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AI Foundation Models

(Stanford University - Jaclyn Chen)

- Overview

AI Foundation models are a type of machine learning (ML) model that can perform a variety of tasks and applications. They are large AI models that can generate a wide range of outputs, including text, images, or audio. Foundation models can be standalone systems or used as a base for other applications. 

Foundation models are a form of generative AI that can generate output from one or more inputs in the form of human language instructions. They are based on complex neural networks, including generative adversarial networks (GANs), transformers, and variational encoders. 

Foundation models are trained on large, unlabeled datasets and fine-tuned for an array of applications. They are pre-trained with extremely large data sets scraped from the public internet. 

The term "foundation model" was coined in August 2021 by the Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM).


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