History of Diffusion
History of Diffusion
Podcast Description
How did diffusion models go from an obscure research idea to the foundation of modern generative AI? In History of Diffusion, BCV partner Slater Stich sits down with the researchers who shaped the field—Jascha Sohl-Dickstein, Yang Song, and Sander Dieleman—to trace the origins, breakthroughs, and future of diffusion-based AI.From the 2015 paper that started it all to the latest advancements in generative models, this series unpacks the key ideas, challenges, and moments that led to diffusion’s dominance in AI.Whether you’re deep in machine learning research or just want to understand the tech behind tools like MidJourney, DALL·E, and Stable Diffusion, this is a must-listen.
Podcast Insights
Content Themes
The podcast covers the evolution of diffusion models in AI, including the foundational 2015 paper, key breakthroughs, and the latest advancements in generative models. Specific episode examples address challenges faced in the field and the influence of diffusion processes on tools like MidJourney and DALL·E.
How did diffusion models go from an obscure research idea to the foundation of modern generative AI? In History of Diffusion, BCV partner Slater Stich sits down with the researchers who shaped the field—Jascha Sohl-Dickstein, Yang Song, and Sander Dieleman—to trace the origins, breakthroughs, and future of diffusion-based AI.
From the 2015 paper that started it all to the latest advancements in generative models, this series unpacks the key ideas, challenges, and moments that led to diffusion’s dominance in AI.
Whether you’re deep in machine learning research or just want to understand the tech behind tools like MidJourney, DALL·E, and Stable Diffusion, this is a must-listen.
Before diffusion models powered tools like DALL·E and MidJourney, they were a niche research idea. In this episode, BCV partner Slater Stich talks with Yang Song about the early bets, surprising math, and big breakthroughs that made diffusion the backbone of generative AI—as well as what’s next.
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