“Kosinski has made a career of warning others about the uses and potential abuses of data. Four years ago, he was pursuing a Ph.D. in psychology, hoping to create better tests for signature personality traits like introversion or openness to change. But he and a collaborator soon realized that Facebook might render personality tests superfluous: Instead of asking if someone liked poetry, you could just see if they ‘liked’ Poetry Magazine,” Kuang writes. “In 2014, they published a study showing that if given 200 of a user’s likes, they could predict that person’s personality-test answers better than their own romantic partner could.”
“After getting his Ph.D., Kosinski landed a teaching position at the Stanford Graduate School of Business and soon started looking for new data sets to investigate,” Kuang writes. “Kosinski first mined 200,000 publicly posted dating profiles, complete with pictures and information ranging from personality to political views. Then he poured that data into an open-source facial-recognition algorithm — a so-called deep neural network, built by researchers at Oxford University — and asked it to find correlations between people’s faces and the information in their profiles. The algorithm failed to turn up much, until, on a lark, Kosinski turned its attention to sexual orientation. The results almost defied belief. In previous research, the best any human had done at guessing sexual orientation from a profile picture was about 60 percent — slightly better than a coin flip. Given five pictures of a man, the deep neural net could predict his sexuality with as much as 91 percent accuracy. For women, that figure was lower but still remarkable: 83 percent.”
“It has become commonplace to hear that machines, armed with machine learning, can outperform humans at decidedly human tasks, from playing Go to playing ‘Jeopardy!’ We assume that is because computers simply have more data-crunching power than our soggy three-pound brains,” Kuang writes. “Kosinski’s results suggested something stranger: that artificial intelligences often excel by developing whole new ways of seeing, or even thinking, that are inscrutable to us. It’s a more profound version of what’s often called the ‘black box’ problem — the inability to discern exactly what machines are doing when they’re teaching themselves novel skills — and it has become a central concern in artificial-intelligence research. In many arenas, A.I. methods have advanced with startling speed; deep neural networks can now detect certain kinds of cancer as accurately as a human. But human doctors still have to make the decisions — and they won’t trust an A.I. unless it can explain itself. This isn’t merely a theoretical concern. In 2018, the European Union will begin enforcing a law requiring that any decision made by a machine be readily explainable, on penalty of fines that could cost companies like Google and Facebook billions of dollars.”
Much more in the full article here.
MacDailyNews Take: An inscrutable neural network that teaches itself and makes its own decisions?
Oh, relax. What could possibly go wrong?