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On the Matter of Artificial Neural Networks

“Imagine a major city completely covered by a video surveillance system designed to monitor the every move of its citizens. Now imagine that the system is run by a fast-learning machine intelligence, that’s designed to spot crimes before they even happen. No, this isn’t the dystopian dream of a cyber-punk science fiction author, or the writers of TV show “Person of Interest”. This is Boston, on the US East Coast, and it could soon be many more cities around the world.

In the aftermath of the Boston Marathon Bombings in April of last year, as law enforcement and the world’s media struggled to make sense of the tragedy, the Boston Police Department contacted a company well-known for developing innovative and cutting-edge surveillance technology based on advanced artificial intelligence.

Behavioral Recognition Systems, Inc. (BRS Labs) is a software development company based out of a nondescript office block in Houston Texas, with the motto: “New World. New security.”

Headed by former Secret Service special agent John Frazzini, the company brings a crack team of security gurus to bear on its ambitious artificial intelligence projects. With the heavy traffic of Houston’s West Loop South Freeway churning out fumes and noise just outside, BRS Labs has developed one of the most advanced, and perhaps most sinister, surveillance platforms known to man.

Reason-based analysis

AISight (pronounced “eyesight”), works by using a particular form of reason-based analysis of video footage that promises to change the way humans conduct their surveillance of other humans.

Artificial intelligence is already in use across surveillance networks around the world. At high security sites like prisons, nuclear facilities or government agencies, it’s commonplace for security systems to set up a number of rules-based alerts for their video analytics. So if an object on the screen (a person, or a car, for instance) crosses a designated part of the scene, an alert is passed on to the human operator. The operator surveys the footage, and works out if further action needs to be taken.

This method of detecting suspicious behaviour has a number of drawbacks: it’s labour-intensive for the operators, each rule has to be programmed by a technician, and routinely generates more false positives than anything useful. What’s more, it means you can’t move the camera or change the environment without having to reprogram all your rules.

BRS Labs’ AISight is different because it doesn’t rely on a human programmer to tell it what behaviour is suspicious. It learns that all by itself.

The system enables a machine to monitor is environment, and build up a detailed profile of what can be considered “normal” behaviour. The AI can then determine what kind of behaviour is abnormal, without human pre-programing.

Artificial neural networks….”

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