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A Look Inside Canada’s AI Commercialization Challenge

AI and IP | Jun 27, 2024

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Can Canada Turn Valuable Research and IP into Market Success?

Canada is internationally recognized for its leadership in artificial intelligence (AI) research, with some of the world's best AI talent and institutions. Canada's AI ecosystem is robust, thanks to leading institutions including Amii in Edmonton, Mila in Montreal, and Vector Institute in Toronto. These institutes are key to the Pan-Canadian Artificial Intelligence Strategy, supporting AI innovation and delivering comprehensive initiatives to help businesses embrace AI technologies.  Despite these advantages, the country fails to translate intellectual excellence into commercial success. This article analyzes a recent post on BNN Bloomberg and explores some key issues and their implications around the commercialization of AI in Canada, from various perspectives.

Barriers to Commercialization

1.  Intellectual Property (IP) Ownership and Foreign Influence

Jim Hinton, IP lawyer:

"About three-quarters of patents produced by researchers who work for Toronto's Vector Institute and Montreal's Mila leave the country, and most of these are in the hands of Big Tech" and "Foreign companies benefit from Canada’s public funding, Hinton argued, and there are 'no guardrails put on the ability for these foreign companies to basically pillage Canada's really good AI invention."​​

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The significant outflow of patents to foreign firms means that Canada loses control over the commercialization and potential economic benefits of its AI technologies. For example, if a pioneering AI algorithm developed at Mila is patented and acquired by a foreign tech behemoth such as Google or Uber, the economic gains, employment creation, and future development predominantly benefit the foreign entity. Canada requires rules that properly protect Canadian intellectual property and ensure the economic benefits of publicly financed research remain within the country.

2.  Public vs Private Sector Employment Dynamics

Elissa Strome, Executive Director of the Canadian Institute for Advanced Research:

"..a small number of our researchers' have part-time employment in the private sector. Those private-sector organizations own the rights to the IP that is generated by those researchers,' she said, but only when they're on the clock for those companies."​

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The dual employment of researchers at academic institutions and private sector enterprises complicates the issue of intellectual property rights. For example, a researcher who works for both the Vector Institute and a private tech firm may develop important intellectual property during their private sector hours, which is then owned by the private enterprise. While this approach can promote collaboration and real-world scientific applications, it also increases the possibility of key intellectual property leaving the public domain, diminishing Canada's direct commercial gains. Strengthening the "firewall" between public and private research outputs could assist to reduce these dangers.

3.  Value Beyond Patents

Elissa Strome, Executive Director of the Canadian Institute for Advanced Research:

"It's long-standing practice in Canadian research 'that there are relationships around contract research with industry,' and 'a really strong firewall' is in place between IP generated via public funds at the AI institutes and that which is generated through private funds. She also argued that patents are not a good measure of commercialization, and 'it’s the people that we're training in the AI ecosystem that actually hold the greatest value in AI, not patents."

This perspective highlights the importance of talent development over patent generation with human capital as the fundamental driver of commercialization. Training highly trained AI experts can be more valuable in the long term because they contribute to the larger ecosystem by pushing innovation and commercial applications.

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For example, a well-trained AI expert may start their own business or dramatically improve the capabilities of a Canadian corporation, boosting domestic commercial success and innovation. That said, and while we applaud the prioritization of human capital, any benefits may be muted if the related IP ends up being owned by a foreign organization.

4.  Ecosystem Gaps

Nicole Janssen, co-CEO of AI company AltaML

"We definitely do not have the ecosystem of companies that you would expect for the amount of talent that we have."​

Despite a large talent pool, Canada lacks the robust ecosystem required for effective commercialization of AI research. Only a few Canadian AI startups have achieved considerable scale when compared to those in the United States or Europe. Canadian AI startups, for example, may struggle to obtain the necessary local venture finance, business development experience, and market access to expand. Improving support systems, such as funding mechanisms, mentorship programs, and market access activities, can help close the gap and enable more successful commercialization of AI advances.

5.  Lagging Behind in Commercialization

See:  Canada’s Lagging AI Adoption Needs to Accelerate to Compete

Prime Minister Justin Trudeau, Prime Minister of Canada

"In spite of that, Canada hasn't always been 'great at commercializing,' Trudeau conceded."​

Prime Minister Justin Trudeau's statement reiterates Canada's long-standing commercialization challenge of turning research into practical commercial goods. This lag can be noticed in the time it takes for AI ideas to go from concept to commercialization. "AltaML takes an average of 18 months to begin developing an AI product in Canada, compared to four months in the United States," Nicole Janssen, co-CEO explains.  For example, Canadian AI firms may take longer to get finance, navigate regulatory approvals, and develop market presence than their American counterparts. This delay affects Canadian companies' ability to compete globally and reap the economic advantages of their technologies.

Leadership Opportunity in Responsible AI

Nicole Janssen, co-CEO of AI company AltaML:

"Canada could be a leader in responsible AI. That is a title that is up for grabs,' she said. 'And no one has grabbed it yet."

The emphasis on responsible AI provides a chance for Canada to stand out globally. By emphasizing ethical AI development and application, Canada may attract firms and researchers who appreciate these principles. For example, developing standards and frameworks for responsible AI use can help Canada become a global leader in this field, drawing foreign partnerships and investments. This focus would help boost commercialization in Canada while ensuring that AI breakthroughs benefit society, balancing economic success with ethical considerations.

Conclusion

By implementing strategic policies, improving support systems, and focusing on responsible AI, Canada may harvest its research assets for economic and societal benefits. Addressing these challenges will be crucial for Canada to maintain its leadership in AI research and translate it into commercial success.


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