The company said that with the new protections, it can block an additional 100 million spam messages every single day.
Google has been using AI as well as rule-based email filters for years.
There is no one definition for 'spam,' and that what looks like a spammy message to one user might be a much-awaited mail for another one, and Google is fully aware of the fact.
In a blog post today, Neil Kumaran, a product manager for Google's Counter Abuse Technology department, describes how the company's current efforts already can block more than 99.99 percent of all spam, phishing attacks and malware from reaching user's Gmail inboxes.
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. Google is aiming to simplify the process with TensorFlow.
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Due to slight variations in their data, NASA and Berkeley Earth both have 2010 in 5th place, with 2014 following up in 6th. Weather extremes in 2018 included wildfires in California and Greece, drought in South Africa and floods in Kerala, India.
Google LLC is beefing up Gmail's anti-spam capabilities with new protections, powered by its machine learning software framework TensorFlow, that are created to complement its existing algorithms. When you think about it, emails have thousands of potential signals and just because a certain email meets characteristics that could categorise it as "spammy", does not mean it is actually an unwanted email.
Google is blocking the extra 100 million spam emails by identifying categories that used to be hard to detect. Hence, integrating TensorFlow with the existing Gmail framework has allowed the latter to personalize the spam filters more effectively.
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. 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.
What kind of additional spam messages did Google now get better at deflecting?