Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon
Episode publishedPodcast: No Priors: Artificial Intellig...No Priors: Artificial Intelligence | Technology | Startups
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Hi, listeners. Welcome back to No Priors. Today, I'm here with Stefano Erman, who is a longtime Stanford professor and now co founder and CEO of Inception. Stefano has a extraordinarily broad body of work around generative modeling, but is especially well known as one of the fathers of diffusion. We talk about his company challenging the large labs and why speed and efficiency are going to be the name of the game in AI over the next few years. Welcome, Stefano. Stefano, thanks so much for being here.
Great to be here.
I would love for us to just start with a little bit of your research background and how you ended up starting your company.
For sure. Yeah. I've been doing research in generative models for like basically my entire career. I started at Stanford in 2014 as an assistant professor, I was working on building generative models. Back then, the the research area was not particularly hot. You know, the the the models were not quite working well. We were still, like, building little generative models over MNIST, and it was, like, a big success if you could generate these grainy images of digits. And it was even hard to publish papers back then on that topic, and you had to justify training a generative model as a way to lea
Did you have like a, besides a curiosity in the area, a personal hope for what the models would do back in 2014 and 'fifteen?
Yeah, I mean, I always felt like that was going to be the that was the right way to think about learning from unlabeled data, that building a generative model is really the right way to make sure you understand the structure in the data. That was kind of like the way I was getting at. I was not even dreaming about the kind of capabilities that these LLMs that we have today could do that. But I was thinking more from a, I think, world models perspective. Like, I was working a lot on images and so thinking about, okay. Like, I have a world model. I can imagine what's gonna happen if I were to st
Yeah, I think that's kind of universally true, though. And sort of walk me through the state of your research and how that that led you to start the company.
Yeah, so I was working on generative models of images, initially working on autoregressive models, which were very slow and kind of like very blurry, and then VAEs and then GANs took over.
Yes.
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