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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 - Auto-regressors 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 auto-regression 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/firefly.html) Text: ChatGPT (https://chat.openai.com) Audio: Suno.ai (suno.ai) Code: Gemini (gemini.google.com/app) Video: Lumiere (Lumiere-video.github.io) Chapters: 00:00 Intro to Generative AI 02:40 Why Naïve Generation Doesn't Work 03:52 Auto-regression 08:32 Generalized Auto-regression 11:43 Denoising Diffusion 14:19 Optimizations 14:30 Re-using Models and Causal Architectures 16:35 Diffusion Models Predict the Noise Instead of the Image 18:19 Conditional Generation 19:08 Classifier-free Guidance