2021 Primer: Canada’s AI Research Ecosystem

Authors

Aaron Brindle, Partner, Radical Ventures

Leah Morris, Associate, Radical Ventures

Daniel Mulet, Investor, Radical Ventures

Overview: Canada’s AI Flywheel

Predicting what comes next in the rapidly evolving field of artificial intelligence (AI) is simple: follow the talent. From quantum neural networks and AutoML, to AI for drug discovery and natural language processing (NLP), the path to future innovations and commercialization is charted by a small subset of the global technology talent pool, the elite AI researchers who call Canada home.^[1]

Canada’s AI pedigree is no accident. Long before their work would define a new era of innovation, the Canadian Institute for Advanced Research (CIFAR) and the Natural Sciences and Engineering Research Council of Canada (NSERC) were funding the work of AI pioneers Geoffrey Hinton, Yoshua Bengio, and Richard Sutton. And, as their breakthroughs made the leap from the lab into real-world applications, vibrant ecosystems of innovation coalesced around Canada’s AI luminaries. Focused government investment, paired with commitments from the private sector, anchored this community in Canada, establishing centres of excellence with the shared mission of attracting world-leading research talent. Today, the Vector Institute for Artificial Intelligence (Vector Institute), the Quebec Artificial Intelligence Institute (Mila) and the Alberta Machine Intelligence Institute (Amii) are major hubs in the hyper-competitive global AI talent landscape.

Like a flywheel, the initial government investment in Canadian AI talent attracted commitments from private industry to build corporate R&D facilities, and spurred direct investments from venture capital to commercialize research in AI-focused startups. The benefits of these investments compound over time, rapidly generating top-tier talent and yielding greater returns for less spend. Once set into motion, the flywheel effect creates sustained growth and momentum in AI-related advancements. Long-term talent retention requires high-paying private sector opportunities and a support network to commercialize research and nurture a new generation of entrepreneurs. Government policies are the lubricant within this flywheel, facilitating the speedy immigration of top international talent, unlocking valuable public data and incentivizing private sector investments in AI technologies and startups.

For investors and enterprises looking to accelerate Canada’s AI flywheel, it is essential to understand the three primary drivers of the ecosystem. This report explores the latest data and developments in talent, industry and policy with exclusive insights from the leadership teams at Canada’s AI research institutes.

Canadian AI Institutes Overview

Talent

Canada excels at producing and attracting machine learning and AI researchers, as well as data engineers and scientists.^[5] When talented individuals choose to build their career in Canada, they are choosing to be part of a vibrant ecosystem of startups, enterprises, research institutions and investors.

Attracting Talent
Canada’s top-tier AI talent includes 109 world leading researchers who oversee labs at universities across the country and, in many cases, are Canada CIFAR AI (CCAI) Chairs.^[6] They include 2018 ACM A.M. Turing Award winners Yoshua Bengio and Geoffrey Hinton, as well as influential machine learning researchers Richard Sutton, Jimmy Ba, Aaron Courville, and Joelle Pineau. Over the past two years, Canada’s AI research leaders collectively published over 4,000 research papers, and trained over 2,400 master’s and doctoral students.^[7]

Amii, Mila, and the Vector Institute benefit from a pipeline of talent emerging from Canadian universities that have machine learning programs ranked among the top  programs globally such as the University of Toronto, the University of Waterloo, Simon Fraser University, the University of British Columbia, the University of Alberta, the Université de Montréal and McGill University.^[8] Together, the institutes supervise 1,200 AI trainees. Where like attracts like, attracting the world’s best machine learning researchers is a catalyst to grow a talented and engaged community, which draws private sector investment (through traditional capital or data resources) and increases the likelihood of technological breakthroughs and commercialization opportunities.

