Portrait de Yoshua Bengio

Yoshua Bengio

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur titulaire, Université de Montréal, Département d'informatique et de recherche opérationnelle
Fondateur et Conseiller scientifique, Équipe de direction
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Causalité
Modèles génératifs
Modèles probabilistes
Modélisation moléculaire
Neurosciences computationnelles
Raisonnement
Réseaux de neurones en graphes
Réseaux de neurones récurrents
Théorie de l'apprentissage automatique
Traitement du langage naturel

Biographie

*Pour toute demande média, veuillez écrire à medias@mila.quebec.

Pour plus d’information, contactez Marie-Josée Beauchamp, adjointe administrative à marie-josee.beauchamp@mila.quebec.

Reconnu comme une sommité mondiale en intelligence artificielle, Yoshua Bengio s’est surtout distingué par son rôle de pionnier en apprentissage profond, ce qui lui a valu le prix A. M. Turing 2018, le « prix Nobel de l’informatique », avec Geoffrey Hinton et Yann LeCun. Il est professeur titulaire à l’Université de Montréal, fondateur et conseiller scientifique de Mila – Institut québécois d’intelligence artificielle, et codirige en tant que senior fellow le programme Apprentissage automatique, apprentissage biologique de l'Institut canadien de recherches avancées (CIFAR). Il occupe également la fonction de conseiller spécial et directeur scientifique fondateur d’IVADO.

En 2018, il a été l’informaticien qui a recueilli le plus grand nombre de nouvelles citations au monde. En 2019, il s’est vu décerner le prestigieux prix Killam. Depuis 2022, il détient le plus grand facteur d’impact (h-index) en informatique à l’échelle mondiale. Il est fellow de la Royal Society de Londres et de la Société royale du Canada, et officier de l’Ordre du Canada.

Soucieux des répercussions sociales de l’IA et de l’objectif que l’IA bénéficie à tous, il a contribué activement à la Déclaration de Montréal pour un développement responsable de l’intelligence artificielle.

Étudiants actuels

Collaborateur·rice alumni - McGill
Collaborateur·rice alumni - UdeM
Collaborateur·rice de recherche - Cambridge University
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Visiteur de recherche indépendant
Co-superviseur⋅e :
Doctorat - UdeM
Collaborateur·rice de recherche - N/A
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Collaborateur·rice de recherche - KAIST
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Doctorat - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Stagiaire de recherche - UdeM
Doctorat
Doctorat - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Stagiaire de recherche - UdeM
Collaborateur·rice de recherche - UdeM
Collaborateur·rice alumni - UdeM
Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni
Doctorat - UdeM
Collaborateur·rice alumni - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Collaborateur·rice de recherche - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Visiteur de recherche indépendant - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - Ying Wu Coll of Computing
Doctorat - University of Waterloo
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - Max-Planck-Institute for Intelligent Systems
Doctorat - UdeM
Postdoctorat - UdeM
Visiteur de recherche indépendant - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Maîtrise recherche - UdeM
Collaborateur·rice alumni - UdeM
Maîtrise recherche - UdeM
Visiteur de recherche indépendant - Technical University of Munich
Doctorat - UdeM
Co-superviseur⋅e :
Postdoctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - UdeM
Collaborateur·rice de recherche
Collaborateur·rice de recherche - KAIST
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :

Publications

Neural Causal Structure Discovery from Interventions
Nan Rosemary Ke
Olexa Bilaniuk
Anirudh Goyal
Stefan Bauer
Bernhard Schölkopf
Michael Curtis Mozer
Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (voir plus) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argu… (voir plus)es for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties"of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argu… (voir plus)es for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties"of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argu… (voir plus)es for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties"of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argu… (voir plus)es for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties"of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argu… (voir plus)es for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties"of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argu… (voir plus)es for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties"of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.
Scientific discovery in the age of artificial intelligence
Hanchen Wang
Tianfan Fu
Yuanqi Du
Wenhao Gao
Kexin Huang
Ziming Liu
Payal Chandak
Shengchao Liu
Peter Van Katwyk
Andreea Deac
Animashree Anandkumar
K. Bergen
Carla P. Gomes
Shirley Ho
Pushmeet Kohli
Joan Lasenby
Jure Leskovec
Tie-Yan Liu
A. Manrai
Debora Susan Marks … (voir 10 de plus)
Bharath Ramsundar
Le Song
Jimeng Sun
Petar Veličković
Max Welling
Linfeng Zhang
Connor Wilson. Coley
Marinka Žitnik
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
The global integration of solar power into the electrical grid could have a crucial impact on climate change mitigation, yet poses a challen… (voir plus)ge due to solar irradiance variability. We present a deep learning architecture which uses spatio-temporal context from satellite data for highly accurate day-ahead time-series forecasting, in particular Global Horizontal Irradiance (GHI). We provide a multi-quantile variant which outputs a prediction interval for each time-step, serving as a measure of forecasting uncertainty. In addition, we suggest a testing scheme that separates easy and difficult scenarios, which appears useful to evaluate model performance in varying cloud conditions. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective use of solar power and the resulting reduction of CO
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Emezue
Tristan Deleu
Stefan Bauer
The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inf… (voir plus)erence. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. To address this gap, we evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets, on the downstream task of treatment effect estimation. Through the implementation of a distribution-level evaluation, we offer valuable and unique insights into the efficacy of these causal discovery methods for treatment effect estimation, considering both synthetic and real-world scenarios, as well as low-data scenarios. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes, while some tend to learn many low-probability modes which impacts the (unrelaxed) recall and precision.