Machine learning can sniff out tell-tale signs of shady URLs so you don’t get phished.
The problem: The internet is riddled with websites set up for the sole purpose of stealing a user’s information or installing malware on a victim’s machine. Antivirus companies blacklist them as fast as they can, but with new sites launched every day, it’s a Sisyphean effort to keep up.
AI to the rescue: A new system called URLNet uses neural networks that look at character-level and word-level combinations in—you guessed it—the site’s URL to detect a site’s risk. URLs contain clues to whether a site is malicious, like length and misspelled domain names.
Results: The researchers trained URLNet on two data sets, one containing a million legit and malicious URLs and one with five million. In each case, URLNet beat other current systems at detecting suspicious sites.
A Roomba recorded a woman on the toilet. How did screenshots end up on Facebook?
Robot vacuum companies say your images are safe, but a sprawling global supply chain for data from our devices creates risk.
The viral AI avatar app Lensa undressed me—without my consent
My avatars were cartoonishly pornified, while my male colleagues got to be astronauts, explorers, and inventors.
Roomba testers feel misled after intimate images ended up on Facebook
An MIT Technology Review investigation recently revealed how images of a minor and a tester on the toilet ended up on social media. iRobot said it had consent to collect this kind of data from inside homes—but participants say otherwise.
How to spot AI-generated text
The internet is increasingly awash with text written by AI software. We need new tools to detect it.
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