What is deep learning?

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. In September 2012, Alex Krizhevsky and Ilya Sutskever, two AI researchers from the University of Toronto, made history at ImageNet, a popular competition in which participants develop software that can recognize objects in a large database of digital images. Krizhevsky and Sutskever, and their mentor, AI pioneer Geoffrey Hinton, submitted an algorithm that was based on deep learning and neural networks, an artificial intelligence technique that the AI community viewed with skepticism because of its past shortcomings. AlexNet, the deep learning algorithm developed by the U of T researchers, was able to win the competition with an error rate of 15.3 percent, a whopping 10.8 percent better than the runner up. By some accounts, the event triggered the deep learning revolution, creating interest in the field by many academic and commercial organizations. Today, deep learning has become pivotal to many of the applications we use every day such as content recommendation systems, translation apps, digital assistants, chatbots and facial recognition systems.