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  • Learning in Machines & Brains

Learning in Machines & Brains

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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.

optimal stimuli
How Google sees you and your cat. These “optimal stimuli” for both human and cat faces resulted from training a deep learning network on more than 10 million pictures

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.

Neural Network
A deep learning network takes in raw information, such as values for individual pixels, in the top input layer, and processes it through one or more hidden layers, with each layer adding a further level of abstraction.

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

Rachel Parker

Fellows & Advisors

Program Directors

Yoshua Bengio

Yoshua Bengio

Program Co-Director
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
IVADO
Mila
Université de Montréal
Canada
Konrad Kording

Konrad Kording

Program Co-Director

Learning in Machines & Brains
University of Pennsylvania
United States

Fellows

Marc G. Bellemare

Marc G. Bellemare

Associate Fellow
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
Google Brain
McGill University
Mila
Canada
Léon Bottou

Léon Bottou

Fellow

Learning in Machines & Brains
Facebook AI Research
New York University
France
Kyunghyun Cho

Kyunghyun Cho

Fellow
CIFAR Azrieli Global Scholar 2017-2019

Learning in Machines & Brains
New York University
United States
Aaron Courville

Aaron Courville

Fellow
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
Mila
Université de Montréal
Canada
Emmanuel Dupoux

Emmanuel Dupoux

Fellow

Learning in Machines & Brains
School for Advanced Studies in the Social Sciences (EHESS)
France
Rob Fergus

Rob Fergus

Associate Fellow

Learning in Machines & Brains
New York University
United States
Chelsea Finn

Chelsea Finn

Fellow

Learning in Machines & Brains
Stanford University
United States
Nando de Freitas

Nando de Freitas

Associate Fellow

Learning in Machines & Brains
University of Oxford
United Kingdom
Alona Fyshe

Alona Fyshe

Fellow
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018

Learning in Machines & Brains
Pan-Canadian AI Strategy
Amii
University of Alberta
Canada
Surya Ganguli

Surya Ganguli

Fellow

Learning in Machines & Brains
Stanford University
United States
Aapo Johannes Hyvärinen

Aapo Johannes Hyvärinen

Fellow

Learning in Machines & Brains
University of Helsinki
Finland
Simon Lacoste-Julien

Simon Lacoste-Julien

Associate Fellow
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
Mila
Samsung SAIT AI Lab in Montreal
Université de Montréal
Canada
Hugo Larochelle

Hugo Larochelle

Associate Fellow
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
Google Brain
Mila
Université de Montréal
Canada
Christopher Manning

Christopher Manning

Fellow

Learning in Machines & Brains
Stanford University
United States
Doina Precup

Doina Precup

Fellow
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
DeepMind
McGill University
Mila
Canada
Blake Richards

Blake Richards

Fellow
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
McGill University
Mila
Canada
Bernhard Schölkopf

Bernhard Schölkopf

Fellow

Learning in Machines & Brains
ETH Zürich
Max Planck Institute for Intelligent Systems
Germany
Richard S. Sutton

Richard S. Sutton

Associate Fellow
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
Amii
DeepMind
University of Alberta
Canada
Raquel Urtasun

Raquel Urtasun

Associate Fellow

Learning in Machines & Brains
Uber ATG
University of Toronto
Canada
Pascal Vincent

Pascal Vincent

Associate Fellow

Learning in Machines & Brains
Mila
Université de Montréal
Canada
Max Welling

Max Welling

Fellow

Learning in Machines & Brains
University of Amsterdam
The Netherlands
Richard Zemel

Richard Zemel

Associate Fellow
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
Google Brain
University of Toronto
Vector Institute
Canada
Joel Zylberberg

Joel Zylberberg

Associate Fellow
CIFAR Azrieli Global Scholar 2016-2018

Learning in Machines & Brains
York University
Canada

Advisors

Pieter Abbeel

Pieter Abbeel

Advisor

Learning in Machines & Brains
University of California Berkeley
United States
Yann LeCun

Yann LeCun

Advisor

Learning in Machines & Brains
Chief AI Scientist
Facebook Professor
New York University
United States
Joelle Pineau

Joelle Pineau

Advisor
Canada CIFAR AI Chair

Learning in Machines & Brains
Pan-Canadian AI Strategy
Facebook
McGill University
Mila
Canada
Terrence J. Sejnowski

Terrence J. Sejnowski

Advisor

Learning in Machines & Brains
Salk Institute for Biological Studies
United States
Sebastian Seung

Sebastian Seung

Advisory Committee Chair

Learning in Machines & Brains
Princeton University
Samsung
United States
Christopher K. I. Williams

Christopher K. I. Williams

Advisor

Learning in Machines & Brains
University of Edinburgh
United Kingdom

CIFAR Azrieli Global Scholars

Stefan Bauer

Stefan Bauer

CIFAR Azrieli Global Scholar 2020-2022

Learning in Machines & Brains
KTH Royal Institute of Technology
Sweden
Kyunghyun Cho

Kyunghyun Cho

Fellow
CIFAR Azrieli Global Scholar 2017-2019

Learning in Machines & Brains
New York University
United States
Eva Dyer

Eva Dyer

CIFAR Azrieli Global Scholars 2022-2024

Learning in Machines & Brains
Georgia Institute of Technology
United States
Alona Fyshe

Alona Fyshe

Fellow
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018

Learning in Machines & Brains
Pan-Canadian AI Strategy
Amii
University of Alberta
Canada
Marzyeh Ghassemi

Marzyeh Ghassemi

CIFAR Azrieli Global Scholar 2020-2022

Learning in Machines & Brains
Massachusetts Institute of Technology
United States
Emma Pierson

Emma Pierson

CIFAR Azrieli Global Scholars 2022-2024

Learning in Machines & Brains
Cornell Tech and Technion - IIT
United States
Benjamin Rosman

Benjamin Rosman

CIFAR Azrieli Global Scholars 2022-2024

Learning in Machines & Brains
University of the Witwatersrand
South Africa
Andrew Saxe

Andrew Saxe

CIFAR Azrieli Global Scholar 2020-2022

Learning in Machines & Brains
University College London
United Kingdom
Graham Taylor

Graham Taylor

Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018

Learning in Machines & Brains
Pan-Canadian AI Strategy
NextAI
University of Guelph
Vector Institute
Canada
Joel Zylberberg

Joel Zylberberg

Associate Fellow
CIFAR Azrieli Global Scholar 2016-2018

Learning in Machines & Brains
York University
Canada

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