I think the whole history of deep learning is in some sense the history of scaling up compute.

When I graduated from grad school, I really thought the rest of my entire career would be towards solving that single problem, which is...

A lot of AI as a field, as a discipline, is inspired by human intelligence.

We thought we were the first people doing it.

It turned out that

was also simultaneously doing it.

So Marble, like basically one way of looking at it,

it's a generative model of 3D worlds, right?

So you can input things like text or image or multiple images

and it will generate for you a 3D world

that kind of matches those inputs.

So while Marvel is simultaneously a world model that is building towards this vision of spatial

intelligence, it was also very intentionally designed to be a thing that people could find

useful today. And we're starting to see emerging use cases in gaming, in BFX, in film, where I think there's

a lot of really interesting stuff that Marvel can do today as a product, and then also

set a foundation for the grand world models that we want to build going into the future.

Fay-Fei Lee is a Stanford professor, the co-director of the Stanford Institute for Human-Centured

Artificial Intelligence, and co-founder of World Labs.

She created ImageNet, the data set that sparked the deep learning revolution.

Justin Johnson is her former PhD student, ex-professor at Michigan, ex-Meta research, and now

co-founder of World Labs.

Together, they just launched Marvel,

...