Brain Canada FR

Creating computers that work like the human brain

By Brain Canada | Research stories
Yoshua Bengio, an AI researcher and professor at the University of Montreal. Photo: Josh Valcarcel/WIRED

Learning in Machines and Brains Senior Fellow, Joëlle Pineau, presents at the December 2015 meeting of CIFAR’s program Learning in Machines and Brains

For a list of the 38 fellows involved in this project, visit Learning in Machines and Brains is one of the 14 global research programs at CIFAR (Canadian Institute For Advanced Research). The CIFAR research programs were created to connect many of the world’s best minds – across borders and between disciplines – to shape new perspectives and spark groundbreaking ideas.

The Learning in Machines & Brains research program (formerly known as Neural Computation & Adaptive Perception) was founded in 2004 and is revolutionizing the field of artificial intelligence, and creating computers that think more like us – that can recognize faces, understand what is happening in a picture or video, and comprehend the actual meaning of language. The result will be computers that are not only powerful but intelligent, and that will be able to do everything from conduct a casual conversation to extract meaning from massive databases of information. The CIFAR program has shaken up the field of artificial intelligence by pioneering a technique called “deep learning”. A decade ago, CIFAR took a risk on researchers who wanted to revive interest in neural networks, a computer technique inspired by the human brain. This program brings together computer scientists, biologists, neuroscientists, psychologists and others, and the result was rich collaborations that have propelled artificial intelligence research forward.

The current co-directors of the program are Yoshua Bengio and Yann LeCun and there are currently 38 members (fellows) from around the globe in the program.

The program has produced several research outputs over the past year including several articles in prestigious science journals. A new research direction has emerged that looks at the application of deep learning in medicine. A group in Montreal is looking at biomedical image analysis and prediction of medical outcomes for personalized medicine and another group is looking at predicting complications after surgery. They have also organized program activities throughout the year. In May 2016, CIFAR partnered with RBC to present a moderated panel discussion in Toronto featuring Senior Fellow Brendan Frey (University of Toronto) and other participants from the investment and technology sectors to explore how artificial intelligence may disrupt the financial industry in future years. Over 500 individuals attended, in addition to a global audience reached by WebEx broadcast.

The program also convenes an annual summer school for its fellows’ graduate and postdoctoral trainees. Student organizers invite CIFAR fellows and other distinguished researchers to lecture on cutting-edge topics not yet covered in the regular university curricula. In 2015/2016, the program opened its doors to host the largest ever summer school in deep learning, attracting participants from around the world, coming from both academia and industry.

The program advanced one of its core research directions, deep learning, by making it the central theme of its annual program meeting in December 2016. This choice reflected a strong surge of interest within the program and the machine learning community at large, driven by a rapid series of outstanding successes in recent years that have seen deep learning embraced by global information companies like Google and Facebook, and the placement of CIFAR fellows at their research helms. The program meeting took place in Montreal during the two days preceding the annual Neural Information Processing Systems (NIPS) conference, the premier venue for the presentation of research in machine learning and neural networks.