Such issues are especially resonant today, in part because algorithms themselves are increasingly reliant on machine learning. That is, instead of programmers explicitly issuing a host of strict if-then commands that determine what the algorithm can and cannot Wallis and Futuna Islands Email List do, algorithms are programmed to, in effect, learn from experience (in the form of huge chunks of data) and then teach themselves the best strategies for solving problems. In 2015 for instance researchers at Mount Sinai Hospital programmed a deep learning algorithm to study. The test reports and doctor diagnoses of 700,000 patients and then derive its own diagnostic rules. The algorithm eventually became as proficient at diagnosing as an experienced doctor. Even more strikingly Google’s algorithm AlphaGo taught itself how to play the game Go by learning from. A database of 30 million moves made by expert Go players and then playing millions of games against itself.
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The algorithm became the best Go player in the world and made moves. That struck experienced Go players as completely original. What’s interesting about these moves is that there was no real way for AlphaGo’s programmers to explain. Why the algorithm did what it did. That’s not a big deal when we’re talking about a game. It comes to fields where algorithms increasingly relied. In financial markets or medical diagnoses or decisions about which gulf email list criminal suspects should get out on bail. Hosanagar suggests as a result that what we need is an algorithmic bill of rights. We need some measure of transparency and control and that those devising algorithms need to acknowledge. The way they can create unintended and perverse consequences.
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But as journalist Clive Thompson shows to great effect in his rigorous and fascinating Coders, the best business book of the year on technology and innovation, the challenge is that the kind of people who write and devise the algorithms that are coming to govern so much of our lives are not, at the moment, necessarily the kind of people who care all that much about their negative effects. Coders is a book laced with deep affection for practitioners of the craft and for the act of traditional computer coding — for its clarity and rigor, and for the simplicity of a reward system in which a program either works or doesn’t (so different, as Thompson says, from the ambiguity and messiness of writing). Thompson writes as a kind of anthropologist investigating a distinctive and vivid subculture, but he’s an anthropologist who feels a certain kinship with his subjects