Yoshua Bengio is a grandmaster of modern artificial intelligence. Alongside Geoff Hinton and Yan LeCun, Bengio is famous for championing a technique known as deep learning that in recent years has gone from an academic curiosity to one of the most powerful technologies on the planet. Deep learning involves feeding data to large, crudely-simulated neural networks, and it has proven incredibly powerful and effective for all sorts of practical tasks, from voice recognition and image classification to controlling self-driving cars and automating business decisions. Bengio has resisted the lure of any big tech company. While Hinton and LeCun joined Google and Facebook respectively, he remains a full-time professor at the University of Montreal.
The winners of the 2018 Turing Award have been announced. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio -- sometimes referred to as the "godfathers of artificial intelligence" -- have won the 2018 Turing Award for their work on neural networks. The three artificial intelligence pioneers' work basically laid the foundation for modern AI technologies. In the 1980s and early 1990s, artificial intelligence experienced a renewed popularity within the scientific community. However, by the mid-90s, scientists had failed to make any major advancements in AI, making it harder to secure funding or publish research.
Once treated by the field with skepticism (if not outright derision), the artificial neural networks that 2018 ACM A.M. Turing Award recipients Geoffrey Hinton, Yann LeCun, and Yoshua Bengio spent their careers developing are today an integral component of everything from search to content filtering. Here, the three researchers share what they find exciting, and which challenges remain. There's so much more noise now about artificial intelligence than there was when you began your careers--some of it well-informed, some not. What do you wish people would stop asking you? GEOFFREY HINTON: "Is this just a bubble?"
Last week, for the first time ever, RE•WORK brought together the'Godfathers of AI' to appear not only at the same event, but on a joint panel discussion. At the Deep Learning Summit in Montreal last week, we saw Yoshua Bengio, Yann LeCunand Geoffrey Hinton come together to share their most cutting edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada. Joelle Pineau from McGill University who was moderating the discussion began by asking each pioneer to introduce their neighbour, which immediately generated a laugh from the packed auditorium. Yoshua kicked off by saying'here's Yann, I met him doing my masters and he was doing his post doc with Geoff and later Yann invited me to come and work with him and start working in convolutional neural networks, and it's still the hot thing today!' Yann went on to introduce Geoffrey and said'I'll be historical as well, when I was an undergrad I studied neural nets and realised there was no research published in the 70s. I saw a paper entitled Optimal Perceptual Inference and Geoff was one of the three authors.
The breakthrough came in 2006: Hinton led a published paper called A Fast Learning Algorithm for Deep Belief Nets, which first proposed the method of greedy layer-wise training for deep neural networks. In an competition run by ImageNet in 2012, Hinton's UofT team used convolutional neural networks (CNN) for image recognition application. Given the large pool of image datasets and the computation power of GPU processors, the team was on the right track, and their results redefined the field of computer vision. Two of Hinton's earliest correspondents were Yoshua Bengio from the University of Montreal and his own postdoc student Yan Lecun, who joined Hinton's UofT lab in 1987 and now leads AI research at Facebook. The accomplished trio is sometimes jokingly referred to as the "Canadian Mafia" of deep learning.