On October 8 the Nobel Prize in Physics was awarded for the development of machine learning. The next day, the chemistry Nobel honored protein structure prediction via artificial intelligence. Reaction to this AI–double whammy might have registered on the Richter scale.
Some argued that the physics prize, in particular, was not physics. “A.I. is coming for science, too,” the New York Times concluded. Less moderate commenters went further: “Physics is now officially finished,” one onlooker declared on X (formerly Twitter). Future physics and chemistry prizes, a physicist joked, would inevitably be awarded to advances in machine learning. In a laconic email to the AP, newly anointed physics laureate and AI pioneer Geoffrey Hinton issued his own prognostication: “Neural networks are the future.”
For decades, AI research was a relatively fringe domain of computer science. Its proponents often trafficked in prophetic predictions that AI would eventually bring about the dawn of superhuman intelligence. Suddenly, within the past few years, those visions have become vivid. The advent of large language models with powerful generative capabilities has led to speculation about encroachment on all branches of human achievement. AIs can receive a prompt, spit out illustrated pictures, essays, solutions to complex math problems—and now, provide Nobel-winning discoveries. Have AIs taken over the science Nobels, and possibly science itself?
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