Yoshua Bengio

jamal.abouhaibeh@mila.quebec

Biography

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Yoshua Bengio is recognized worldwide as a leading expert in AI. He is most known for his pioneering work in deep learning, which earned him the 2018 A.M. Turing Award, “the Nobel Prize of computing,” with Geoffrey Hinton and Yann LeCun.

Bengio is a full professor at Université de Montréal, and the founder and scientific advisor of Mila – Quebec Artificial Intelligence Institute. He is also a senior fellow at CIFAR and co-directs its Learning in Machines & Brains program, serves as special advisor and founding scientific director of IVADO, and holds a Canada CIFAR AI Chair.

In 2019, Bengio was awarded the prestigious Killam Prize and in 2022, he was the most cited computer scientist in the world by h-index. He is a Fellow of the Royal Society of London, Fellow of the Royal Society of Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. In 2023, he was appointed to the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

Concerned about the social impact of AI, Bengio helped draft the Montréal Declaration for the Responsible Development of Artificial Intelligence and continues to raise awareness about the importance of mitigating the potentially catastrophic risks associated with future AI systems.

Current Students

Jamal Abou Haibeh

Collaborating Alumni - McGill University

Collaborating researcher - Cambridge University

Principal supervisor :

David Rolnick

berkes.anais@mila.quebec

sanghyeok.choi@mila.quebec

Rim Assouel

PhD - Université de Montréal

assouelr@mila.quebec

Xiaoyin Chen

PhD - Université de Montréal

xiaoyin.chen@mila.quebec

Sanghyeok Choi

Collaborating researcher - KAIST

PhD - Université de Montréal

aniket.didolkar@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Guillaume Lajoie

eric.elmoznino@mila.quebec

PhD - Université de Montréal

lena-nehale.ezzine@mila.quebec

Jean-Pierre Falet

PhD - Université de Montréal

jean-pierre.falet@mila.quebec

PhD

pietro.greiner@mila.quebec

Mohsin Hasan

PhD - Université de Montréal

mohsin.hasan@mila.quebec

thomas.jiralerspong@mila.quebec

Moksh Jain

PhD - Université de Montréal

moksh.jain@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Guillaume Lajoie

Collaborating Alumni - Université de Montréal

younesse.kaddar@mila.quebec

Minsu Kim

Collaborating researcher - Université de Montréal

minsu.kim@mila.quebec

Hyeonah Kim

Postdoctorate - Université de Montréal

Principal supervisor :

Alex Hernández-García

hyeonah.kim@mila.quebec

Jeongjae Lee

Collaborating researcher - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

Collaborating researcher

Song LIU

Collaborating researcher - s.o.

liusong299@gmail.com

cristian-dragos.manta@mila.quebec

Cristian Dragos Manta

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

Sarthak Mittal

PhD - Université de Montréal

Principal supervisor :

Independent visiting researcher - Université de Montréal

nguyenph@mila.quebec

Ali Parviz

Collaborating researcher - Ying Wu Coll of Computing

ali.parviz@mila.quebec

Lena Podina

Collaborating researcher - University of Waterloo

Principal supervisor :

David Rolnick

elena.podina@mila.quebec

jarrid.rector-brooks@mila.quebec

Jarrid Rector-Brooks

PhD - Université de Montréal

Danyal REHMAN

Postdoctorate - Université de Montréal

danyal.rehman@mila.quebec

oliver.richardson@mila.quebec

Oli RICHARDSON

Postdoctorate - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Alex Hernández-García

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborating Alumni - Université de Montréal

Co-supervisor :

Alex Tong

Collaborating Alumni - Université de Montréal

alexander.tong@mila.quebec

Collaborating researcher - University of Cambridge

oszkar.urban@mila.quebec

donna.vakalis@mila.quebec

Donna Vakalis

Collaborating Alumni - Université de Montréal

Co-supervisor :

David Rolnick

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

siddarth.venkatraman@mila.quebec

Collaborating researcher

joseph.viviano2@mila.quebec

Collaborating researcher - Université de Montréal

esmeralda.whitammer@mila.quebec

Collaborating Alumni

omar.younis@mila.quebec

Collaborating researcher - Université de Montréal

taeyoung.yun@mila.quebec

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Tianyu Zhang

PhD - Université de Montréal

tianyu.zhang@mila.quebec

PhD - McGill University

Principal supervisor :

Harry Zhao

Collaborating Alumni - McGill University

Principal supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Scaling in the Service of Reasoning & Model-Based ML

