By Peter High, published on Forbes
Yoshua Bengio is one of the foremost thinkers in a field within artificial intelligence known as artifical neural networks and deep learning. Although significant progress has been made in recent years due to (among other factors) the combination of the proliferation of data, the decreasing cost of compute, and the tremendous amount of money and talent now devoted to artificial intelligence, Bengio chose this as a field of study during the 1980s, in the throes of what some referred to as the AI winter, seeing through a period when money and enthusiasm for artificial intelligence had dried up.
Bengio is the co-author (with Ian Goodfellow and Aaron Courville) of Deep Learning, a book that Elon Musk referred to as “the definitive textbook on deep learning.” On top of his growing influence in this field, he has also been enormously influential in shaping Montreal to become a hotbed for artificial intelligence. Bengio co-founded Element AI in 2016, which has a stated mission to “turn the world’s leading AI research into transformative business applications.” Element AI aims to foster partnership between the private sector and academia to help push the expansion of AI.
Bengio believes Montreal has emerged as a powerhouse due to the combination of great universities, great companies (including a number of Silicon Valley companies who have established offices in Montreal), and Canada’s ethos of cooperation among elite minds. We cover all of the above and more herein.
Peter High: Where does the field of deep neural networks currently stand in your estimation?
Yoshua Bengio: We have made amazing progress, but we are far from human level intelligence with computers. Most of the progress has been with supervised learning, which means machines are taught by essentially imitating humans. With supervised learning, humans provide the high-level concepts that the computer learns, which can be tedious and limits the ability of computers to discover things by themselves. Unsupervised learning, or what we call reinforcement learning, is when the learner is not merely passively observing the world, or how humans do things, but interacts with the environment and gets feedback. Humans are good at this. Combining unsupervised deep learning and reinforcement learning is one of the things that I am working on.
High: What steps are needed to reach the more fully realized version of unsupervised learning?