In fact, the firm has insisted it now blocks 99.9 percent of "spam, phishing, and malware from reaching Gmail inboxes" at the moment. Along with this, Gmail has also been using AI with the rule-based filters.
No doubt most Gmail users will welcome any efforts to eliminate spam, but analyst Holger Mueller of Constellation Research Inc. told SiliconANGLE that Google's real aim was to highlight the ways in which TensorFlow can be used to enhance enterprise's machine learning efforts. It's still an impressive achievement, of course, especially when you consider how many unwanted messages it already blocks.
Explaining the change, the American tech giant said: "1.5 billion people use Gmail every month, and 5 million paying businesses use Gmail in the workplace as a part of G Suite". Getting the hand on last bit of spam is increasingly hard, but TensorFlow has been great for terminating that gap. TensorFlow was initially developed for Google's internal use.
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Applying ML at scale can be both complex and time consuming which is why TensorFlow contains many components to make ML easier and more efficient.
Admirers of this ML framework are impressed by its ability to seamlessly merge with Google's other AI services, besides being appreciative of its flexibility and capacity to scale. This complete process has been taking place for years where Gmail looks for some particular signals from users on the basis of which it judges the spam. According to the company, it is successful at identifying 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.
Google continued: "Where did we find these 100 million extra spam messages?" The technology also makes it easier to adapt quickly to new tactics used by spammers while personalizing spam protections for each user as one person's spam may be an important message for someone else.