April 4, 2023

Yoshua Bengio

Edward J. Hu

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

March 23, 2022

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

March 15, 2022

Generative Flow Networks

Yoshua Bengio

Publications

Integrating Generative and Experimental Platforms for Biomolecular Design

Cheng-Hao Liu

Jarrid Rector-Brooks

Soojung Yang

Sidney L Lisanza

Francesca-Zhoufan Li

Hannes Stärk

Jacob Gershon

Lauren Hong

Pranam Chatterjee

Tommi Jaakkola

Regina Barzilay

David Baker

Frances H. Arnold

Biomolecular design, through artificial engineering of proteins, ligands, and nucleic acids, holds immense promise in addressing pressing me… (see more)dical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a palpable disconnect exists with experimental biology: many ML research efforts prioritize static benchmark performance, potentially sidelining impactful biological applications. This workshop seeks to bridge this gap by bringing computationalists and experimentalists together, catalyzing a deeper interdisciplinary discourse. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and biological problems ready for ML. To attract high-quality and diverse research, we partnered with Nature Biotechnology for a special collection, and we created dedicated tracks for in-silico ML research and hybrid ML-experimental biology research. Our lineup features emerging leaders as speakers and renowned scientists as panelists, encapsulating a spectrum from high-throughput experimentation and computational biology to generative ML. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology.

2024-12-31

ICLR.cc/2025/Workshop_Proposals (published)

International AI Safety Report

Bronwyn Fox

André Carlos Ponce de Leon Ferreira de Carvalho

Mona Nemer

Raquel Pezoa Rivera

Yi Zeng

Juha Heikkilä

Guillaume Avrin

Antonio Krüger

Balaraman Ravindran

Hammam Riza

Ciarán Seoighe

Ziv Katzir

Andrea Monti

Hiroaki Kitano

Nusu Mwamanzi

Fahad Albalawi

José Ramón López Portillo

Haroon Sheikh

Gill Jolly … (see 86 more)

Olubunmi Ajala

Jerry Sheehan

Dominic Vincent Ligot

Kyoung Mu Lee

Crystal Rugege

Denise Wong

Nuria Oliver

Christian Busch

Ahmet Halit Hatip

Oleksii Molchanovskyi

Marwan Alserkal

Chris Johnson

Amandeep Singh Gill

Saif M. Khan

Sören Mindermann

Daniel Privitera

Tamay Besiroglu

Rishi Bommasani

Stephen Casper

Yejin Choi

Philip Fox

Ben Garfinkel

Danielle Goldfarb

Hoda Heidari

Anson Ho

Sayash Kapoor

Leila Khalatbari

Shayne Longpre

Sam Manning

Vasilios Mavroudis

Mantas Mazeika

Julian Michael

Jessica Newman

Kwan Yee Ng

Chinasa T. Okolo

Deborah Raji

Girish Sastry

Elizabeth Seger

Theodora Skeadas

Tobin South

Emma Strubell

Florian Tramèr

Lucia Velasco

Nicole Wheeler

Daron Acemoglu

Olubayo Adekanmbi

David Dalrymple

Thomas G. Dietterich

Edward W. Felten

Pascale Fung

Pierre-Olivier Gourinchas

Fredrik Heintz

Geoffrey Hinton

Nick Jennings

Andreas Krause

Susan Leavy

Percy Liang

Teresa Ludermir

Vidushi Marda

Helen Margetts

John McDermid

Jane Munga

Arvind Narayanan

Alondra Nelson

Clara Neppel

Alice Oh

Gopal Ramchurn

Stuart Russell

Marietje Schaake

Bernhard Schölkopf

Dawn Song

Alvaro Soto

Lee Tiedrich

Gael Varoquaux

Andrew Yao

Ya-Qin Zhang

Baran Acar

Ben Clifford

Lambrini Das

Claire Dennis

Freya Hempleman

Hannah Merchant

Rian Overy

Ben Snodin

Jonathan Barry

Benjamin Prud’homme

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced… (see more) AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

2024-12-31

arXiv (preprint)

Open Technical Problems in Open-Weight AI Model Risk Management

Stephen Casper

Kyle O'Brien

Shayne Longpre

Elizabeth Seger

Kevin Klyman

Rishi Bommasani

Aniruddha Nrusimha

Ilia Shumailov

Sören Mindermann

Steven Basart

Frank Rudzicz

Kellin Pelrine

Avijit Ghosh

Andrew Strait

Robert Kirk

Dan Hendrycks

Peter Henderson

J. Zico Kolter

Geoffrey Irving

Yarin Gal … (see 2 more)

Dylan Hadfield-Menell

2024-12-31

SSRN Electronic Journal (accepted)

Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?

Michael Cohen

Joumana Ghosn

Adam Oberman

Jesse Richardson

Oliver Richardson

Marc-Antoine Rondeau

Pierre-Luc St-Charles

David Williams-King

The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue go… (see more)als across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.

