Yoshua Bengio

AI pioneer Yoshua Bengio explores paths forward to human-level artificial intelligence at the January 2017 Asilomar conference organized by the Future of Life Institute.

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A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main challenges ahead?

Yoshua Bengio believes that understanding the basics of AI is within every citizen’s reach. That democratizing these issues is important so that our societies can make the best collective decisions regarding the major changes AI will bring, thus making these changes beneficial and advantageous for all.

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Yoshua Bengio is one of the pioneers of Deep Learning. He is the head of the Montreal Institute for Learning Algorithms (MILA), Professor at the Université de Montréal, member of the NIPS board and co-founder of Element AI. With a PhD from McGill University (1991, Computer Science) and postdocs at MIT and AT&T Bell Labs, he holds the Canada Research Chair in Statistical Learning Algorithms, is a Senior Fellow of the Canadian Institute for Advanced Research and co-directs its program focused on deep learning. He is best known for his contributions to deep learning, recurrent nets, neural language models, neural machine translation and biologically inspired machine learning.

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Watch Yoshua Bengio, Professor of Computer Science and Operations Research at Université de Montréal on stage at World Summit AI Americas 2019

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We invited Yoshua Bengio, Scientific Director at Mila, Quebec Artificial Intelligence Institute to SHIFT 2019. In this video he presents his keynote on Deep Learning For AI, which he presented at SHIFT.

Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He is a professor, famous writer and computer scientist working dedicatedly on both artificial neural networks and deep learning.

Bengio's current interests include fundamental questions on deep learning, the geometry of generalization in high-dimensional spaces, biologically inspired learning algorithms, and challenging applications of statistical machine learning in artificial intelligence tasks.

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