Among the many steps along the road to high-performance AI, one of the most important was taken in 2007 by Fei-Fei Li, then an assistant professor in Princeton’s computer science department. Using Amazon’s Mechanical Turk service to amass many millions of small acts of human judgment, Li built a vast database of hand-labelled images.
“We settled on a goal of 1,000 different photographs of every single object category,” she writes in her autobiography The Worlds I See. “One thousand different photographs of violins. One thousand different photographs of German shepherds.”
The database, ImageNet, was released in 2009, and Li started a competition for researchers to build the best image-recognition algorithms. A few years later, a graduate student named Alex Krizhevsky, advised by AI pioneer Geoffrey Hinton, trained a neural network on ImageNet — and blew the competition away.