About a decade ago, spam brought email to near-ruin. The contest to save your inbox was on, with two of the world’s biggest tech companies vying for the title of top spam-killer. By 2012, Microsoft boasted that its filters were removing all but 3 percent of the junk messages from Hotmail, the company’s online email service at the time.
Google responded by claiming that its service, Gmail, removed all but about one percent of spam messages, adding that its false positives rate, legitimate mail misidentified as spam, was also about one percent. It was a point of pride for the two companies, particularly Microsoft, whose Hotmail service once carried such a poor reputation for spam. And the relative success of both showed that heuristic technologies, which identify spam based on a pre-defined rules, were working.
But they still weren’t working well enough. One percent spam is still pretty annoying. And a one percent false-positive rate is, well, quite a bit more than annoying, if crucial messages go unread. Naturally, these companies continue to hone their spam-battling techniques, and now, Google has upped the ante with a new breed of artificial intelligence tools.
Three years after it last released Gmail’s spam stats, Google says that its spam rate is down to 0.1 percent, and its false positive rate has dipped to 0.05 percent. The company credits the significant drop in large part to the introduction of brain-like “neural networks” into its spam filters that can learn to recognize junk mail and phishing messages by analyzing scads off the stuff across an enormous collection of computers.
“One of the great things about machine learning is that it adapts to changing situations.” says John Rae-Grant, a senior product manager for Gmail, which Google says is now used by 900 million people across the globe. In other words, Gmail’s spam filters don’t just curb junk by applying pre-existing rules. They create new rules themselves as they go along.