about deep learning systems and things like what Modular is building is that it has raised compute to this graph level. Once you have gotten things out of for loops and semicolons and, you know, out of the muck and into something that's more declarative, well, now you can do things where you transform the compute. This is something that I think that many people don't yet realize because it's kind of possible, but it's really such a pain with these existing systems is that, you know, a lot of the power of what this abstraction provides is the ability to do things like Pmap and Vmap, like where you're taking a computation and then transforming it. And one of the things I was very inspired by my time at Google is, you know, we started out with these very low level things and, you know, single node GPU machines and then clusters and then async programming, like all this very little stuff. And by the time I had left, we had had, you know, researchers in Jupyter Notebook training petaflop supercomputers. You just think about that. That is an enormous lift in terms of the tech. And that was made possible by a lot of very layered and well-architected systems, by a lot of, you know, novel HPC type hardware, by a lot of these breakthroughs that had happened. And so what I'd love to see is for that technology to get even more widely adopted, generalized and get out there and also kind of break down a lot of the complexity that got built up along the way. Beautiful. You use very precise terms, AI engine, AI framework, AI compiler. And I think that means special things for you, especially within the modular context. Do you care to define them so we can have context for the rest of the conversation? Yeah, absolutely. That's a great point. When I think about framework, I'm usually talking about things like TensorFlow and PyTorch. These are things that, you know, most people building a model will use something like PyTorch to build it and train it and do things like that. Underneath that, you end up getting a whole bunch of ways to talk to the hardware. And often it's CUDA or Intel MKL or something like this. And so those things are the engine. And that interface of the hardware is generally what I think of when I talk about an engine.…

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