AI Crash Course

Ubiquity Extended Team member Amol Kapoor begins this Intro to AI Series with an explanation of 3 foundational concepts of machine learning: features, embeddings, and losses.

Ubiquity Extended Team member Amol Kapoor explains how LLMs (large language models) can be thought of a higher level programming language (vs. C, C++, etc). Voyager is an agent using multiple LLMs that can play Minecraft given plain English direction.

Ubiquity Extended Team member Amol Kapoor continues this Intro to AI Series with an explanation of the CLIP model for guided image generation. This model generates an embedding space where images and descriptive text are near one another.

Ubiquity Extended Team member Amol Kapoor explains how the ML model Stable Diffusion works by training a model to successively remove noise from an image. Now, when you feed in new noisy images along with a text prompt, you get a polished image as output.

Ubiquity Extended Team member Amol Kapoor explains now the machine learning model NeRF is able to predict views of a 3-dimensional view given a set of 2-dimensional views of a scene or object.

Ubiquity Extended Team member Amol Kapoor explains how GPT and Large Language Models (LLMs) work by outputting probabilities of what the next most likely word is using the concept of "attention".