Canadian organizations and wealthy individuals have also prioritized talent by creating attractive workspaces across Canada such as the Schwartz Reisman Innovation Centre – a 750,000 square foot complex designed to anchor the University of Toronto’s unique cluster of world-leading AI scientists and biomedical experts. This was made possible through a $100 million gift to the University of Toronto from Gerald Schwartz and Heather Reisman.^[9] The Temerty Centre for AI Research and Education in Medicine has similarly been made possible through a $250 million gift from the Temerty family to advance machine learning tools for clinical applications and to further opportunities for entrepreneurship. These centralized locations bring research efficiencies, offering important access to compute power and infrastructure to enable world-class discoveries.

Researchers suggest the key to attracting and retaining top-tier talent is the availability of long-term, reliable funding and opportunities to solve interesting problems. Federal funding groups such as NSERC provided more than $17 million in funding to graduate-level AI projects.^[10] Industry partners also continue to invest. These efforts have resulted in 2,054 Canadian-based AI papers being published in 2019.^[11] As Geoffrey Hinton, whose research has been funded by government and industry, puts it: “In the long run, curiosity-driven research just works better. Real breakthroughs come from people focusing on what they’re excited about.”^[12]

Retaining Talent: Canada as a Talent Platform

The Canadian AI talent retention rate has been lower than other leading nations such as the United States, the United Kingdom, and France.^[13] Uneven AI adoption across sectors has been a push factor for talent as jobs are not consistently available in Canada.

As more industries apply AI solutions, however, career opportunities will expand across a variety of new application fields. An estimated 3,683 AI jobs were created and 16,205 AI jobs were retained in Ontario over 2020.^[14] In a survey of 151 organizations operating in Ontario, 97% said they had created AI jobs.^[15] The Canadian Occupational Projection System (COPS) estimates that demand for technology jobs will grow faster than other occupations with an estimated 100,000 relevant technology jobs being created by 2028.^[16] This demand may create a labour shortage in AI-related jobs. To date, AI employment in Canada has been heavily weighted in financial services, healthcare, and general technology (see table below).

Sectors of employment for 2019-20, based on alumni of Vector-recognized AI master’s programs

Source: Vector Institute, 2020.

Canada has positioned itself as a generator of the limited global AI talent, renowned for developing and attracting a higher concentration of AI PhDs than most other nations (see Fig. 1, Appendix).^[17] In the United States and Canada, the number of international doctoral students graduating in AI continues to grow, and currently exceeds 60% of the PhDs produced from these programs, up from less than 40% in 2010.^[18] Canada continues to lead in the number of AI professionals who conduct research in the private sector.^[19] This is, in part, due to a growing demand for talent that spans the need for both fundamental AI research and business applications.^[20]

Employee education is an important consideration for industry looking to adopt and develop AI technologies. Canada still lags behind its peer nations when it comes to training industry employees to use AI in their jobs. Canada also has fewer developers and IT staff to create and deploy new AI solutions.^[21] Organizations solely focusing on acquiring and educating a new generation of AI experts may overlook the importance of developing and retaining existing workforces.

Creating job opportunities that retain top-tier talent requires a private sector that understands the implicit long-term value of AI and commits resources to building out AI functions within their business. In Ontario, nearly half (44%) of the business executives in a survey of 151 organizations said their organization considers AI to have a strategically important role to play in achieving its business objectives.^[22] Comparatively, 23% of companies globally in a Deloitte survey noted that AI was strategically important to their business’ success.^[23]

Increasingly, the commercialization of Canadian research is generating demand for technology jobs. Canada’s AI job market has grown over time, now ranking 4th across 50 countries on the LinkedIn AI Hiring Index which measures the increase in AI job advertisements on LinkedIn globally.^[24] Between 2015 and 2019, Canada (alongside Singapore, Brazil, Australia, and India) experienced the fastest growth in AI hiring.^[25] Canadian AI research institutes, with the support of CIFAR, have developed a community of excellence that retains, attracts, and generates world-class AI researchers in Canada (Case Study 1, Appendix).

