Learning in Machines & Brains
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.
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.
Advancing key research on machine learning
Program members published important findings this year. Fellow Bernhard Schölkopf (ETH Zurich, Max Planck Institute for Intelligent Systems) published a paper exploring cross-pollination between the fields of machine learning and graphical causality, discussing the implications and intersections for both communities. Program Co-Director Konrad Kording (University of Pennsylvania) and colleagues presented a paper at the first Causal Learning and Reasoning Conference, partially funded by the Sloan Foundation, on a study that used instrumental variables for causal inference. The study frames exclusion as a data-driven estimation problem and applies flexible machine learning methods to estimate the probability of a unit complying with the instrument. Fellow Chelsea Finn (Stanford University) published a paper on how progress in machine learning stems from a combination of data availability, computational resources and an appropriate encoding of inductive biases.
Developing a neuroscientific data collection model
Fellows Blake Richards (Mila, McGill University) and Joel Zylberberg (York University) engaged with a number of leaders in the neurotech industry and the Canadian neuroscience community to explore opportunities for large-scale neuroscientific data collection that will help build foundational models for neural computation.
Exploring the ethics of artificial intelligence
Building on two previous roundtables on ethical AI, program members and Canada CIFAR AI Chairs participated in a virtual meeting in February 2022, organized by CIFAR in conjunction with the Ada Lovelace Institute (UK) and the Partnership on AI. This meeting brought together scholars and scientists from academia and industry to develop recommendations for best practices in ethical review at AI and machine learning conferences.
SELECTED PAPERS
Hinton, G. E., Osindero, S. and Teh, Y. (2006). “A fast learning algorithm for deep belief nets.” Neural Computation, 18, pp 1527-1554.
Y. Bengio and P. Lamblin and D. Popovici and H. Larochelle, “Greedy Layer-Wise Training of Deep Networks,” Neural Information Processing Systems Proceedings (2006).
Salakhutdinov, R. and Hinton, G., “Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure,” Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 412-419 (2007).
Graves, A., Mohamed, A., Hinton, G. E., “Speech Recognition with Deep Recurrent Neural Networks,” 39th International Conference on Acoustics, Speech and Signal Processing, Vancouver (2013).
Yann LeCun, Yoshua Bengio and Geoffrey Hinton. (2015). “Deep Learning.” Nature, 521, pp 436–444. ABSTRACT
Path to Societal Impact
We invite experts in industry, civil society, healthcare and government to join fellows in our Learning in Machines & Brains program for in-depth, cross-sectoral conversations that drive change and innovation.
Social scientists, industry experts, policymakers and CIFAR fellows in the Learning in Machines & Brains program are addressing complex ethical issues in research and training environments and in the implementation of AI.
Areas of focus:
- Exploring existing and future societal implications of AI research.
- Addressing issues in AI research and implementation, including privacy, accountability, and transparency.
Founded
2004
Renewal Dates
2008, 2014, 2019
Partners
Brain Canada Foundation through the Canada Brain Research Fund, Inria
Supporters
Alfred P. Sloan Foundation, Meta Platforms, Inc., RBC Foundation
Interdisciplinary Collaboration
Computer science, including artificial intelligence, deep learning, reinforcement learning
Neuroscience
Bioinformatics
Computational biology
Statistics
Data science
Psychology
CIFAR Contact
Fellows & Advisors
Program Directors
Yoshua Bengio
Program Co-Director
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Mila
Université de Montréal
Konrad Kording
Program Co-Director
Fellows
Marc G. Bellemare
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
McGill University
Mila
Léon Bottou
Fellow
New York University
Kyunghyun Cho
Fellow
CIFAR Azrieli Global Scholar 2017-2019
Aaron Courville
Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Université de Montréal
Emmanuel Dupoux
Fellow
Rob Fergus
Associate Fellow
Chelsea Finn
Fellow
Nando de Freitas
Associate Fellow
Alona Fyshe
Fellow
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
Pan-Canadian AI Strategy
University of Alberta
Surya Ganguli
Fellow
Aapo Johannes Hyvärinen
Fellow
Simon Lacoste-Julien
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Samsung SAIT AI Lab in Montreal
Université de Montréal
Hugo Larochelle
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Mila
Université de Montréal
Christopher Manning
Fellow
Doina Precup
Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
McGill University
Mila
Blake Richards
Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Mila
Bernhard Schölkopf
Fellow
Max Planck Institute for Intelligent Systems
Richard S. Sutton
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
DeepMind
University of Alberta
Raquel Urtasun
Associate Fellow
University of Toronto
Pascal Vincent
Associate Fellow
Université de Montréal
Max Welling
Fellow
Richard Zemel
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
University of Toronto
Vector Institute
Joel Zylberberg
Associate Fellow
CIFAR Azrieli Global Scholar 2016-2018
Advisors
Pieter Abbeel
Advisor
Yann LeCun
Advisor
Facebook Professor
New York University
Joelle Pineau
Advisor
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
McGill University
Mila
Terrence J. Sejnowski
Advisor
Sebastian Seung
Advisory Committee Chair
Samsung
Christopher K. I. Williams
Advisor
CIFAR Azrieli Global Scholars
Stefan Bauer
CIFAR Azrieli Global Scholar 2020-2022
Kyunghyun Cho
Fellow
CIFAR Azrieli Global Scholar 2017-2019
Eva Dyer
CIFAR Azrieli Global Scholars 2022-2024
Alona Fyshe
Fellow
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
Pan-Canadian AI Strategy
University of Alberta
Marzyeh Ghassemi
CIFAR Azrieli Global Scholar 2020-2022
Emma Pierson
CIFAR Azrieli Global Scholars 2022-2024
Benjamin Rosman
CIFAR Azrieli Global Scholars 2022-2024
Andrew Saxe
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
Associate Fellow
CIFAR Azrieli Global Scholar 2016-2018
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CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.