The article discusses how machine learning is being used to calculate the macroscopic world that would emerge from string theory, a theory that posits the existence of tiny, invisible extra dimensions. These calculations have been difficult due to the enormous number of possibilities, but recent advances in artificial intelligence have made it possible to approximate the shapes of the Calabi-Yau manifolds, the objects that resemble loofahs and host quantum fields in string theory. The calculations have been able to reproduce the number of particles in the standard model, but not their specific masses or interactions. The long-term goal is to use these calculations to predict new physical phenomena beyond the standard model. The article also mentions that some physicists are skeptical of the usefulness of string theory and the role that machine learning will play in it.
In the coming weeks, Symmetry will explore the ways scientists are using artificial intelligence to advance particle physics and astrophysics. This series of articles will be written and illustrated entirely by humans.
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