Canada, an Early A.I. Hub, Fights to Stay Relevant
In the late 1980s, Geoffrey Hinton was a few years into teaching at Carnegie Mellon University in Pittsburgh, Pa., when he became increasingly troubled about the state of the nation he had left his home country of England for a decade prior. Hinton took issue with Ronald Reagan’s foreign policy, particularly the mining of harbors in Nicaragua, and the fact that the A.I. research he was pursuing was largely funded by the U.S. Department of Defense. So when he was presented with an opportunity to head North, he jumped at the chance.
“My wife and I were very fed up with the U.S.,” Hinton told Observer, “and Canada seemed like a good place.” Enticed to Toronto by a strong social system and a generous offer to become a fellow at the Canadian Institute for Advanced Research (CIFAR), a global research organization, Hinton made his way to the country in 1987. He’s largely stayed put ever since, picking up a Nobel Prize for his contribution to A.I. research along the way.
Hinton wasn’t alone. Decades of sustained funding for curiosity-driven research has brought scores of pioneering A.I. researchers to Canada, where a series of breakthroughs laid the foundations for the A.I. products dominating today’s tech industry. Canada built upon this momentum in 2017 when it became the first country to implement a national A.I. strategy, one that congregated much of its innovative work in three A.I. hubs spread out across Toronto, Montreal and Edmonton.
Despite the country’s contributions towards the now-booming technology, many say Canada has failed to reap the rewards of its own innovations. It isn’t just ideas that have been exported to the U.S., but much of the nation’s talent. “It’s this historic Canadian challenge of being often the inventors and pioneers of new technology, but not necessarily seeing the commercial success here,” Cam Linke, head of the Alberta Machine Intelligence Institute (Amii), told Observer.
While attempts to establish competitive A.I. companies in Canada have been largely unsuccessful over the past few decades, a combination of enhanced government funding, bolstered research institutions and changing cultural attitudes is starting to make a gradual impact. The Toronto-based startup Cohere, for example, earlier this year raised $500 million—an unprecedented amount for a Canadian generative A.I. startup—from a mix of Canadian, American and international investors. While conceding that Canada’s A.I. “brain drain” is still an ongoing issue, Nick Frosst, a co-founder of Cohere, told Observer, “I feel the tide is turning.”
Attracting the best researchers in the game
Long before companies like OpenAI and Anthropic broke out into the scene, Canada was a beacon for those drawn to ambitious A.I. research. The country might have had less national funding than the U.S., but it was an oasis for those pursuing long-term and experimental projects. Due to its social system and funding for basic research, “there were three researchers who were very happy to live in Canada,” said Hinton. They were Rich Sutton, Yoshua Bengio and Hinton himself—the latter two of whom would go on to be donned “Godfathers of A.I.” after winning the 2018 Turing Prize alongside Yann LeCun, now Meta’s chief A.I. scientist.
After Hinton set up shop for himself at the University of Toronto in the late 1980s, Sutton, an American researcher known for his pivotal work in reinforcement learning, headed out west to the University of Alberta as he became disenchanted with U.S. politics. Deep learning pioneer Bengio, meanwhile, returned to his hometown of Montreal to work at the University of Montreal. The presence of the three talents, in combination with Canada’s more lenient immigration politics, drew in even more A.I. researchers, according to Amii’s Linke. “That created this cycle of great people wanting to work with those folks.”
While they may have been spread out across the country, Hinton, Sutton and Bengio were aligned in their passion for a particular field of A.I. research—one that for decades wasn’t even linked to the term A.I. “There was something called A.I. and there was something called neural nets, and they were in opposition,” according to Hinton, who described the two as “warring camps.” Traditional A.I. emphasized symbolic reasoning, while the neural net worldview was based on mirroring the human brain.
Despite being regarded as a “crazy theory” at the time, according to Hinton, the neural net field was backed by CIFAR. In 2004, for example, it began “Neural Computation and Adaptive Perception,” a program directed by Hinton that Bengio and LeCun also took part in. “It was a while before [neural nets] had practical applications, and so you needed to fund people to work on them without being able to produce any spectacular applied uses of them and so on,” said Hinton. “In the U.S., it was much harder to get that money.”
Researchers involved in the program met annually to present their ideas to each other, according to Ruslan Salakhutdinov, a professor at Carnegie Mellon University. In 2005, Salakhutdinov had strayed away from A.I.
