In a sense, you’re making these recommendations yourself. Machine-learning algorithms monitor information about what you do, find patterns in that data, and make informed guesses about what you want to do next. Without you, there’s no data, and there’s nothing for machine learning to learn. But when you provide your data, and when the guesses are correct, machine learning operates invisibly, leaving you to experience life as an endless stream of tiny, satisfying surprises.
Or at least that’s how things could be, according to computer scientist Pedro Domingos. In The Master Algorithm, Domingos envisions an individually optimized future in which our digital better halves learn everything about us, then go out into the world and act for us, thereby freeing us to be our best non-digital selves. In this vision, machine-learning algorithms replace tedious human activities like online shopping, legal filing, and scientific hypothesis testing. Humans feed data to algorithms, and algorithms produce a better world for humans. [...]
So the future, which is the present, isn’t looking good for humans. What is to be done? Domingos’s answer is, approximately, learn more about machine learning. The Master Algorithm insists on a politics of data in which hypervigilant data citizens actively manipulate the algorithms that might otherwise constrain them. Since machine learning depends on data, Domingos argues, “your job in a world of intelligent machines is to keep making sure they do what you want, both at the input (setting the goals) and at the output (checking that you got what you asked for).”