The health of Canada’s AI ecosystem can be measured, in part, by the number of well-paying AI jobs for highly qualified AI professionals who have graduated from AI-related programs. In contrast to Canada, talent in the United States remains relatively stable, with less outflow as a proportion of the country’s overall talent pool. This is attributed to many companies being headquartered in the United States where researchers are invited to work.^[26] The trending pull for talent to relocate to the United States may be changing as more countries are able to retain their talent. A reduction of inflow for the United States may be accelerated by stay-at-home policies related to the pandemic that allow researchers to work remotely, as well as challenges in reversing a recent history of stricter immigration rules that increased the difficulty for students to stay in the country after graduation.^[27]

While Canada has more outflow of talent on average when compared with other AI-focused nations, Canada also has more inflow than its peer AI nations. Few countries have both higher than average inflow and outflow of talent. Aside from Canada, these countries (sometimes described as “platform countries”) include the Netherlands, Singapore, Switzerland, and the United Kingdom.^[31] Canada is a central node in the global AI research network. 79.3% of Canadian AI articles from 2015 to 2019 were the result of an international collaboration (by comparison, over the same period 50.6% of articles from researchers in the United States were international collaborations). Multinational AI research labs based in Canada, play an important role in attracting and retaining a significant percentage of AI talent (see Fig. 2, Appendix). However, Canada’s research institutes remain an essential anchor point for top tier talent.

Industry

Private sector participation in the Canadian AI ecosystem has seen robust growth over the past five years. Corporate research labs and traditional enterprises are increasing their use of – and focus on – machine learning technologies. More than 45 companies have invested in AI research labs in Canada, including the Royal Bank of Canada, TD Bank, Microsoft, Nvidia and Alphabet (see Fig. 2, Appendix). These R&D labs are concentrated in Toronto, Montreal, Edmonton and Vancouver.

Essential to these industry investments is a collaboration with research scientists that brings material value to the private sector (see Case Study 2, Appendix). “The exchange works both ways, with companies learning about the latest AI research developments, and researchers having the opportunity to consider real-world data and applications of AI tools,” states Garth Gibson, Vector’s President and CEO. “We’ve found that close two-way interaction, sharing ideas and experiences, between researchers and industry practitioners has generated some of the best results for our industry sponsors.”

In Alberta, Amii has refined a framework called the AI Adoption Spectrum to guide their consulting projects with industry.

Amii AI Adoption Spectrum

Source: Amii, 2020

In Ontario, the Vector Institute has over forty sponsors who gain access to their Industry Innovation programs designed to accelerate the application of advanced AI in their organizations. These programs bring together Vector’s world-renowned researchers, advanced compute environment, diverse talent community and AI engineering capabilities to support organizations working to transform AI into business value.

Mila created a team of applied research scientists with the aim of helping industry bridge the gap between research advances and applications. Mila has undertaken various collaborations between Mila’s ecosystem of researchers  and industry partners, including the Gates Foundation for AI and drug repurposing, and IBM Canada for the Orion-IBM project to accelerate AI and machine learning adoption using open-source technology.

Canadian research institutes also offer opportunities to engage with the ecosystem through consortium projects such as Mila’s involvement in Le Consortium Québécois de Soins-Intelligents (the Quebec Intelligence Care Consortium), a collaboration with institutional partners, SMEs, and large corporations such as Hoffman La-Roche to enable Quebec patients access to their healthcare data, as well as the Consortium Project on Natural Language Processing at the Vector Institute to support the commercialization of NLP projects (see Case Study 3, Appendix).

Venture Capital and the AI Startup Ecosystem

A hallmark of a thriving technology ecosystem, such as Silicon Valley, is a critical mass of entrepreneurs who have successfully built businesses and re-invest their experience and capital. As companies grow to become successful, they eventually experience a liquidity event allowing the founders to capture some of the value that they have created. Often, this capital is re-invested in a new generation of companies by way of angel and venture investments. These investments are much sought after sources of smart capital, offering judgment and knowledge alongside capital for building a business. This cycle repeats itself over and over again, with the critical mass of participants growing larger over time.

