From the source
AutoDiscovery, our AI agent for scientific research, analyzes datasets, proposes hypotheses, runs experiments to test them, and ranks the results by Bayesian surprise—the gap between what the model predicted and what the data showed.
In a University of Washington classroom this spring, it surfaced a possible flaw in how battery aging is measured, an unexpected pattern in silicon polymers, and a recurring feature in proteins that respond to light.
But a surprising result is not necessarily an important one—or one that will withstand further testing.
Students still had to scrutinize the evidence, compare the findings with published research, and decide whether each result reflected a genuine discovery, a coincidence, or a problem with the data.
Luna Yue Huang, an associate teaching professor of materials science and engineering at the UW, invited students to explore this way of working with AI through the GenAI for Science: Ai2–UW Materials Challenge .
Twenty-five teams proposed a dataset and research question; ten were selected to spend several weeks working with AutoDiscovery.
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