Instagram users have used hashtags to classify billions of images, unintentionally creating great labels to train the company’s computer vision algorithms.
The news: Facebook created a data set of 3.5 billion pictures and 17,000 hashtags pulled from public Instagram accounts to improve how well it can recognize objects in images, the company announced on stage at its annual F8 developer conference today. Using a subset of that data set, Facebook was able to label 85.4 percent of photos correctly, the highest level the company has achieved to date.
But: This free labor does have drawbacks: some hashtags like #photooftheday or #tbt—short for throwback Thursday—don’t describe what’s in the image and can confuse the algorithm.
Why it matters: More accurate computer vision could do everything from surface more relevant content to help keep abusive posts off the site. Of course, Mark Zuckerberg has already had to answer for how users’ data gets handled, and not everyone might be happy to find out that their vacation photos are being put to work.
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.
Video: Geoffrey Hinton talks about the “existential threat” of AI
Watch Hinton speak with Will Douglas Heaven, MIT Technology Review’s senior editor for AI, at EmTech Digital.
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