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AI Science

Computer Spots Fakers Better Than People Do 62

Rambo Tribble (1273454) writes "Using sophisticated pattern matching software, researchers have had substantially better success with a computer, than was obtained with human subjects, in spotting faked facial expressions of pain. [Original, paywalled article in Current Biology] From the Reuters piece: '... human subjects did no better than chance — about 50 percent ...', 'The computer was right 85 percent of the time.'"
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Computer Spots Fakers Better Than People Do

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  • by Trepidity ( 597 ) <[gro.hsikcah] [ta] [todhsals-muiriled]> on Saturday March 22, 2014 @06:04PM (#46554009)

    I'd say it's a mixture of the two. The computer can't discriminate these facial features without people to program it, but the people can't discriminate these facial features on their own, either, because we aren't good at applying this kind of analysis ourselves (even if we can come up with what it ought to be). The existence of a computer isn't enough, and the existence of the people is also insufficient, to carry out the task. So I'd call it a collaborative activity.

  • by Artifakt ( 700173 ) on Saturday March 22, 2014 @07:21PM (#46554389)

    There's a good precedent for your argument that this is a question of instinctual skill vrs trained skill, but it doesn't take anything like a billion examples to train a person in the example I'm considering. A very common way to teach health care personnel to recognize Fetal Alchohol Syndrome is to give them an album with several hundred photos of people in various life stages, all suffering from FAS. This method has worked since the time when the photos were black and white, and in fact, using color shots or video footage doesn't seem to have any impact on success or the number of examples needed. Once someone is trained that way, the success percentage is in the very high 90s, and stays that way, at least for a typical crreer. Similar methods are used for other diseases, for example most people have learned to spot Down's syndrome from just a few examples, but where the syndrome produces only some of the usual appearance effects, spotting the 'borderline cases' with high accuracy can be taught this same way, usually taking about 15 minutes.

I've noticed several design suggestions in your code.

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