Toronto, Montreal, and Vancouver have seen robust growth in startup activity over the past 10 years. Overall, private investment in Canada has grown 307% from 2015 to 2019, a faster rate than the United States at 194%.^[32] Hubs of entrepreneurial AI activity have emerged around organizations that cultivate entrepreneurship at the earliest stages of venture formation, such as NEXT AI, and the Creative Destruction Lab’s AI streams in Montreal and Toronto. These programs have an outsized impact in seeding and accelerating venture activity in the ecosystem.

Canadian startups, scaleups, and the three national AI institutes primarily collaborate in three ways:

• Spinouts: The rarest and most valuable are the entrepreneurially minded researchers who spin-out companies based on their academic work. These top-tier scientists often attract global investment to their businesses, which creates the opportunity to grow a business in its local environment. For example, Alán Aspuru-Guzik, an advisor to Radical Ventures, CCAI Chair and Vector Faculty Member, has co-founded two companies that have raised a combined total of US$80 million.
• Advisory support: Startups often turn to AI researchers for technical advisory roles which help them stay at the frontier of applied AI research. Many faculty at the research institutes are formal advisors to companies. Some scale their advisory role to commercialization programs such as Next AI and the Creative Destruction Lab. The Institutes are supportive of these non-core activities because it helps retain faculty who could earn more by engaging in private sector consulting or leading private research labs.
• Employment: Local startups play an important role as employers of graduating students, offering career opportunities in Canada. In turn, the startups that engage actively with the AI community benefit from access to talent, top-tier research and global networks of expertise. Anecdotally, all of these play important roles in a startup’s ability to build differentiated technology and attract private sector investment.

Investment Activity

From a private-market investment perspective, there has been a decrease in the volume of investment into pure-play core AI technologies over the past five years. At the same time, the number of AI-powered companies has grown significantly, and the volume of capital raised has increased. This trend may be attributed, in part, to the wider adoption of AI technologies across businesses, which supports our thesis that all businesses are becoming AI businesses.

Fig1. Distribution of disclosed venture capital funding rounds across
industries in Canada, January 2015 to November 2020

Source:  Radical Ventures analysis of Tracxn data, Jan. 2015 to Nov. 2020 funding activity for AI startups in Canada.

Just ten years ago, there was virtually no venture capital investment in Canada’s AI ecosystem. Today, the tremendous potential of the sector is attracting substantial investments as Canada is home to over 250 active AI startups with publicly disclosed venture financing that raised capital between 2015 and 2020.^[33] From 2015 to November 2020, there have been 246 seed rounds, 130 Series A rounds, and 12 rounds at late-stage financings. In 2020, Canadian AI startups raised a total of US$1.16 billion in venture capital, up 50% from 2018.^[34] Despite the increase in total funds raised, the number of investment events dropped slightly, signalling a higher amount raised per funding round.^[35] Looking at the makeup of investment each year, funding across each stage is up through 2018 with a large increase in late stage capital in 2019 for some mega deals.

Fig 2. Venture Capital Raised by AI startups in Canada, January 2015 to November 2020

Source:  Radical Ventures analysis of Tracxn data, 2015 to Nov. 2020 funding activity for AI startups in Canada.

Despite a relatively active investment landscape, an increase in venture capital at the earliest stages of company formation will drive entrepreneurial activity tied to Canada’s AI research institutes.

Given its talent bench strength, the Canadian AI ecosystem may be undervalued. Investment in Canadian AI is an order of magnitude smaller than the disclosed investment value in 2019 for the United States (US$25 billion) and China (US$5.4 billion), with total estimated values closer to US$47 billion and US$7 billion respectively.^[36] It is worth noting that Chinese investors are estimated to have participated in less than 2% of investments into American AI companies.^[37] Globally, private-market AI investment has grown tremendously in the past five years.^[38]

Policy

Immigration

Ultimately, talented individuals and subsequent investments are more likely to be drawn to countries where governments are actively reducing the barriers to AI adoption. The Canadian policy landscape has enabled businesses to quickly deploy highly skilled AI talent. As part of the Global Skills Strategy (GSS), over 1,100 Canadian employers have used the Global Talent Stream fast-track program since its inception in 2017. This has attracted 24,000 workers to Canada. Policies that have supported Canada as a top-choice for talent include post-study work permits, eligibility for a worker’s family members to find employment, as well as paths to permanent working eligibility and citizenship.

