This is, by the way, the dirty secret of the machine learning movement: almost everything produced by ML could have been produced, more cheaply, using a very dumb heuristic you coded up by hand, because mostly the ML is trained by feeding it examples of what humans did while following a very dumb heuristic. There's no magic here. If you use ML to teach a computer how to sort through resumes, it will recommend you interview people with male, white-sounding names, because it turns out that's what your HR department already does. If you ask it what video a person like you wants to see next, it will recommend some political propaganda crap, because 50% of the time 90% of the people do watch that next, because they can't help themselves, and that's a pretty good success rate.

Forget Privacy: You're Terrible at Targeting Anyway

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If there is one myth regarding computer technology that ought to be swept into the dustbin it is the pervasive believe that computers can do only what they are specifically programmed to do. As we’ve seen, machine learning algorithms routinely churn through data, revealing statistical relationships and, in essence, writing their own programs on the

Martin Ford • Rise of the Robots: Technology and the Threat of a Jobless Future

Rich Sutton's "The Bitter Lesson" reveals a fascinating pattern in AI research:

For decades, AI researchers have tried to encode human knowledge and expertise into their systems. They believed this would lead to better results. They were wrong. Time and time again, the approaches that won out were simple, general methods that leveraged raw computat