The researchers tested the system by seeing how well it distinguished one player from another. They gave the system 100 games from each of about 3000 known players, and 100 fresh games from a mystery player. To make the task harder, they hid the first 15 moves of each game. The system looked for the best match and identified the mystery player 86% of the time, the researchers reported last month at the Conference on Neural Information Processing Systems (NeurIPS). “We didn’t quite believe the results,” says Reid McIlroy-Young, a student in Anderson’s lab and the paper’s primary author. A non-AI method was only 28% accurate. […] The researchers are aware of the privacy risks posed by the system, which could be used to unmask anonymous chess players online. With tweaks, McIlroy-Young says, it could do the same for poker. And in theory, they say, given the right data sets, such systems could identify people based on the quirks of their driving or the timing and location of their cellphone use.
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