Jennifer Yi

The long shadow that United States casts over Canada is ubiquitous across all industries; there are US companies eclipsing Canadian companies through merger and take-over, the dominating role in entertainment by Hollywood and the large migration of skilled professionals and top researchers from Canada to the United States in the technology industry. Elon Musk, founder of Paypal, SpaceX and Tesla, had a short stint as an undergraduate at Queen’s University before leaving for UPenn. He believed the move would be propitious to his career –a prediction that turned out correctly. In the twenty years since, the trend continues.

When I log onto Facebook, I see my friends who graduated from computer science and engineering are either already there, or are gravitating toward the US. The University of Waterloo is recognized as the school that produces top engineering and computer science graduates in Canada, and incidentally, it is known as a school where Bay-area technology companies get their recruits. It has since sent 600 alumni to Microsoft alone and has about 2000 alumni in California, many of them working as engineers.

And the reasoning for young professionals is in plain sight – higher salaries, access to a competitive market, and perks such as food budget for employees helps too (Google’s food budge for its employees is worth at least $20 per day). While the US affords greater opportunities for growth in the private sector, there are similar allures in its public sectors too.

Many have a hard time believing that Canada houses some of the world’s top researchers and professors in Machine Learning – a subfield of computer science that is generating a lot of discussion for pushing the frontier in artificial intelligence. Evolved from the study of pattern recognition and computational learning theory, its construction of algorithms can allow computers to learn from and make predictions on a data. It is intrinsic in innovation underpinning technologies in self-driving cars, data analysis, personal assistants, smarter prosthetic arms, to industrial robots.

University of Toronto’s Machine Learning group is head by Geoffrey Hinton, who has spent decades studying neural networks, a branch of computer science previously deemed so obscure and futile that many academia turned its back and funding was meager. In the mid 2000s, government-backed Canadian Institute for Advanced Research in Toronto was one of the few that funded research on neural network, it offered research grants on a relatively obscure branch in computer science. The technologies developed in Canadian labs can be found in facial recognition algorithms used by both Google’s Photos app and Facebook, smartphone voice recognition and even robots.

Geoffrey Hinton is now at Google Research, where he spends the bulk of his time. Another top professor Ruslan Salakhutdinov at the University of Toronto is leaving to work for the Machine Learning Department at Carnegie Mellon University, citing its over 100 PhD students powerhouse as the main draw. University of Alberta’s professor Kevin Murphy is at Google and University of British Columbia researcher Nando de Freitas moved to Oxford and now also works part-time at Google.

The reasoning for these researcher ultimately boils down to similar things – higher salary, access to more talents (CMU has an entire department of Machine Learning with much higher number of PhDs), better lab equipment, and monetizing capabilities.

The refreshed interests and high demands from big companies put Canada at risk of losing its AI edge, not only in cultivating research talents but also in building up an industry that has the potential to transform Canada’s economy. In 2012, Hinton and two of his U of T PhDs created a start-up based on an image recognition contest. They built a computer vision algorithm that learns to identify objects in millions of pictures with an error rate of the average human. Google acquired it for an undisclosed amount.

In an interview with Financial Post, Ajay Agrawal, a professor at the University of Toronto’s Rotman School of Management echoes the sentiment. “We are losing our top talent, the talent at every level,” he says. “While we had that advantage, it is slipping through our fingers.”

It not only spells out a problem with talent retention but it speaks to the bigger picture of Canadian economy. Canada has been pushed out of its competitive capacity by the ever-expanding economic capacity in the United States. Trade expansion and cooperation with the US have made a big domestic market in Canada near impossible. Even when it did materialize, such as the case of Blackberry and Nortel, it has been incredibly short-lived. Nortel has gone bankrupt and consumers have largely abandoned Blackberry as an option for smartphones.

An Ipsos Reid poll indicates that nearly three quarters of those interviewed believe that country is suffering from a brain drain. Fortunately, there is a sense of urgency at the University of Toronto to not squander a promising lead in AI technology. As we speak, the Centre for Engineering Innovation and Entrepreneurship is breaking ground as a much needed response to the sweeping changes taking place in engineering. The CEIE will feature an entrepreneurship hatchery, design workshop and house a number of cutting edge research facilities. This commitment reflects a landmark decision by the university to foster collaboration and accelerate innovation.

The Canadian government must incent change through innovation. It must take a new approach by investing in its human capital and set up competitive rewards and opportunities for the retention of talents. The Canadian government must recognize that start-ups are pivotal to fostering a future economy that’s not dependent on natural resources. Investing in its human capital and developing an edge that is unique to our own market are what will keep the Canadian economy sustainable in the long run.

Jennifer Yi is a 2017 Master of Public Policy student candidate at the School of Public Policy and Governance. She earned a Bachelor degree in Political Science from the University of Toronto. Jennifer has worked in advertising, academic sectors and non-profit companies. Her policy interests are in technology, research and development, municipal finances and consumer services.