Artificial Intelligence in the Great White North, Part 1: How did Canada become a world leader in AI?

In 2017, in front an audience of students, Russian President Vladimir Putin spoke about the revolutionary implications of Artificial Intelligence (AI). He argued that “Whoever becomes the leader in AI will be the leader of the world”.

Indeed, AI will generate significant productivity and economic gains across a wide range of industries leading to double-digit growth rates. According to a 2017 PwC report, AI might add $15.7 trillion to the world economy by 2030, including $6.6 trillion from increased productivity and $9.1 trillion from greater consumption. In consequence, a country leading in AI will not only maintain its economic position on the world stage but can improve it.

In the context of this AI revolution, according to a 2017 study from the Wuzhen Institute, Canada stands as the 5th global AI hub following the US, China, Britain and India, with 228 organizations focused on AI such as academic institutes, startups and multinational tech firms. Global tech players working on AI in Canada include Google, Facebook, Microsoft, IBM and Samsung among others. The bulk of Canadian AI activity is concentrated in three superclusters: Montreal, Edmonton and the Toronto-Waterloo corridor. Canada is known among tech circles as the birthplace of the latest AI innovation breakthroughs called Machine Learning (ML) and most notably Deep Learning (DL) originating from Toronto and Montreal, as well as Reinforcement Learning (RL) coming from Edmonton.

As a reminder, ML is an AI method where a computer is trained on large amounts of data to identify and act according to patterns. DL is a superior variant of ML using a neural network like a human brain able to train with an even larger amount of data to identify and act according to more nuanced patterns. This method was pioneered by Geoffrey Hinton and Yan Le Cun from University of Toronto and Yoshua Bengio from Université de Montréal. As for RL, it is another subset of ML where a machine is trained through environmental feedback thus enabling the computer to define patterns by identifying which actions are helpful or harmful in reaching a goal. This approach was conceived by Richard Sutton from University of Alberta.

It is worth noting that the foundation of Canada’s global lead in AI lies in the possession of three key resources in ever-growing quantities, that are necessary to produce cutting-edge AI solutions. The first resource is data which is the fuel for ML algorithms and a lot of it is required to train a machine able to identify and act according to patterns. Secondly, to process the vast amount of data, the second key resource required is high-performance hardware with significant computing power (or compute). Finally, the third pre-requisite is a talent pool capable of conducting AI innovation consisting of fundamental and applied research.

How did Canada come to possess those three essential resources in vast and increasing quantities in the first place?

In large part, the answer lies in early public funding in AI fundamental research. Indeed, it is mostly government financial support that enabled Canada to gain a first-mover advantage in AI through the pioneering work of its scientists in DL and RL. This voluntarist government approach originated in the early 1980s when Ottawa started investing in AI research despite an AI winter where resources were scarce, and research hit a brick wall. It was during this time when the Canadian scientists mentioned earlier theorized DL and RL. However, there wasn’t enough data and compute to apply those concepts. It was only in the late 2000s that technology caught up and provided those researchers with sufficiently large datasets and enough data crunching power enabling them to prove their theories correct. Those innovation breakthroughs caught the attention of mostly American global tech firms who recruited some of Canada’s AI pioneers and brought them to the US, where they will continue their work in fundamental and applied research. Indeed, compared to Canada, Big Tech in the US owns a massive amount of consumer data indispensable for training ML algorithms with commercial applications, in addition to high performance compute. For instance, Geoffrey Hinton was recruited by Google while Yan Le Cun went on to work with Facebook.

Fearing a brain drain where Canada would simply be an AI talent pipeline, Ottawa opted to create a national AI ecosystem able to compete on the world stage. Federal and provincial levels of government would provide funding to develop one of the three essential resources indispensable to produce top-notch AI capabilities: the talent pool able to conduct fundamental AI research leading to breakthrough innovations like DL; and applied research leading to incremental innovation. As such,  public authorities would double down on AI education where it would not only fund the training and recruitment of top AI researchers but would also financially support AI innovations undertaken by those said scientists. Ultimately, the creation of such a talent pool would attract the other two key resources that are data and compute coming from private sector actors interested in AI. In addition to attracting data and compute from private players, an emerging AI talent pool would also entice private funding that would supplement government money destined for AI innovation.

Concretely, this public funding would mostly go to established and newly created AI academic institutes that will recruit top AI researchers like the Canadian AI pioneers as well as train the new batch of scientists. These entities include Université de Montréal’s Montreal Institute for Learning Algorithms (MILA) headed by Yoshua Bengio, the Vector Institute under Geoffrey Hinton affiliated with University of Toronto and the Alberta Machine Intelligence Institute (AMII) linked to the University of Alberta with Richard Sutton. Within those academic institutes, researchers would have one foot in the academic world to undertake mostly fundamental AI research, while having the other foot in the private sector to conduct mainly applied AI research.

By adopting such an approach, Canada hopes that instead of simply recruiting grey matter to bring back to their home country, private sector actors would set up shop in Canadian cities harboring those AI institutes, with the aim of tapping into a skilled workforce undertaking fundamental and applied AI research. Consequently, those private sector players would bring with them data, compute as well as funding to complement public financial support in AI innovation. Indeed, since there is currently a global scarcity of talent for AI research and integration, private sector players are fiercely competing to capture all the talents they can find in the world to gain an edge over competitors.

Today, because of sustained government support in AI education, Canada boasts the 3rd largest AI talent pool in the world making it an attractive AI R/D powerhouse enticing global tech actors and startups.

Part 2 will examine the results of Canada’s education-focused policy aimed at making the True North a global AI hub. As such, this series’ next installment will focus on the successes of the Canadian strategy as well as the challenges encountered.

Featured Image: a picture of a robot from Pixabay

Disclaimer: Any views or opinions expressed in articles are solely those of the
authors and do not necessarily represent the views of the NATO Association of

About Alexis Amini

Alexis Amini – editor for the Canadian Armed Forces program – is a graduate student in public and international affairs at Université de Montréal (UdeM), Québec. He has a BSc in political science from the same university. Having lived in Djibouti and the United Arab Emirates where he witnessed major geopolitical events, Alexis developed a passion for international security. His research focus revolves around geopolitics, defense policies and political risk analysis. Upon completion of his master’s program, Alexis intends to join the strategic intelligence industry.