Diffusion
topic on 4 shows · 5 statements across 5 episodes
American Optimist 
Latent Space 
the MAD Podcast 
TBPN
5 statements about Diffusion, every show
Future long-form video generation will likely hybridize autoregressive and diffusion techniques
“So if we think about the architecture that's going to get us there to these longer, richer sequences, it's probably, like you said, going to be a mix of the auto aggressive and the diffusion working together to do what each piece is good at.”
Levie: Real-World Diffusion, Not Intelligence, Is AI's Ultimate Rate Limiter
“Diffusion actually is, like, your biggest rate limiter, and diffusion is, like, a human, Has to get the intelligence from the model plus probably their data, and then they have to go interact with the real world and do something in the real world, and then bri…”
Ethan He: LLM Video Agents Will Orchestrate Diffusion Models and Editing Tools
“Video agents, mostly language models, they'll call these generative model, either it's a separate model or a diffusion head or whatever as tool. So this model can iteratively Refine the results or even like you generate longer content through a very long trend…”
Hotz: Shift to diffusion AI models will erode centralized cloud moats
“With AI, I think there's going to be a much less of a moat. Especially when you look at the move from autoregression to diffusion. So autoregression can run in large batch sizes. When you run ChatGPT, you're running with a whole bunch of other people on that s…”
Howard: Non-autoregressive AI architectures will deliver major performance gains
“But I think it probably will work. And I think that'll probably be a significant jump in performance when you can sketch out the entirety of the solution first, and then fill in the blocks and gradually increasing levels of specificity.”