What Comes After ChatGPT? The Mother of ImageNet Predicts The Future
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What is Marble and how does it generate 3D worlds from text or images?
I think the whole history of deep learning is in some sense the the history of scaling up compute.
When I graduated from grad school I really thought the l 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 the system, 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 Marble 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. Um and we're see starting to see emerging use cases. um fr in gaming, in VFX, um in 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 for the for the grand world models that we want to build going into the future.
Feife Lee is a Stanford professor, the co-director of the Stanford Institute for Human-Centered 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, the first model that generates explorable 3D worlds from texture images. In this episode, Beifei and Justin explore why spatial intelligence is fundamentally different from language. What's missing from current world models? Hit physics. and the architectural insight that transformers are actually set models, not sequence models.
Hey everyone, welcome to the Laden Space Podcast. This is Alestio, founder of Kernel Labs, and I'm joined by Swix, editor of Ladin Space.
And we are so excited to be in the studio with Feife and Justin of uh World Labs. Welcome.
We're excited too. I normally
say in Marble. Yeah, thanks for having us. I think there's a lot of interest in world models and you've done a you've done a little bit of publicity around spatial intelligence and all that. Um, I guess maybe one of the part of the story that is a rare opportunity to for you to tell is how you two came together uh to start building world labs.
That's very easy because Justin was my former student. Yeah. So Justin came to my I you know, uh in my the other hat I wear is a professor of computer science at Stanford. Justin joined my lab when? Which year?
Uh twenty twelve. Actually the the semester that I uh the quarter that I joined your lab was the same quarter that that uh Alexnet came out.
Yeah, yeah. So Justin is uh my first time Were you
involved in the whole announcement uh drama? No, no, not at all. But I was sort of watching all the ImageNet excitement around Alexnet at that that quarter.
So he was my one of my very best students and uh and then he went on to have a very successful uh early career as a professor in Michigan, University of Michigan and Arbor and Meta. And then when we um I think around You know, more than two years ago for sure. I think both independently, both of us have been looking at the development of the large models and thinking about what's beyond language models and and this idea of building world models, spatial intelligence. uh really was natural for us. So we started talking and decided that we should just put all the eggs in one basket and focus on the solving this problem and started warlaps together.
Yeah, pretty much. I mean, like I after that seeing that kind of ImageNet era during my PhD, um, I had the sense that the next sort of decade of computer vision was going to be about getting getting AI out of the out of the data center and out into the world. Um, so a lot of my interests post PhD kind of shifted in uh to into 3D vision, a little bit more into into computer graphics, uh, more into generative modeling.
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Chapters
5 chapters
1
What is Marble and how does it generate 3D worlds from text or images?
0:00–5:08
2
How did Fei‑Fei Li’s ImageNet work lead to the idea of spatial intelligence?
5:08–7:28
3
Why is physics considered the missing piece in today’s world models?
7:28–20:59
4
How does the rapid scaling of compute change the path toward spatial AI?
20:59–54:51
5
Why do transformers model sets rather than sequences, and what does that mean for future architectures?
54:51–1:01:49
Speakers
3 identifiedMore from The a16z Show
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