Gmail Receives A Major Upgrade As Google Intensifies Efforts Against Spam

Gmail now boasts improved defense against spam emails. Recently, Google announced plans to delete inactive accounts. Now, the company is rolling out a new update that uses artificial intelligence (AI) filters to eliminate spam emails.

Traditional spam filters can be bypassed by scammers using special characters and emoji. The new system, known as RETVec (Resilient & Efficient Text Vectorizer), aims to make spam protection better.

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What is the new anti-spam system?

Google claims that the new mechanism is 38% better at identifying spam emails and reduces false positives by as much as 19.4%. The company, it appears, was secretly testing the system ahead of its release.

With the new system in place, users can worry less about flagging emails as junk in their inbox and dedicate their time to important emails. Google's new system should be able to flag phishing email campaigns in a more robust manner than earlier.

Users don't need to do anything to turn on the new feature. In fact, Google is currently automatically rolling out the new spam filter worldwide. When the upgraded system is up and running, Google will not notify users. Perhaps the only change you'll see is the less number of spam emails.

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Also read: A Guide To Blocking Specific Email Addresses In Gmail

Google's Cybersecurity & AI Research Director Elie Bursztein and Software Engineer Marina Zhang wrote a blog about RETVec. "Over the past year, we battle-tested RETVec extensively inside Google to evaluate its usefulness and found it to be highly effective for security and anti-abuse applications. In particular, replacing the Gmail spam classifier¡¯s previous text vectorizer with RETVec allowed us to improve the spam detection rate over the baseline by 38%."

Also read: Sending Confidential And Encrypted Emails With Gmail: A Step-By-Step Guide

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"Due to its novel architecture, RETVec works out-of-the-box on every language and all UTF-8 characters without the need for text preprocessing, making it the ideal candidate for on-device, web, and large-scale text classification deployments," the blog post states. "Models trained with RETVec exhibit faster inference speed due to its compact representation. Having smaller models reduces computational costs and decreases latency, which is critical for large-scale applications and on-device models."

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