By training machine learning models with examples of basic science, Miles Cranmer hopes to push the pace of scientific discovery forward.

Physics dazzled Miles Cranmer(opens a new tab) from an early age. His grandfather, a physics professor at the University of Toronto, gave him books on the subject, and his parents took him to open houses at universities near their home in southern Ontario, Canada. The Perimeter Institute for Theoretical Physics was a favorite. “I remember someone talking about infinity when I was super young, and it was so cool to me,” Cranmer said. In high school, he interned at the University of Waterloo’s Institute for Quantum Computing — “the best summer of my life at that point.” Soon he began studying physics as an undergraduate at McGill University.

Then one night during his second year, the 19-year-old Cranmer read an interview with Lee Smolin in Scientific American(opens a new tab) in which the eminent theoretical physicist claimed it would “take generations” to reconcile quantum theory and relativity. “That just tripped something in my brain,” Cranmer said. “I can’t have that — it needs to go faster.” And for him, the only way to speed up the timeline of scientific progress was with artificial intelligence. “That night was a moment where I decided, ‘We have to do AI for science.’” He began studying machine learning, eventually fusing it with his doctoral research in astrophysics at Princeton University.

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