Artificial intelligence has to go in new directions if it's to realize the machine equivalent of common sense, and three of its most prominent proponents are in violent agreement about exactly how to do that. Yoshua Bengio of Canada's MILA institute, Geoffrey Hinton of the University of Toronto, and Yann LeCun of Facebook, who have called themselves co-conspirators in the revival of the once-moribund field of "deep learning," took the stage Sunday night at the Hilton hotel in midtown Manhattan for the 34th annual conference of the Association for the Advancement of Artificial Intelligence. The three, who were dubbed the "godfathers" of deep learning by the conference, were being honored for having received last year's Turing Award for lifetime achievements in computing. Each of the three scientists got a half-hour to talk, and each one acknowledged numerous shortcomings in deep learning, things such as "adversarial examples," where an object recognition system can be tricked into misidentifying an object just by adding noise to a picture. "There's been a lot of talk of the negatives about deep learning," LeCun noted.
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.