“You’ve graduated from the school of spectral hard knocks,” Paul Tilghman, a U.S. Defense Advanced Research Projects Agency (DARPA) program manager, told the teams competing in the agency’s Spectrum Collaboration Challenge (SC2) finale on 23 October. The three-year competition had just concluded, and the top three teams were being called on stage as a song that sounded vaguely like “Pomp and Circumstance” played overhead.

“Hard knocks” wasn’t an exaggeration—the 10 teams that made it to the finale, as well as others who were eliminated in earlier rounds of the competition—had been tasked with proving something that hadn’t been demonstrated before. Their challenge was to see if AI-managed radio systems could work together to share wireless spectrum more effectively than static, pre-allocated bands. They had spent years battling it out in match-ups in a specially-built RF emulator DARPA built for the competition, Colosseum.

By the end of the finale, the top teams had demonstrated their systems could transmit more data over less spectrum than existing standards like LTE, and shown an impressive ability to reuse spectrum over multiple radios. In some finale match-ups, the radio systems of five teams were transmitting over 200 or 300 percent more data than is currently possible with today’s rigid spectrum band allocations. And that’s important, given that we’re facing a looming wireless spectrum crunch.

Facebook remains embroiled in a multibillion-dollar judgement lawsuit over its facial recognition practices, but that hasn’t stopped its artificial intelligence research division from developing technology to combat the very misdeeds of which the company is accused. According to VentureBeat, Facebook AI Research (FAIR) has developed a state-of-the-art “de-identification” system that works on video, including even live video. It works by altering key facial features of a video subject in real time using machine learning, to trick a facial recognition system into improperly identifying the subject.

This de-identification technology has existed in the past and there are entire companies, like Israeli AI and privacy firm D-ID, dedicated to providing it for still images. There’s also a whole category of facial recognition fooling imagery you can wear yourself, called adversarial examples, that work by exploiting weaknesses in how computer vision software has been trained to identify certain characteristics. Take for instance this pair of sunglasses with an adversarial pattern printed onto it that can make a facial recognition system think you’re actress Milla Jovovich.