Learning in Machines & Brains
How do we understand intelligence and build intelligent machines?
Learning in Machines & Brains program draws on neuro- and computer science to investigate how brains and artificial systems become intelligent through learning. The program’s fundamental approach – going back to basic questions rather than focusing on short-term technological advances – has the dual benefit of improving the engineering of intelligent machines and leading to new insights into human intelligence. The Learning in Machines & Brains program aims to understand the computational and mathematical principles that enable intelligence through distributed learning in neural networks, be it in brains or in machines.
The program has two key thrusts.
- It aims to use insights from neuroscience and cognitive science to further our understanding of learning in machines.
- It also aims to use insights from machine learning to advance our understanding of brains and cognition.
We wrestle with the complexities of how brains and modern AI systems can deal with complex environments, producing insights into how the world works and how we can control it. Fellows and scholars in the program focus on questions at the intersection of high-dimensional deep learning systems and neural circuits. Research will investigate novel reinforcement and unsupervised learning algorithms, credit assignment mechanisms, memory architectures and their analogues in the brain.
Impact Clusters
The Learning in Machines & Brains program is part of the following CIFAR Impact Clusters: Decoding Complex Brains and Data and Exploring Emerging Technologies. CIFAR’s research programs are organized into 5 distinct Impact Clusters that address significant global issues and are committed to fostering an environment in which breakthroughs emerge.
Research and Societal Impact Highlights
A partnership to advance artificial intelligence research
CIFAR’s Learning in Machines & Brains program has an ongoing partnership with Inria, the French national research institute for digital science and technology. Much like CIFAR, Inria encourages scientific risk-taking and interdisciplinarity and both organizations are leaders in pioneering new approaches to machine learning and AI.
Using AI to learn about the brain
CIFAR Fellow and Canada CIFAR AI Chair Blake Richards (McGill University, Mila) and Associate Fellow Joel Zylberberg (University of California, Los Angeles) led a workshop with experts from academia, industry and non-profit institutions to discuss the scale, scope, use cases, organizational structures and funding required to build neuro-foundation models, applying recent advances in AI to better understand the brain. These big-scale machine learning models, pre-trained on large quantities of data, can be adapted to new tasks with relatively small amounts of new training data and computational power,and could potentially serve both the neuroscience and neurotechnology communities, while also benefiting machine-learning research.
Bridging the gap between machine learning and human intelligence
Advisory Committee Chair Yoshua Bengio’s (LawZero, IVADO, Canada CIFAR AI Chair at Mila, Université de Montréal) research group is developing new theories to bridge the gap between current machine-learning techniques and human intelligence. By studying the kind of inductive biases that humans and animals exploit, CIFAR researchers are clarifying the principles that are hypothesized to guide human and animal intelligence and which could provide inspiration for both AI research and neuroscience. Work continues to develop AI systems that can exhibit flexible out-of-distribution learning and systematic generalization, areas where contemporary machine learning approaches still lag human cognitive abilities.
Founded
2004
Renewal Dates
2008, 2014, 2019, 2025
Partners
Inria
Interdisciplinary Collaboration
Computer science
Artificial intelligence
Deep learning
Reinforcement learning
Neuroscience
Bioinformatics
Computational biology
Statistics
Data science
Psychology
CIFAR Contact
Fellows & Advisors
Program Directors
Konrad Kording
Program Co-Director
Timothy Lillicrap
Program Co-Director
Fellows
Stefan Bauer
Fellow
CIFAR Azrieli Global Scholar 2020-2022
Marc G. Bellemare
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
McGill University
Mila
Reliant AI
Léon Bottou
Associate Fellow
New York University
Kyunghyun Cho
Associate Fellow
CIFAR Azrieli Global Scholar 2017-2019
Eva Dyer
Fellow
CIFAR Azrieli Global Scholars 2022-2024
Chelsea Finn
Associate Fellow
Nando de Freitas
Associate Fellow
Alona Fyshe
Fellow
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
National Program Committee member
Pan-Canadian AI Strategy
University of Alberta
Surya Ganguli
Fellow
Alison Gopnik
Fellow
Advisor
Learning in Machines & Brains
Geoffrey Hinton
Distinguished Fellow
Vector Institute
Simon Kornblith
Associate Fellow
Simon Lacoste-Julien
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Samsung SAIT AI Lab in Montreal
Université de Montréal
Nikolay Malkin
Fellow
Lerrel Pinto
Fellow
Doina Precup
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
McGill University
Mila
Blake Richards
Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Mila
Benjamin Rosman
Fellow
CIFAR Azrieli Global Scholars 2022-2024
Andrew Saxe
Fellow
CIFAR Azrieli Global Scholar 2020-2022
Pascal Vincent
Associate Fellow
Université de Montréal
Max Welling
Fellow
Martha White
Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
University of Alberta
Joel Zylberberg
Fellow
CIFAR Azrieli Global Scholar 2016-2018
Advisors
Yoshua Bengio
Advisory Committee Chair
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Mila
Université de Montréal
Hugo Larochelle
Advisory Committee Member
Mila
Université de Montréal
Yann LeCun
Advisor
Facebook Professor
New York University
Bernhard Schölkopf
Advisor
Max Planck Institute for Intelligent Systems
Sebastian Seung
Advisory Committee Member
Samsung
CIFAR Global Scholars
Kelsey Allen
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2026 - 2028
Pan-Canadian AI Strategy
Vector Institute
Stefan Bauer
Fellow
CIFAR Azrieli Global Scholar 2020-2022
Quentin Bertrand
CIFAR Global Scholar 2026-2028
Kyunghyun Cho
Associate Fellow
CIFAR Azrieli Global Scholar 2017-2019
Eva Dyer
Fellow
CIFAR Azrieli Global Scholars 2022-2024
Alona Fyshe
Fellow
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
National Program Committee member
Pan-Canadian AI Strategy
University of Alberta
Marzyeh Ghassemi
CIFAR Azrieli Global Scholar 2020-2022
Erin Grant
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2026 - 2028
Pan-Canadian AI Strategy
University of Alberta
Emma Pierson
CIFAR Azrieli Global Scholars 2022-2024
Benjamin Rosman
Fellow
CIFAR Azrieli Global Scholars 2022-2024
Andrew Saxe
Fellow
CIFAR Azrieli Global Scholar 2020-2022
Graham Taylor
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
Pan-Canadian AI Strategy
University of Guelph
Vector Institute
Joel Zylberberg
Fellow
CIFAR Azrieli Global Scholar 2016-2018
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