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Only later did analysts realized that the American tanks had been photographed on a sunny day and the Russian tanks had been photographed on a cloudy day. Gay people, he has found, tend to post higher-quality photos. Kosinski said that they went to great lengths to guarantee that such confounders did not influence their results.Still, he agreed that it’s easier to teach a machine to see than to understand what it has seen.This story is often told to warn about the limits of algorithms and importance of data collection to avoid where the collected data can be solved using algorithms that do not generalize to the true data distribution, but the tank story is usually never sourced.I collate many extent versions dating back a quarter of a century to 1992 along with two NN-related anecdotes from the 1960s; their contradictions & details indicate a classic , with a probable origin in a speculative question in the 1960s by Edward Fredkin at an AI conference about some early NN research, which was subsequently classified & never followed up on.I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning and that it would be better to not repeat it but use real examples of dataset bias and focus on larger-scale risks like AI systems optimizing for wrong utility functions. T.’s Center for Brains, Minds and Machines, offered a classic parable used to illustrate this disconnect.Drawing on Google/Google Books/Google Scholar/Libgen/Less Wrong/Hacker News/Twitter, I have compiled a large number of variants of the story from various sources; below, in reverse chronological order by decade. The Army trained a program to differentiate American tanks from Russian tanks with 100% accuracy. Cox has spotted a version of this in his own studies of dating profiles.

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