Gmail is now blocking 100 million more spam messages with TensorFlow


Google says it is now blocking close to 100 million more spam messages daily following the recent implementation of TensorFlow, its in-house open-source machine learning AI framework. The company says that it already blocks 99.9 percent of spam emails through its implemented machine learning and rule-based processes, and TensorFlow helps in filling that 0.1 percent gap.

In fact, the firm has insisted it now blocks 99.9 percent of "spam, phishing, and malware from reaching Gmail inboxes" at the moment. It was done by employing artificial intelligence (AI) apart from the traditionally used rule-based filters which block out only the most obvious spam.

100 million emails might sound like a lot, but when put into context against Gmail's 1.5 billion users, it only works out at one extra blocked spam email per 10 users, according to The Verge. After all, what one person considers spam might be considered important by another user, Kumaran said.

"At the scale we're operating at, an additional 100 million is not easy to come by".

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Google added the new machine learning framework from TensorFlow does not replace the existing technologies that were already in place, but it simply exists alongside them. Since then, "teams and researchers all over the world" have produced a whopping 71,000 forks of the public code and other open-source contributions, establishing a strong community that makes it easy to quickly apply "new research and ideas".

"Using TensorFlow has helped us block image-based messages, emails with hidden embedded content, and messages from newly created domains that try to hide a low volume of spammy messages within legitimate traffic".

Chances are, you have more emails in your spam folder than your inbox on Gmail.

Google isn't saying whether TensorFlow will help with the accuracy of spam detection when it comes to flagging non-spam email as spam, but the personalised spam detection should help. All in all, TensorFlow allows Gmail to scale its ML efforts, requiring fewer engineers to run more experiments and protect users more effectively.