Procurement

The public sector is building AI procurement practices to stimulate a competitive AI marketplace. Canada’s preliminary public procurement guidelines assess the risks for using AI while supporting the potential public benefit.

A macro-level commitment to develop responsible AI has spurred continued investment. The Canadian government has invested $750 million in AI since 2016.^[39] According to CIFAR, that number expands to one billion dollars when including initiatives where AI is not the focus but is embedded – such as the Digital Supercluster in British Columbia and the Next Generation Manufacturing cluster in Ontario.^[40]

Data

AI models learn by viewing millions of real life examples to make predictions based on variations in information. Generally, the quality and quantity of the data will impact the accuracy of the model. Once the model is trained, more data is needed to test the model’s accuracy when making predictions based on information it has never viewed before. In some cases, such as medical data, there are strict laws in place to ensure privacy is maintained such as the GDPR, HIPAA, and PHIPA. Companies with significant resources are able to form partnerships, generate their own data, and use strategies that are often not available to startups.

Even when large data quantities are available, data quality still remains an issue if algorithms are to function as intended. Data that is noisy, dirty, or incomplete will slow down the efforts of the most competent AI developers. Quality data requires meticulous data governance to ensure  developers have adequate access to data for statistically accurate analysis while protecting the privacy of the individual. Across Canada, efforts have been made to tackle this issue through dataset creation efforts.

These efforts have been particularly salient in the healthcare space. Amii notes that Canada’s single payer health system may offer a data governance opportunity to leverage health data in a manner that respects the privacy of patients, preserves the security of the data and algorithms, while also advancing our ability to detect, trace, treat and cure. According to Cam Linke, CEO at Amii, “this is a key conversation that would help unlock significant potential for AI”. Amii is actively collaborating with organizations to collect, curate, and make research datasets available.

Responsible AI

Canada has taken a global leadership role in the development of AI and technology regulations. Since 2019, Canada has reviewed existing regulatory requirements and is currently conducting a reform of the Personal Information Protection and Electronic Documents Act (PIPEDA). The implications of this reform have yet to be fully determined. The Government published Canada’s Digital Charter in 2019, which outlined 10 principles to build a foundation of trust for Canadians in the digital sphere.

Canada is currently involved in leading a global online consultation on AI ethics spearheaded by Mila and Algora Lab (UdeM) in partnership with UNESCO. This builds on the 2017 Montreal Declaration for a Responsible Development of Artificial Intelligence which articulated ten fundamental values to underpin ethics principles for AI.^[41] Canada is also co-leading the Global Partnership on AI (GPAI) with France. This is the first global initiative bringing together 14 countries and the European Union to guide the responsible use and development of AI.

Simultaneously, organizations such as the Schwartz Reisman Institute for Technology and Society, facilitate collaborative projects with a cross-section of researchers, industry stakeholders, government, and civil society to build AI and other advanced technology solutions that are high value and globally actionable, as well as safe, responsible, and inclusive. Another example is the International Observatory on the Social Impacts of AI and Digital Technologies (OBVIA) led by Lyse Langlois, Professor of Industrial Relations and Director of the Institut d’éthique appliquée de l’Université Laval. The Observatory will bring together nearly 20 universities and colleges, as well as nearly 90 research centres, including Mila to maximize the positive outcomes of AI and technology for communities, organizations, and individuals.

Pan-Canadian AI Strategy

Established in 2017, the Pan-Canadian AI Strategy is a $125 million national AI strategy led by the Canadian Institute for Advanced Research (CIFAR). The strategy aims to further establish Canada’s international leadership in AI. CIFAR plays a significant role in AI research in Canada and around the world, supporting the fundamental breakthroughs in the field through the Learning in Machines & Brains program. The Pan-Canadian AI Strategy provides funding to professors and research across institutes through CIFAR. For example, Irina Rish, a CCAI Chair and Mila professor was awarded a Canada Excellence Research Chair in the summer of 2020, along with a budget of $34 million over seven years.

