Google using machine learning to filter spam on Gmail

Google eliminates more spam from Gmail with TensorFlow

The platform was launched in 2015 by Google and is an open source framework.

"That's why we recently implemented new protections powered by TensorFlow, an open-source machine learning (ML) framework developed at Google". The company says it's using some of TensorFlow's capabilities to detect 100 million more spam emails every day.

Artificial intelligence, machine learning, and deep learning may still sound like vague concepts or insufficiently mature technologies to impact the average mobile device consumer in truly significant ways, but according to Google, one palpable development already made possible by something called TensorFlow is less spam than ever before in our Gmail inboxes. Moreover, Google, Intel, SAP, Airbnb, and Qualcomm are the top users of this software.

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". Along with this, Gmail has also been using AI with the rule-based filters. '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.

"At the scale we're operating at, an additional 100 million is not easy to come by", and that "Getting the last bit of incremental spam is increasingly hard, [but] TensorFlow has been great for closing that gap".

Even though it may seem trivial, as Google tries to eliminate the remaining 0. TensorFlow Extended (TFX) is one of these components that allows Google to deploy ML pipelines in a quick and standardized fashion while TensorBoard allows it to monitor model training pipelines and quickly evaluate new models to determine their usefulness.

TensorFlow also helps Google personalise spam protection for each user.

TensorFlow will now help Gmail users to get rid of emails with hidden embedded content, image-based spam and spam laden messages from newly created domains which getaway through the existing filters by managing to send very few messages within legitimated traffic.

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