Last week, Google announced some astounding statistics about the success rate of their spam filtering technology in Google Mail. Google says that less than 0.1% of email in the average user’s Gmail inbox is spam, and the rate for legitimate email ending up in the spam folder is even lower at less than %0.05.
Despite these superb results, Google is continuing to innovate and find new ways of detecting and blocking spam. This year, Google has announced several innovations across their product line that utilize their machine learning technology, affectionately referred to as Google Brain. Using their artificial neural network software, Google Photos can identify objects in your photographs, like dogs or cats or bridges or birthday cakes. Google Maps can automatically detect new businesses or speed limit signs in the imagery collected by their entire fleet of Street View cars. Google Earth Engine can detect new refuge camps or deforestation boundaries by scanning for visual patterns in petabytes of satellite images.
Google is now using its artificial neural networks to fight spam. A Google spokesman explains:
Our neural net system learns based on a huge collection of example “wanted” messages and a similar body of example spam mails. The system tracks thousands of attributes of each message (for example, the words in the message or the sender’s IP address). The spam filter then uses a technique called clustering analysis to find attribute groupings which differentiate spam from wanted mail. Essentially, the spam filter finds the sneaky spam by ignoring the similarities, and focusing only on the differences. As both spam and wanted mail evolve, the system is constantly relearning this differentiation. When users report spam (or not spam) that content is fed into the system, and it learns more. Ultimately, our spam filter learns from these user reports, which is how it has improved so much in the last few years.
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