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.
Geoffrey Hinton tells us why he’s now scared of the tech he helped build
“I have suddenly switched my views on whether these things are going to be more intelligent than us.”
Deep learning pioneer Geoffrey Hinton has quit Google
Hinton will be speaking at EmTech Digital on Wednesday.
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ChatGPT has sparked speculation about artificial general intelligence. But the next real phase of AI will be in specific domains and contexts.
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