It’s not clear which is more challenging: constructing AIs that can collaboratively learn to manage radio spectrum more effectively than humans can, or presenting the results in a way that isn’t snooze-inducing. Yet both were accomplished during Phase 2 of the U.S. Defense Advanced Research Projects Agency’s (DARPA) Spectrum Collaboration Challenge (SC2), held on 12 December at the Johns Hopkins University Applied Physics Lab.

For three hours, presenters cracked jokes and gave a play-by-play rundown of test results, complete with hand-drawn circles and arrows, that would have made any sportscaster proud. While watching replays of the most high-stakes tests, the audience gasped and cheered and shouted encouragement at the AI programs.

AI-managed spectrum allocation might not seem important enough to celebrate with such zeal. But bandwidth is a finite resource, and engineers are searching for ways to squeeze as many bits as possible out of every hertz. One way to avoid a spectrum crunch is to teach machines to be better at managing bandwidth than humans could ever hope to be. That idea is important enough that DARPA has dedicated a Grand Challenge to it, which completed its Phase 1 competition in December 2017.