Artificial intelligence is commonly touted for its potential to change technology, the workplace, and society at large. But as deep learning, data science, and other technical advances bring certain aspects of AI closer to reality, some of its deepest impacts could come in the world of science.
Today, AI already outperforms humans on specific tasks critical for scientific research, such as detecting small signals in immense noise or understanding complex, high-dimensional systems. Researchers already use forms of AI for drug discovery and the design of new materials, but hope to someday thread it through every level of science, from understanding the cosmic secrets of dark matter to more accurate medical diagnoses.
In late January, several dozen scientists from UChicago, Argonne National Laboratory, Fermilab, and the Toyota Technological Institute at Chicago gathered at John Crerar Library to discuss how to best use these new techniques to fuel discovery, and how to seed the future of AI for science.
“Everybody in this next generation is going to be data native,” said Andrew Ferguson, associate professor of molecular engineering at the University of Chicago. “I think we’re going to approach a point where AI becomes a tool just like the slide rule or the desktop computer became in their ages, it’s just something that the physical scientist uses without thinking about it, it’s just a standard tool in our toolbox.”
The event, organized by the UChicago Office of Research and National Laboratories and the Center for Data and Computing, was intended to establish new collaborations and reduce barriers between the attending institutions on projects that benefit both AI and domain science. While previous partnerships used AI experts as “consultants” for science problems or science data as fodder for independent AI research, workshop organizer Rebecca Willett argued for a more deeply entangled future.
“We’d like to identify projects that will simultaneously lead to new scientific discoveries and also advance our understanding of AI, machine learning, and data science,” said Willett, professor of computer science and statistics at UChicago. “Hopefully, we will together address open questions on both sides, not just one or the other.”