Learning in Machines & Brains
How do we understand intelligence and build intelligent machines?
Artificial Intelligence has created a global industry that touches on every business sector imaginable — from improved security of our banking to innovation in farming, education, law enforcement, health care, space exploration and customer service.
The Learning in Machines & Brains program played a major part in the revolution by examining how artificial neural networks could be inspired by the human brain, and developing the powerful technique of deep learning.
Now the program is expanding our understanding of the fundamental computational and mathematical principles that enable intelligence through learning, whether in brains or in machines.
Current AI systems are limited in their ability to understand the world around us. This program attacks those limitations by going back to basic questions rather than focusing on short-term technological advances. This fundamental approach has the dual benefit of improving the engineering of intelligent machines and explaining intelligence.
Details for applicants to the CIFAR Azrieli Global Scholars program
A core research direction that is fueling this investigation from the machine learning point of view is deep learning, which studies brain-inspired algorithms that allow machines to gather knowledge from large quantities of data and discover multiple levels of representation of that data. In the last few years, deep learning methods have revolutionized artificial intelligence (AI) research and industrial development. The Learning in Machines & Brains program is particularly interested in pursuing deep learning methods that actively interact with their environment. Such methods will further broaden the impact of deep learning systems, as we allow them to directly interact with people (e.g. for personal assistants) and things (e.g. for robotics). Action and interaction are necessary for intelligent systems to acquire world models that are truly grounded and actionable representations of the world, enable prediction, reasoning and planning, and are thus a crucial step towards the development of AI beyond the recent success in machine perception. Fellows and scholars in the program will focus on the development of agent learning procedures and new forms of active unsupervised learning, or who have an interest in moving deep learning from low-level perception and action to higher level cognitive and linguistic skills, which used to be central to the goals of classical symbolic AI. In addition, causal inference from any number of relevant fields including computer science, statistics, and economics is a new stream of research relevant to the program.
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. PDF
Y. Bengio and P. Lamblin and D. Popovici and H. Larochelle, “Greedy Layer-Wise Training of Deep Networks,” Neural Information Processing Systems Proceedings (2006). PDF
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). PDF
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). PDF
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.
Do you want to shape the future of ethical AI?
Contact: Fiona Cunningham, Director of Innovation
Founded
2004
Renewal Dates
2008, 2014, 2019
Supporters
Alfred P. Sloan Foundation, Bristol Gate Capital Partners, Facebook
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
Yann LeCun
Program Co-Director
Facebook Professor
New York University
Fellows
Marc G. Bellemare
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
McGill University
Mila
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
Alona Fyshe
Fellow
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
Pan-Canadian AI Strategy
University of Alberta
Simon Lacoste-Julien
Associate Director
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
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
Pascal Vincent
Associate Fellow
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
Université de Montréal
Richard Zemel
Canada CIFAR AI Chair
Vector Institute
Joel Zylberberg
Associate Fellow
CIFAR Azrieli Global Scholar 2016-2018
Advisors
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
Marzyeh Ghassemi
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2020-2022
Pan-Canadian AI Strategy
Vector Institute
Andrew Saxe
CIFAR Azrieli Global Scholar 2020-2022
Graham Taylor
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
Pan-Canadian AI Strategy
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.