Background

The past few years have seen incredible progress in the text-to-image domain. Models like DALL-E 2, Imagen, and Stable Diffusion can generate extremely high quality, high resolution images given only a sentence of what should be depicted in the image.

These models rely heavily on Diffusion Models (DMs). DMs are a relatively new type of Deep Learning method that is inspired by physics. They learn how to start with random "TV static", and then progressively "denoise" it to generate an image. While DMs are very powerful and can create amazing images, they are relatively slow.

Researchers at MIT have recently unveiled a new image generation model that also is inspired by physics. While DMs pull from thermodynamics, the new models, PFGMs, pull from electrodynamics. They treat the data points as charged particles, and generate data by moving particles along the electric field generated by the data.

Why they matter

PFGMs (Poisson Flow Generative Models) constitute an exciting area of research for many reasons, but in particular they have been shown to be 10-20x faster than DMs with comparable image quality

Topics of discussion

1. With the barrier to generate images getting lower and lower (stable diffusion was released only a couple of months ago!), how will the ability for anyone to create high quality images and art affect the economy and what we perceive to be valuable art in the coming years? Short term, medium term, and long term
2. DMs and PFGMs are both inspired by physics. Machine Learning and Deep Learning especially have been integrating concepts from high level math and physics over the past several years. How will the research in these well-developed domains inform the development of Deep Learning? In particular, will leveraging hundreds of years of scientific knowledge and mathematics research be at the foundation of an intelligence explosion?
3. Even someone casually interested in AI will have noticed the incredible progress made in the last couple of years. While new models like this are great and advance our understanding, what responsibility do researchers, governments, and private entities have to ensure AI research is being done safely? Or, should we intentionally not be attempting to build any guard rails to begin with?