How Multimodal AI Models Work 🤖

Multimodality is the ability of an AI model to work with different types (or "modalities") of data, like text, audio, and images. Multimodality is what allows for a model like GPT-4 to write code given a diagram, and models like DALL-E 3 to generate an image given a description.

In this video, we'll learn about how multimodality works in AI, and the distinction between multimodal models and multimodal interfaces.

Links:

Intro repository: https://github.com/AssemblyAI-Examples/chatgpt-image-interface
Introduction to Diffusion Models: https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/
How DALL-E works: https://www.assemblyai.com/blog/how-dall-e-2-actually-works/
Build your own text-to-image model: https://www.assemblyai.com/blog/minimagen-build-your-own-imagen-text-to-image-model/
How RLHF works: https://www.assemblyai.com/blog/how-rlhf-preference-model-tuning-works-and-how-things-may-go-wrong/

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#MachineLearning #deeplearning

0:00 Writing code with GPT-4
0:31 Generating music with MusicLM
0:48 What is multimodality?
1:15 Fundamental concepts of multimodality
2:30 Representations and meaning
4:00 A problem with multimodality
4:50 Multimodal models vs. multimodal interfaces
6:21 Outro