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The Brains Behind AI

Nov 5 / 19

The Brains Behind AI is a 10-part video series that provides a behind-the-scenes look at some of Canada’s most talented researchers in artificial intelligence, what areas of research they are focusing on over the next five years as Canada CIFAR AI Chairs and CIFAR fellows, and what motivates them to pursue research in a variety of areas that will transform the way we live and work.

CIFAR is leading the Government of Canada's $125 million Pan-Canadian Artificial Intelligence Strategy, working in partnership with three newly established AI Institutes - Amii in Edmonton, Mila in Montreal and the Vector Institute in Toronto.

Join the conversation on Twitter using #RealBrains with @CIFAR_News.



What is Canada doing to support equity, diversity and inclusion in AI research and development? Dr. Elissa Strome is AVP Research and the Executive Director for the Pan-Canadian AI Strategy at CIFAR. She's one of the brains behind the CIFAR Pan-Canadian AI Strategy. It is the world's first national AI Strategy, supporting better equity, diversity and inclusion in AI research, training for the next generation, and in the development of future products and services.


 


How do we make neural networks faster? Canada CIFAR AI Chair Jimmy Ba is the brains behind the Adam Optimizer, one of the go-to algorithms to train deep learning models. A faculty member of the Vector Institute in Toronto and Assistant Professor in the Department of Computer Science at the University of Toronto, he's leading research in AI to develop better technologies for all.





Can we train computers to understand language the way humans do? And can we use this language to build a virtual 3-D world? Canada CIFAR AI Chair Angel Chang is using AI to convert natural language into 3-D representations of the world. This will better prepare the systems of the future to interpret and act in the real world. Angel is a member of the Alberta Machine Intelligence Institute (Amii) and an assistant professor at Simon Fraser University.





Could AI solve the mystery of human and animal intelligence? Canada CIFAR AI Chair Yoshua Bengio is pioneering research in deep learning and is one of the brains behind artificial neural networks, an approach that teaches computers to mimic human intelligence. Yoshua is co-director and fellow of CIFAR’s Learning in Machines & Brains program a professor at Université de Montréal, founder and scientific director of Mila, scientific director of IVADO. He is a Canada Research Chair in Statistical Learning Algorithms and a recipient of the 2018 A.M Turing Award, often referred to as the “Nobel Prize” in computing for his contributions to deep learning.





How do we teach machines to make intelligent decisions in the real world? Canada CIFAR AI Chair Martha White is using reinforcement learning techniques to train autonomous agents to make smarter and more efficient decisions through trial and error. The applications are wide-ranging and could be used in autonomous vehicles and robotics, as well as industries such as finance, manufacturing and more. .Martha White is a member of Amii, an assistant professor at the University of Alberta’s Department of Computing Science, and the director of RLAI.





How does your brain determine what’s important enough to remember?  Canada CIFAR AI Chair and CIFAR Fellow Blake Richards is using artificial intelligence to better understand the fundamental principles of intelligence for both machines and brains. Blake is a fellow in the CIFAR Learning in Machines & Brains program, a member of Mila and professor at the Montréal Neurological Institute and the School of Computer Science at McGill University.





When computers see an image, how do they perceive them? Canada CIFAR AI Chair Alona Fyshe is pioneering research in computer vision and helping computers to see the world the way humans do. Alona is a fellow in the CIFAR Child & Brain Development program, an assistant professor at the University of Alberta and a member of Amii.





Neural networks have revolutionized areas such as computer vision and natural language processing but how do we train them to be faster and more efficient? Canada CIFAR AI Chair Roger Grosse is helping machines to better predict and adapt to different situations. Roger is an assistant professor of computer science at the University of Toronto, a founding member of the Vector Institute, a Canada Research Chair in Probabilistic Inference and Deep Learning and co-creator of Metacademy.





The AI systems of the future, such as robots and autonomous vehicles, will play a more integrated role in society. Canada CIFAR AI Chair Graham Taylor is exploring how to make machines more accurate and robust so that they can better interact with humans in the real world. Graham is a fellow in the CIFAR Learning in Machines & Brains program, a CIFAR Azrieli Global Scholar, a faculty member at the Vector Institute, an associate professor of engineering at the University of Guelph and an academic director at Next AI.





Solving the mysteries of machine intelligence could lead to more profound answers about our own human intelligence. Rich Sutton is pioneering the field of reinforcement learning, a type of machine learning that allows machines to learn from interactions with its environment. RIch is a senior fellow at CIFAR, a fellow at Amii, a professor at the University of Alberta and founder of DeepMind.





Machines learn best when they have lots of data, but large datasets are not always easy to come by. Canada CIFAR AI Chair Hugo Larochelle is advancing research in few shot learning, a technique commonly employed in computer vision. Hugo is looking at ways to make systems faster and more accurate in the absence of lots of training data, a feat that could make machine learning more accessible for broader audiences. Hugo is an associate fellow in the CIFAR Learning in Machines & Brains program, a faculty member of Mila, a professor at the Université de Montréal. He leads the Google Brain group in Montréal.





Canada CIFAR AI Chair James Wright is using machine learning algorithms to predict human strategic behaviour, an approach that seeks to find ways to align interests and goals between many people, things and situations. His research will have considerable impact to the areas of economics, commerce and market. It could revolutionize how we organize online platforms to benefit all. James is a fellow at Amii, an assistant professor at the University of Alberta.