Machine learning makes it possible to generate more data than mathematician can in a lifetime

For the first time, mathematicians have partnered with artificial intelligence to suggest and prove new mathematical theorems. While computers have long been used to generate data for mathematicians, the task of identifying interesting patterns has relied mainly on the intuition of the mathematicians themselves. However, it's now possible to generate more data than any mathematician can reasonably expect to study in a lifetime. Which is where machine learning comes in.

Two separate groups of mathematicians worked alongside DeepMind, a branch of Alphabet, Google's parent company, dedicated to the development of advanced artificial intelligence systems. András Juhász and Marc Lackenby of the University of Oxford taught DeepMind's machine learning models to look for patterns in geometric objects called knots. The models detected connections that Juhász and Lackenby elaborated to bridge two areas of knot theory that mathematicians had long speculated should be related. In separate work, Williamson used machine learning to refine an old conjecture that connects graphs and polynomials.

András Juhász and Marc Lackenby of the University of Oxford taught DeepMind's machine learning models to look for patterns in geometric objects called knots. The models detected connections that Juhász and Lackenby elaborated to bridge two areas of knot theory that mathematicians had long speculated should be related. In separate work, Williamson used machine learning to refine an old conjecture that connects graphs and polynomials.

"The most amazing thing about this work and it really is a big breakthrough is the fact that all the pieces came together and that these people worked as a team," said Radmila Sazdanovic of North Carolina State University. 

Some observers, however, view the collaboration as less of a sea change in the way mathematical research is conducted. While the computers pointed the mathematicians toward a range of possible relationships, the mathematicians themselves needed to identify the ones worth exploring.