Division of Science, Mathematics, and Computing Presents

Five Centuries of Reductive Science vs. Artificial Intelligence: A Seminar Modeled After a Bard Common Course

Since Galileo, the goal of scientific understanding is to explain complex phenomena with a compact description, a model. Yet today, artificial intelligence –specifically, machine-learning using neural nets– has engendered a radical departure from traditional approaches.  Machine-learning using neural nets is not grounded in a unifying theory. There are no hypotheses being tested. Instead, the goal is to find parameters (often billions of them) that can capture the phenomenon under consideration and to then utilize the parameters predictively. This approach has met with stunning success in multiple venues, but it is no longer science as we have come to know it.

Where do we go from here? In this talk, George D. Rose will address this question using the protein folding problem as an example.

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For more information, call 845-758-6822, or e-mail [email protected].

Time: 3:00 pm EDT/GMT-4

Location: Reem-Kayden Center Laszlo Z. Bito '60 Auditorium

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