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Desiderata and ML Research Programme for Higher-Level Cognition
Yoshua Bengio

Mon Dec 13 08:45 AM -- 09:15 AM (PST) @

How can deep learning be extended to encompass the kind of high-level cognition and reasoning that humans enjoy and that seems to provide us with stronger out-of-distribution generalization than current state-of-the-art AI? Looking into neuroscience and cognitive science and translating these observations and theories into machine learning, we propose an initial set of inductive biases for representations, computations and probabilistic dependency structure. These strongly tie the notion of representation with that of actions, interventions and causality, possibly giving a key to stronger identifiability of latent causal structure and ensuing better sample complexity in and out of distribution, as well as meta-cognition abilities facilitating exploration that seeks to reduce epistemic uncertainty of the underlying causal understanding of the environment.

Author Information

Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

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