When I first sampled this model I was actually shocked that it works so well.

The pure improvements in performance and text understanding of these models.

Is it possible to derive something like a physical law from such a model. I think a really important

part of this is the fact that these models have been accessible to everyone.

It learns a representation of the world.

Today many it learns a representation of the world.

Today, many people are familiar with text to text and text to image AI models. Think ChatGPT or Mid- Journey.

But what about text to video? Well, several companies are working to make that a reality,

but for many reasons, it's a lot harder. For one, their size. Just think, you'll often find text files in the kilobytes, images, maybe a few

megabytes, but it's not uncommon for high quality video to be in the gigabytes.

Plus, video requires a much more dynamic representation of the world that incorporates the physics of movement, 3D objects, and more.

Imagine the hand challenge in text image, but in this case, it's hand squirt.

But this is not stopping the researchers behind stable video diffusion,

which as of November 21st was released

as a state of the art open source generative video model.

And today you'll get to hear directly from two of the technical researchers behind that model,

Andreas Blatman and Robin Rombach.

Robin, by the way, is also the co-inventor of Stable Stable Image models.

So in today's episode, together with A16Z General Partner,JNEMETA, you'll get to

first hand what makes text a video so much harder.

The challenges like selecting the right data sets that enable realistic representations

of the world, applications where this technology is actually already

...