Breaking the Black Box: When Machines Learn by Experimenting on Us

This is the third installment in a series that aims to explain and peer inside the black-box algorithms that increasingly dominate our lives. As we enter the era of artificial intelligence, machines are constantly trying to predict human behavior. Google predicts traffic patterns based on motion sensors in our phones. Spotify anticipates the music we might want to listen to. Amazon guesses what books we want to read next. Machines learn to make these predictions by analyzing patterns in huge amounts of data. Some patterns that machines find can be nonsensical, such as analyses that have found that divorce rates in Maine go down when margarine consumption decreases. But other patterns can be extremely useful: For instance, Google uses machine learning to understand how to optimize energy use at its…


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