2022 has seen incredible growth in foundation models — AI models trained on a massive scale — a revolution that began with Google’s BERT in 2018, picked up steam with OpenAI’s GPT-3 in 2020, and entered the zeitgeist with the company’s DALL-E text-to-image generator in early 2021.

The pace has only accelerated this year and moved firmly into the mainstream, thanks to the jaw-dropping text-to-image possibilities of DALL-E 2, Google’s Imagen and Midjourney, as well as the options for computer vision applications from Microsoft’s Florence and the multimodal options from Deep Mind’s Gato.

That turbocharged speed of development, as well as the ethical concerns around model bias that accompany it, is why one year ago, the Stanford Institute for Human-Centered AI founded the Center for Research on Foundation Models (CRFM) and published “On the Opportunities and Risks of Foundation Models” — a report that put a name to this powerful transformation.

“We coined the term ‘foundation models’ because we felt there needed to be a name to cover the importance of this set of technologies,” said Percy Liang, associate professor in computer science at Stanford University and director of the CRFM.