There is increasing interest in applying AI methods to problems in the sciences. Particularly in biology, these methods hold the promise of extracting actionable hypotheses from complex data, and developing practically useful predictive models. In this talk, I'll discuss a specific example: the recent progress in predicting a protein's 3D structure from its amino acid sequence, in particular using the model AlphaFold. While the focus will be on how this model works and how it's being applied in the life sciences, I'll also try to touch on the relationship between AlphaFold and protein physics.

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