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Wednesday, April 17, 2019

Warnings of a Dark Side to A.I. in Health Care

Cade Metz and Craig S. Smith
The New York Times
Originally published March 21, 2019

Here is an excerpt:

An adversarial attack exploits a fundamental aspect of the way many A.I. systems are designed and built. Increasingly, A.I. is driven by neural networks, complex mathematical systems that learn tasks largely on their own by analyzing vast amounts of data.

By analyzing thousands of eye scans, for instance, a neural network can learn to detect signs of diabetic blindness. This “machine learning” happens on such an enormous scale — human behavior is defined by countless disparate pieces of data — that it can produce unexpected behavior of its own.

In 2016, a team at Carnegie Mellon used patterns printed on eyeglass frames to fool face-recognition systems into thinking the wearers were celebrities. When the researchers wore the frames, the systems mistook them for famous people, including Milla Jovovich and John Malkovich.

A group of Chinese researchers pulled a similar trick by projecting infrared light from the underside of a hat brim onto the face of whoever wore the hat. The light was invisible to the wearer, but it could trick a face-recognition system into thinking the wearer was, say, the musician Moby, who is Caucasian, rather than an Asian scientist.

Researchers have also warned that adversarial attacks could fool self-driving cars into seeing things that are not there. By making small changes to street signs, they have duped cars into detecting a yield sign instead of a stop sign.

The info is here.

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