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Learning in Machines & Brains

How do we understand intelligence and build intelligent machines?

Learning in Machines & Brains

Overview

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.


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

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.


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. 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

Founded in: 2004


Members: 38


Renewal Dates: 2008, 2014, 2019


Supporters:
Bristol Gate Capital Partners, Facebook, Céline and Jacques Lamarre


Partners:
Brain Canada Foundation through the Canada Brain Research Fund, Inria


Disciplines:
Computer science, including artificial intelligence & machine learning; neuroscience; bioinformatics & computational biology


Next Program Meeting: December 2019


Contact: Rachel Parker

Fellows & Advisors

Program Directors

LMB_YoshuaBengio
Yoshua Bengio
Program Co-Director

Yoshua Bengio’s current interests include fundamental questions on deep learning, the geometry of generalization in high-dimensional spaces, biologically inspired learning algorithms, and challenging applications of statistical machine learning in artificial intelligence tasks.

LMB_YannLaCun
Yann LeCun
Program Co-Director

Yann LeCun’s research interests include computational and biological models of learning and perception

LMB_HugoLarochelle
Hugo Larochelle
Associate Program Director

Hugo Larochelle is a computer scientist whose research focuses on machine learning, i.e., on the development of algorithms capable of extracting concepts and abstractions from data.

Fellows

LMB_FrancisBach

Francis Bach

  • Fellow
  • Learning in Machines & Brains
  • École normale supérieure Paris
  • Inria
  • France
Marc G. Bellemare

Marc G. Bellemare

  • Associate Fellow
  • Learning in Machines & Brains
  • McGill University
  • Google Brain
  • Canada
Bottou_BW

Léon Bottou

  • Fellow
  • Learning in Machines & Brains
  • New York University
  • Facebook AI Research
  • France
LMB_KyunghyunCho

Kyunghyun Cho

  • Associate Fellow
  • Learning in Machines & Brains
  • New York University
  • United States
Bio Outline

Aaron Courville

  • Fellow
  • Learning in Machines & Brains
  • Université de Montréal
  • Canada
Bio Outline

Emmanuel Dupoux

  • Fellow
  • Learning in Machines & Brains
  • School for Advanced Studies in the Social Sciences (EHESS)
Bio Outline

Rob Fergus

  • Associate Fellow
  • Learning in Machines & Brains
  • New York University
  • United States
ChelseaFinn_BW

Chelsea Finn

  • Fellow
  • Learning in Machines & Brains
  • Stanford University
  • United States
LMB_NandoDeFreitas

Nando de Freitas

  • Associate Fellow
  • Learning in Machines & Brains
  • University of Oxford
  • United Kingdom
BMC_AlonaFyshe

Alona Fyshe

  • Fellow
  • Learning in Machines & Brains
  • University of Alberta
  • Canada
LMB_SuryaGanguli

Surya Ganguli

  • Fellow
  • Learning in Machines & Brains
  • Stanford University
  • United States
LMB_AapoHyvarinen

Aapo Johannes Hyvärinen

  • Fellow
  • Learning in Machines & Brains
  • University College London
  • Finland
LMB_KonradKording

Konrad Kording

  • Fellow
  • Learning in Machines & Brains
  • University of Pennsylvania
  • United States
LMB_Simon

Simon Lacoste-Julien

  • Associate Fellow
  • Learning in Machines & Brains
  • Université de Montréal
  • Canada
LMB_HugoLarochelle

Hugo Larochelle

  • Associate Fellow
  • Learning in Machines & Brains
  • Université de Montréal
  • Google Brain
  • Canada
LMB_ChristopherManning

Christopher Manning

  • Fellow
  • Learning in Machines & Brains
  • Stanford University
  • United States
Bio Outline

Doina Precup

  • Fellow
  • Learning in Machines & Brains
  • McGill University
  • Canada
LMB_BlakeRichards

Blake Richards

  • Fellow
  • Learning in Machines & Brains
  • McGill University
  • Canada
Scholkopf_BW

Bernhard Schölkopf

  • Fellow
  • Learning in Machines & Brains
  • ETH Zurich
  • Max Planck Institute for Intelligent Systems
  • Germany
LMB_RichardSutton

Richard Sutton

  • Associate Fellow
  • Learning in Machines & Brains
  • University of Alberta
  • Canada
Bio Outline

Raquel Urtasun

  • Fellow
  • Learning in Machines & Brains
  • University of Toronto
  • Uber ATG
Bio Outline

Pascal Vincent

  • Associate Fellow
  • Learning in Machines & Brains
  • Université de Montréal
  • Canada
LMB_MaxWelling

Max Welling

  • Fellow
  • Learning in Machines & Brains
  • University of Amsterdam
  • The Netherlands
LMB_RichardZemel

Richard Zemel

  • Associate Fellow
  • Learning in Machines & Brains
  • University of Toronto
  • Canada
LMB_JoelZylberberg

Joel Zylberberg

  • Associate Fellow
  • Learning in Machines & Brains
  • York University
  • Canada

Advisors

LMB_PieterAbbeel

Pieter Abbeel

  • Advisor
  • Learning in Machines & Brains
  • University of California Berkeley
  • United States
Bio Outline

Raia Hadsell

  • Advisor
  • Learning in Machines & Brains
LMB_GeoffHinton

Geoffrey Hinton

  • Advisor
  • Learning in Machines & Brains
  • University of Toronto
  • Google
  • Canada
LMB_JoellePineau

Joelle Pineau

  • Advisor
  • Learning in Machines & Brains
  • McGill University
  • Canada
Bio Outline

Terrence J. Sejnowski

  • Advisor
  • Learning in Machines & Brains
  • Salk Institute for Biological Studies
  • United States
LMB_SebastianSeung

Sebastian Seung

  • Advisor
  • Learning in Machines & Brains
  • Princeton University
  • United States
LMB_ChristopherWilliams

Christopher K. I. Williams

  • Advisor
  • Learning in Machines & Brains
  • University of Edinburgh
  • United Kingdom

CIFAR Azrieli Global Scholars

LMB_KyunghyunCho

Kyunghyun Cho

  • CIFAR Azrieli Global Scholar 2017-2019
  • Learning in Machines & Brains
  • New York University
  • United States
LMB_JoelZylberberg

Joel Zylberberg

  • CIFAR Azrieli Global Scholar 2016-2018
  • Learning in Machines & Brains
  • York University
  • Canada

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