Justin Johnson
speaker
345 appearances
1 recordings
1 series
first heard Nov 2025
last heard 25 Nov
Justin Johnson’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Nov 2025 with 1.
Appearances
I think the whole history of deep learning is in some sense the the history of scaling up compute.
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 seeing starting to see emerging uses.
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.
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.
involved in the whole announcement uh drama?
No, no, not at all.
But I was uh sort of watching all the ImageNet excitement around Alexnet at that that quarter.
Yeah, pretty much.
I mean, like I 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.
Um, and I was uh I thought I was kind of drifting away from my advisor post PhD, but then when
when we reunited a couple of years later it turned out she was thinking of very similar things.
Yeah, I I I think one is just there is a lot more data in compute generally available.
Um, I think the whole history of deep learning is in some sense the the history of scaling up compute.
Um and if we think about, you know, Alexnet required this jump from CPUs to GPUs, but even from AlexNet to today, we're getting about a thousand times more performance per card um than we had in Alexnet days.
And now it's common to train to train models not just on one GPU but on hundreds or thousands or tens of thousands or even more.
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