Algorithms

FROM WIRED...

IN 2012, ARTIFICIAL intelligence researchers engineered a big leap in computer vision thanks, in part, to an unusually large set of images—thousands of everyday objects, people, and scenes in photos that were scraped from the web and labeled by hand. That data set, known as ImageNet, is still used in thousands of AI research projects and experiments today.

But last week every human face included in ImageNet suddenly disappeared—after the researchers who manage the data set decided to blur them.

Just as ImageNet helped usher in a new age of AI, efforts to fix it reflect challenges that affect countless AI programs, data sets, and products.


“We were concerned about the issue of privacy,” says Olga Russakovsky, an assistant professor at Princeton University and one of those responsible for managing ImageNet.

ImageNet was created as part of a challenge that invited computer scientists to develop algorithms capable of identifying objects in images. In 2012, this was a very difficult task. Then a technique called deep learning, which involves “teaching” a neural network by feeding it labeled examples, proved more adept at the task than previous approaches.


Since then, deep learning has driven a renaissance in AI that also exposed the field’s shortcomings. For instance, facial recognition has proven a particularly popular and lucrative use of deep learning, but it's also controversial. A number of US cities have banned government use of the technology over concerns about invading citizens’ privacy or bias, because the programs are less accurate on nonwhite faces.

Today ImageNet contains 1.5 million images with around 1,000 labels. It is largely used to gauge the performance of machine learning algorithms, or to train algorithms that perform specialized computer vision tasks. Blurring the faces affected 243,198 of the images.  READ MORE