2024-12-31

arXiv (preprint)

A neuronal least-action principle for real-time learning in cortical circuits

Walter Senn

Dominik Dold

Akos F. Kungl

Benjamin Ellenberger

Jakob Jordan

João Sacramento

Mihai A. Petrovici

One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal lea… (see more)st-action principle for cortical processing of sensory streams to produce appropriate behavioural outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimize the local somato-dendritic mismatch error within individual neurons. For motor output neurons, it implies minimizing an instantaneous behavioural error. For deep network neurons, it implies a prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory inputs and the motor feedback during the whole sensory-motor trajectory. Ongoing synaptic plasticity reduces the somato-dendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic dynamics for global real-time computation and learning in the brain and in physical substrates in general.

2024-12-19

eLife (published)

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving

Aniket Didolkar

Anirudh Goyal

Nan Rosemary Ke

Siyuan Guo

Michal Valko

Timothy Lillicrap

Danilo Rezende

Michael Mozer

Sanjeev Arora

Metacognitive knowledge refers to humans' intuitive knowledge of their own thinking and reasoning processes. Today's best LLMs clearly posse… (see more)ss some reasoning processes. The paper gives evidence that they also have metacognitive knowledge, including ability to name skills and procedures to apply given a task. We explore this primarily in context of math reasoning, developing a prompt-guided interaction procedure to get a powerful LLM to assign sensible skill labels to math questions, followed by having it perform semantic clustering to obtain coarser families of skill labels. These coarse skill labels look interpretable to humans. To validate that these skill labels are meaningful and relevant to the LLM's reasoning processes we perform the following experiments. (a) We ask GPT-4 to assign skill labels to training questions in math datasets GSM8K and MATH. (b) When using an LLM to solve the test questions, we present it with the full list of skill labels and ask it to identify the skill needed. Then it is presented with randomly selected exemplar solved questions associated with that skill label. This improves accuracy on GSM8k and MATH for several strong LLMs, including code-assisted models. The methodology presented is domain-agnostic, even though this article applies it to math problems.

2024-12-10

Neural Information Processing Systems (Accept (poster))

RGFN: Synthesizable Molecular Generation Using GFlowNets

Michał Koziarski

Andrei Rekesh

Dmytro Shevchuk

Almer Van Der Sloot

Piotr Gainski

Cheng-Hao Liu

Mike Tyers

Robert A. Batey

Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional… (see more) in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

Trajectory Flow Matching with Applications to Clinical Time Series Modeling

Xi Zhang

Yuan Pu

Yuki Kawamura

Andrew Loza

Dennis L. Shung

Alexander Tong

2024-12-10

Conference on Neural Information Processing Systems (Accept (spotlight))

Imagining and building wise machines: The centrality of AI metacognition

Samuel G. B. Johnson

Amir-Hossein Karimi

Nick Chater

Tobias Gerstenberg

Kate Larson

Sydney Levine

Melanie Mitchell

Iyad Rahwan

Bernhard Schölkopf

Igor Grossmann

2024-11-03

ArXiv (preprint)

Correction: Al content detection in the emerging information ecosystem: new obligations for media and tech companies

Alistair Knott

Dino Pedreschi

Toshiya Jitsuzumi

Susan Leavy

David Eyers

Tapabrata Chakraborti

Andrew Trotman

Sundar Sundareswaran

Ricardo Baeza-Yates

Przemyslaw Biecek

Adrian Weller

Paul D. Teal

Subhadip Basu

Mehmet Haklidir

Virginia Morini

Stuart Russell

2024-10-21

Ethics and Information Technology (published)

A Complexity-Based Theory of Compositionality

2024-10-17

ArXiv (preprint)

Can Safety Fine-Tuning Be More Principled? Lessons Learned from Cybersecurity

David Williams-King

Linh Le

Adam Oberman

As LLMs develop increasingly advanced capabilities, there is an increased need to minimize the harm that could be caused to society by certa… (see more)in model outputs; hence, most LLMs have safety guardrails added, for example via fine-tuning. In this paper, we argue the position that current safety fine-tuning is very similar to a traditional cat-and-mouse game (or arms race) between attackers and defenders in cybersecurity. Model jailbreaks and attacks are patched with bandaids to target the specific attack mechanism, but many similar attack vectors might remain. When defenders are not proactively coming up with principled mechanisms, it becomes very easy for attackers to sidestep any new defenses. We show how current defenses are insufficient to prevent new adversarial jailbreak attacks, reward hacking, and loss of control problems. In order to learn from past mistakes in cybersecurity, we draw analogies with historical examples and develop lessons learned that can be applied to LLM safety. These arguments support the need for new and more principled approaches to designing safe models, which are architected for security from the beginning. We describe several such approaches from the AI literature.

2024-10-11

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)