Progress in AI systems often feels cyclical. Every few years, computers can suddenly do something they’ve never been able to do before. “Behold!” the AI true believers proclaim, “the age of artificial general intelligence is at hand!” “Nonsense!” the skeptics say. “Remember self-driving cars?”
The truth usually lies somewhere in between.
We’re in another cycle, this time with generative AI. Media headlines are dominated by news about AI art, but there’s also unprecedented progress in many widely disparate fields. Everything from videos to biology, programming, writing, translation, and more is seeing AI progress at the same incredible pace.
Why is all this happening now?
You may be familiar with the latest happenings in the world of AI. You’ve seen the prize-winning artwork, heard the interviews between dead people, and read about the protein-folding breakthroughs. But these new AI systems aren’t just producing cool demos in research labs. They’re quickly being turned into practical tools and real commercial products that anyone can use.
There’s a reason all of this has come at once. The breakthroughs are all underpinned by a new class of AI models that are more flexible and powerful than anything that has come before. Because they were first used for language tasks like answering questions and writing essays, they’re often known as large language models (LLMs). OpenAI’s GPT3, Google’s BERT, and so on are all LLMs.
But these models are extremely flexible and adaptable. The same mathematical structures have been so useful in computer vision, biology, and more that some researchers have taken to calling them "foundation models" to better articulate their role in modern AI.
Where did these foundation models came from, and how have they broken out beyond language to drive so much of what we see in AI today?