Have you ever wondered how generative AI actually works? Well the short answer is, in exactly the same as way as regular AI!

In this video I break down the state of the art in generative AI Autoregressors and Denoising Diffusion models and explain how this seemingly magical technology is all the result of curve fitting, like the rest of machine learning.

Come learn the differences (and similarities!) between autoregression and diffusion, why these methods are needed to perform generation of complex natural data, and why diffusion models work better for image generation but are not used for text generation.

The following generative models were featured as demos in this video:
Images: Adobe Firefly (https://www.adobe.com/products/firefl...)
Text: ChatGPT (https://chat.openai.com)
Audio: Suno.ai (suno.ai)
Code: Gemini (gemini.google.com/app)
Video: Lumiere (Lumierevideo.github.io)

Chapters:
00:00 Intro to Generative AI
02:40 Why Naïve Generation Doesn't Work
03:52 Autoregression
08:32 Generalized Autoregression
11:43 Denoising Diffusion
14:19 Optimizations
14:30 Reusing Models and Causal Architectures
16:35 Diffusion Models Predict the Noise Instead of the Image
18:19 Conditional Generation
19:08 Classifierfree Guidance