For the last few years, police forces around China have invested heavily to build the world's largest video surveillance and facial recognition system, incorporating more than 170 million cameras so far. In a December test of the dragnet in Guiyang, a city of 4.3 million people in southwest China, a BBC reporter was flagged for arrest within seven minutes of police adding his headshot to a facial recognition database. And in the southeast city of Nanchang, Chinese police say that last month they arrested a suspect wanted for "economic crimes" after a facial recognition system spotted him at a pop concert amidst 60,000 other attendees. These types of stories, combined with reports that computer vision recognizes some types of images more accurately than humans, makes it seem like the Panopticon has officially arrived. In the US alone, 117 million Americans, or roughly one in two US adults, have their picture in a law enforcement facial-recognition database.

Thousands of attendees of the 2017 Champions League final in Cardiff, Wales were mistakenly identified as potential criminals by facial recognition technology used by local law enforcement. According to the Guardian, the South Wales police scanned the crowd of more than 170,000 people who traveled to the nation's capital for the soccer match between Real Madrid and Juventus. The cameras identified 2,470 people as criminals. Having that many potential lawbreakers in attendance might make sense if the event was, say, a convict convention, but seems pretty high for a soccer match. As it turned out, the cameras were a little overly-aggressive in trying to spot some bad guys.

In the ongoing battle against cyber attacks, a man-machine collaboration could offer a new path to security. To keep up with cyber threats, the cybersecurity industry has turned to assistance from unsupervised artificial intelligence systems that operate independently from human analysts. But the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology in Cambridge, Mass., in partnership with the machine-learning startup PatternEx, is offering a fresh approach. Their new program, AI2, draws on what humans and machines each do best: It allows human analysts to build upon the large scale pattern recognition and learning capabilities of artificial intelligence. The industry standard right now is unsupervised machine learning, CSAIL research scientist Kalyan Veeramachaneni, who helped develop the program, says in a phone interview with The Christian Science Monitor.