String theory plus AI

“It’s good that people do this machine learning business, because I’m sure we will need it at some point,” Van Riet said. But first “we need to think about the underlying principles, the patterns. What they’re asking about is the details.”

Plenty of physicists have moved on from string theory to pursue other theories of quantum gravity. And the recent machine learning developments are unlikely to bring them back. Renate Loll, a physicist at Radboud University in the Netherlands, said that to truly impress, string theorists will need to predict — and confirm — new physical phenomena beyond the Standard Model. “It is a needle-in-a-haystack search, and I am not sure what we would learn from it even if there was convincing, quantitative evidence that it is possible” to reproduce the Standard Model, she said. “To make it interesting, there should be some new physical predictions.”

New predictions are indeed the ultimate goal of many of the machine learners. They hope that string theory will prove rather rigid, in the sense that doughnuts matching our universe will have commonalities. These doughnuts might, for instance, all contain a kind of novel particle that could serve as a target for experiments. For now, though, that’s purely aspirational, and it might not pan out.

“String theory is spectacular. Many string theorists are wonderful. But the track record for qualitatively correct statements about the universe is really garbage,” said Nima Arkani-Hamed, a theoretical physicist at the Institute for Advanced Study in Princeton, New Jersey.

Ultimately, the question of what string theory predicts remains open. Now that string theorists are leveraging the power of neural networks to connect the 6D microworlds of strings with the 4D macroworlds of particles, they stand a better chance of someday answering it.