Managing expectations of artificial intelligence

The public’s view of artificial intelligence may not be accurate, but that doesn’t mean that those developing new technologies can afford to ignore it.

When all mail used to arrive by post, sorting out the junk was a minor chore. But when mail began to be sent digitally, computers started churning out so much junk e-mail that it was necessary to automate the act of identifying the real mail — the ‘ham’, in Internet jargon — from the torrents of spam. But clever spammers soon found ways to avoid the spam filters, so e-mail providers turned to artificial intelligence (AI) to outsmart them. These days, AI systems are trained to tell spam from ham by being fed enormous amounts of both, and they defend your inbox without you having to lift a finger.

This is one way in which AI has slipped into our daily lives almost without us noticing. In the early 2000s, there were fears that spam was on the verge of killing e-mail. Machine learning saved the day because it excels at recognizing the kinds of pattern that are inevitable in mass mailings. It is not infallible; databases must be updated to keep up with spammers’ new tricks, and users still have to occasionally check that the spam filter hasn’t swallowed something important. But machine-learning systems are the best tools we have.

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