The company said that with the new protections, it can block an additional 100 million spam messages every single day.
In fact, the firm has insisted it now blocks 99.9 percent of "spam, phishing, and malware from reaching Gmail inboxes" at the moment.
Google has been using AI along with rule-based filters to detect spam for years.
100 MILLION spam messages blocked out every day sounds like a staggeringly high figure, but considering that there are more than 1.5 billion people using Gmail every month, it works out to only ONE extra blocked spam message per 10 users.
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One of the more mainstream ideas was to further improve Gmail's already impressive spam protection by adapting quickly to ever-changing, increasingly sophisticated techniques of spreading spammy messages. 'Within Gmail, we're now experimenting TensorFlow in other security-related areas, such as phishing and malware, as part of our continuous efforts to keep users safe, ' Google explains on its blog.
This does not dismiss the achievement of TensorFlow, though, as the blocking of the additional nuisance emails suggests that Google's spam-blocking functionality has been enhanced through machine learning. It has been praised both for its flexibility and capacity to scale alongside working with Google's other AI services.
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. "ML allows us to look at all of these signals together to make a determination". "But CxOs want to see proof points before adopting technologies, so that's why Google is showcasing its internal uptake of TensorFlow for fighting spam".
Google said the machine learning helps to flag certain trickier suspicious messages, such as "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".