Large language models aren't just powering chatbots like ChatGPT. This type of computational model is an example of a particular flavor of artificial intelligence known as foundation models, which are trained on vast amounts of data to make inferences in new areas. Although text is one rich data source, science offers many more from biology, chemistry, physics and more. Such models open up a tantalizing new set of research questions. How effective are foundation models for science? How could they be improved? Could they help researchers work on challenging questions? And what might they mean for the future of science?

This episode begins a series where we'll explore these questions and more, talking with computational scientists about their work with foundation models and the opportunities and challenges in this exciting, rapidly changing area of research. We'll start by talking with Ian Foster of Argonne National Laboratory and the University of Chicago about AuroraGPT, a foundation model being developed for science and named for Argonne's new exascale computer.

You'll meet:

Ian Foster is a senior scientist at Argonne National Laboratory where he directs the data science and learning division. He’s also a professor of computer science at the University of Chicago. He is the co-leader of the data team for Argonne's AuroraGPT project.