The strategy’s objectives are as follows:

• To increase the number of outstanding AI researchers and skilled graduates in Canada.
• To establish interconnected nodes of scientific excellence in Canada’s three major centres for AI in Edmonton, Montréal and Toronto.
• To develop global thought leadership on the economic, ethical, policy and legal implications of advances in AI, and;
• To support a national research community on AI.

CIFAR’s leadership of the Pan-Canadian AI Strategy is funded by the Government of Canada, with support from Facebook and the RBC Foundation.

Headwinds

Despite Canada’s pioneering AI pedigree and growing talent base, strong competition is mounting globally. The 2020 updates to the Tortoise AI Index, ranking state capacity for AI, reveal worrying trends for Canada.^[42] This year, Canada held fourth place on the global stage, but shifted down ranks significantly in multiple categories across implementation, innovation and investment.

Most notably, Canada’s operating environment fell from fifth place to thirty-second. This may be based on low rankings for privacy legislation implementation, gender diversity, and a lack of public trust. Possible indications of public misgivings were demonstrated through the loss of the Sidewalk Labs waterfront development project in Toronto last year.^[43] Additionally, Canada slipped nine places in terms of development, a category requiring industry collaboration to support contributions to new models, techniques and products. The report also cites declining incentives to seek patents as a contributing factor to a slip in the rankings.

These issues will, in part, be addressed by industry accelerating AI adoption through commercial partnerships that provide access to data and the development of practical AI applications. AI adoption will be further aided through up-skilling the existing labour force to improve comfort levels with technology and to ensure workers are AI-ready. Such steps have the ancillary benefit of shoring up public awareness and understanding around the value of AI-based technologies.

How to Engage Canada’s AI Ecosystem

Endorse the Pan-Canadian AI Strategy

The United States has a significantly greater capacity to fund R&D efforts – spending almost US$1 billion in nondefense AI R&D in 2020. Similarly, China had an estimated nondefense AI R&D budget between US$1.7 and $5.7 billion for 2018. While the United States and China surpass Canadian funding on a per capita basis, Canada continues to compete on the basis of top quality talent producing impressive research results. In 2020, Canada moved from 7th to 6th place globally when ranked by total article share in the AI field from 2015 to 2019. Although funding is important, recent policy developments have made Canada a more attractive labour market for AI talent.  Endorsing the renewal of the federal Pan-Canadian AI Strategy is the quickest route to prioritizing investments in talent and building on Canada’s AI research legacy.

Advocate for Data Access

Access to data remains an issue for AI communities globally. While Canada is the top country in Open Data Barometer’s assessment of 30 countries on access to data, collaboration with industry will cement this lead through the provision of high quality data opportunities for AI research.^[44] For instance, while Canada has a large opportunity based on its single-payer healthcare system, competing priorities and unforeseen complexities in executing a centralized database has, to date, alluded attempts to fully actualize this resource for research.

In building out data strategies, consideration must be given to ensuring secure and private AI mechanisms that protect both algorithms and datasets. Further consideration should also be given to safe and effective methods for human-AI collaboration, as well as improving the overall safety, privacy and security of AI systems. Organizations can set up advisory panels drawn from research institutes including academia, government and industry to help determine a set of relevant principles for the company’s AI work.

Invest in Canadian AI

Investing in research institutes offers a unique opportunity to contribute to Canadian AI research while benefiting from AI advancements tailored to business outcomes. Each research institute offers various types of involvement dependent on the needs of the partner. Engagements range from industry knowledge exchange to collaborative projects. These projects contribute to commercializing AI in sectors through quality data access and engaging job opportunities. The opportunity to engage with top-tier talent has attracted some of the largest Fortune 500 companies to Canada to expand their existing operations or to establish new R&D hubs (see Fig. 2, Appendix).

