Simanaitis Says

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SEEKING ANONYMITY IN A CROWD PART 1

IT’S LIKE LIVING IN a small town, where everyone knows everything about everyone else. Or, at least, they think they do. Modern surveillance technology recognizes the face in the crowd. But is it possible to retain anonymity?

John Seabrook’s “Dressing for the Surveillance Age” discusses this online in The New Yorker, March 9, 2020 (and its March 19, 2020, print edition as “Adversarial Man.”). Here, in Parts 1 and 2 today and tomorrow, are tidbits about this fine article, together with links to artificial intelligence, machine learning, facial recognition, and surveillance touched on here at SimanatisSays.

Illustration by Ana Galvañ from The New Yorker, March 9, 2020.

A 2010 Breakthrough. Until 2010, computer vision had an error rate of around 30 percent, “roughly six times higher than a person’s,” John Seabrook notes. “Quantum and A.I. Tidbits Part 2” here at SimanaitisSays gave examples of cat images subtly revised to be 99-percent recognized as guacamole and photos of stop signs fudged to invisibility.

Advancements in machine learning around 2010 changed this. Seabrook writes, “Within five years, machines could perform object recognition with not just human but superhuman performance, thanks to deep learning, the now ubiquitous approach to A.I., in which algorithms that process input data learn through multiple trial-and-error cycles.”

In 72 hours of deep learning, for example, Google’s AlphaZero algorithm has taught itself the electronic ken to beat any human grand master of the ancient and complex game of Go.

Go players. Image from usgo.org of a carving at the Seattle Asian Art Museum.

Seabrook says, “Computers can now look for abnormalities in a CT scan as effectively as the best radiologists. Underwater C.V. [computer vision] can autonomously monitor fishery populations, a task that humans do less reliably and more slowly. Heineken uses C.V. to inspect eighty thousand bottles an hour produced by its facility in France—an extremely boring quality-control task previously performed by people. And then there is surveillance tech….”

Keeping an Eye On People. Seabrook observes, “Advances in computer vision have occurred so rapidly that local and national privacy policies—what aspects of your face and body should be protected by law from surveillance machines—are lagging far behind A.I.’s technological capabilities, leaving a public vulnerable to a modern panopticon, a total-surveillance society that could be built before we know enough to stop it.”

I’ve already experienced an element of this in my severed relationship with Facebook. Being recognized—or misrecognized—is already part of our culture.

Image by Igor Bastidas for The New York Times, January 18, 2019.

Seabrook offers as an example “China’s use of face recognition and other surveillance technologies—widely deployed as part of Xi Jinping’s ‘stability maintenance’ drive….”

Tomorrow in Part 2, Seabrook discusses ubiquitous surveillance of us all through A.L.P.R., Auto License Plate Recognition, and also our possible countermeasures of obfuscation and invisibility.

© Dennis Simanaitis, SimanaitisSays.com, 2020

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