Salakhutdinov, who was working in banking at the time, ran into his former teacher Hinton on the street. Hinton convinced Salakhutdinov to return to school and pursue a Ph.D. under him after showing him his latest work on deep learning models.
The excitement within Canada’s neural net community began to grow in the early 2010s as breakthroughs in neural networks, such as superior speech recognition abilities, started to emerge. Hinton, along with his students Krizhevsky and Sutskever, made headlines in 2012 by winning an object recognition competition with neural networks. This success led to the creation of a startup called DNNresearch, which was later acquired by Google for $44 million.
As the field of neural nets gained recognition, researchers like Hinton, Sutton, Bengio, and LeCun received lucrative offers from companies like Google, DeepMind, and Meta. Many young researchers from Canada also headed to the U.S. for better opportunities, attracted by high-paying job offers.
To combat the brain drain in the A.I. sector, the Canadian government launched the Pan-Canadian A.I. Strategy in 2017, investing billions of dollars into A.I. research. Three Canadian hubs for A.I. technology were established, with researchers like Bengio, Sutton, and Hinton leading the way.
Despite the advancements in A.I. research in Canada, the domestic tech industry was slow to adopt the new technology. Companies like BlackBerry and Element AI struggled to capitalize on the potential of neural nets due to conservatism and financial challenges. The University of Toronto also faced obstacles in supporting entrepreneurial efforts within its academic community. In Canada, students utilizing university resources to transform their research into startups often had to surrender larger portions of company ownership compared to their American counterparts at prestigious institutions like Stanford and Carnegie Mellon, according to Carnegie Mellon’s Salakhutdinov. The University of Toronto stated that such equity agreements are typically negotiated on a case-by-case basis, with the school’s share ranging from single digits to low double digits.
Moreover, Canada faces a significant challenge in terms of compute infrastructure when compared to the U.S., as noted by Hinton, who described it as the country’s “one big drawback” for aspiring young researchers. He highlighted a situation where a former student, Jimmy Ba, faced limitations in accessing the necessary graphics processing units (GPUs) required to train large language models while working at the Vector Institute. Ba eventually joined Elon Musk’s A.I. startup xAI. Hinton expressed his belief that Canada may struggle to achieve global leadership in the field of artificial intelligence due to resource constraints, despite its previous success in basic research.
While there has been a noticeable exodus of talent from Canada, some researchers have chosen to remain in the country. Foreign companies have also established outposts and research labs in Canada, facilitating opportunities for local graduates. Lacoste-Julien, who leads a Samsung lab at Mila, acknowledged the positive impact of international offices in retaining talent within Canada after graduation. He emphasized that while the brain drain issue may not be fully resolved, progress has been made.
The prevailing values embedded in Canadian culture, which initially attracted renowned figures like Hinton and Sutton to the country, may hinder its ability to compete with foreign rivals in the A.I. industry. In provinces such as Quebec, there is a distinct perspective that prioritizes quality of life and equality over the traditional notion of success tied to the size of a company. Despite these challenges, some emerging startups in Canada are challenging the status quo. For instance, Artificial Agency, founded by former Google DeepMind researchers, secured $16 million in funding this year for its innovative approach to enhancing gaming through generative A.I.
The Canadian startup scene is experiencing a renewed sense of momentum, particularly in cities like Toronto. In 2022, the Canadian A.I. sector attracted $8.6 billion in venture capital, positioning the country as a top destination for A.I. investment. Companies like Waabi, focused on autonomous vehicles, and Cohere, a rising star in the A.I. landscape, have garnered significant funding and attention. The growing support from firms like Radical Ventures, a prominent Toronto-based venture capital firm, underscores the increasing potential of Canada’s A.I. ecosystem.
Collaboration between businesses and research institutions in Canada, such as Vector and Amii, has further fueled the growth of startups in the country. The retention of talent within Canada, particularly in provinces like Ontario, reflects a shift in mindset among local companies that now prioritize building and expanding within the country. Artificial Agency’s experience highlights the evolving synergy between startups and academia, with a focus on attracting and retaining top graduate students.
As Canada’s A.I. ecosystem continues to evolve, there is a growing tradition of successful startups setting a precedent for future generations of researchers and entrepreneurs. The shift towards nurturing a thriving local A.I. industry offers hope for a sustainable and innovative future in Canada’s technology landscape. sentence in a different way:
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