Collaboration and partnership opportunities also occur through engaging with startups and scaleups directly. Investing in venture capital funds offers a frontrow seat to industry trends, an increased capacity to source partnership opportunities for the application of AI technologies to products and services, and most importantly, fuels the growth of local technology companies poised to deliver outsized returns and global impact.

Appendix

Collaboration Case Studies

Case Study 1:  Mila and Vector

Case Study 2:  Research-Private Sector Collaboration

Case Study 3:  Collaboration with Research Institutes through Consortium Projects

Figure 1:  Student Programming and Opportunities

Figure 2: Corporate Research Labs (Exemplary Selection)

End Notes

^[1] One in ten of the papers selected for Oral Presentations at NeurIPS 2019 were authored by researchers based in Canada. Oral Presentations represent the most prestigious class of papers at NeurIPS, with an acceptance rate of 0.5% in 2019.

^[2] While this primer focuses on the three established Canadian AI research institutes to date, Radical Ventures also recognizes British Columbia (BC) as a strong node in the Canadian AI Ecosystem. This includes CAIDA as the main AI research organization at the University of British Columbia (UBC) with over 100 professors and research associates. UBC contributed 17 percent of Canada’s AI publications in 2019. BC is the forefront region for computer vision innovation in Canada with Simon Fraser University ranking as the top university in computer vision in Canada on CSRankings (2010 to 2020) and 11th in US and Canada.

^[3] “Core Academic Members,” Mila, Accessed December 18, 2020, mila.quebec/en/mila/team/; “Faculty Members,” Vector Institute, vectorinstitute.ai/#people; “Researchers,” Amii, 2020, amii.ca/about/our-people/

^[4] “Industry Solutions,” Amii, Accessed December 18, 2020, amii.ca/industry-solutions/; “Industrial partnerships,” Mila, mila.quebec/en/mila/industrial-partnerships/; “‘Partners,” Vector Institute, vectorinstitute.ai/#partners.

^[5] “Global AI Index Methodology Report,” Tortoise Media, 2019; “Global AI Index Methodology Report,”Tortoise Media, 2020.

^[6] “Pan-Canadian AI Strategy Impact Assessment Report,” Accenture & CIFAR, October, 2020, p. 4; “Canada CIFAR AI Chairs Program Surges Past 100,” January 19, 2021, “https://cifar.ca/cifarnews.

^[7] “Pan-Canadian AI Strategy Impact Assessment Report,” Accenture & CIFAR, October, 2020; “Canada’s AI Ecosystem: Government Investment Propels Private Sector Growth,” University of Toronto, 2020.

^[8]  As per CSRankings, 2020; “Pan-Canadian AI Strategy Impact Assessment Report,” 2020, p. 5.

^[9] All values in Canadian Dollars unless otherwise stated.

^[10] “Artificial intelligence: 2019-2020”, accessed November 14, 2020, NSERC Award Database, nserc-crsng.gc.ca/. Note: this includes “expert systems”, “knowledge representation”, “learning and inference theories”, “logic programming”, “natural language and speech understanding”. This number increases to $22,277,813 when including Robotics (“computer vision”, “pattern analysis and machine intelligence”, and “intelligent machine systems”.

^[11] “Pan-Canadian AI Strategy Impact Assessment Report,” Accenture & CIFAR, October, 2020, p. 4.

^[12] Kate Allen, “How a Toronto Professor’s Research Revolutionized Artificial Intelligence,” Toronto Star, April 17, 2015, thestar.com/news/world/.

^[13] “2019 Canadian AI Ecosystem,” JF Gagne, 2019, jfgagne.ai/2019-canadian-ai-ecosystem/; “2020 Canadian AI Ecosystem,” JF Gagne, 2020, jfgagne.ai/2020-canadian-ai-ecosystem/.
Note: Measured by location of arXiv author affiliations.

^[14] “Ontario AI Snapshot: The state of the province’s AI ecosystem in 2019/20,” Produced by Deloitte on behalf of the Vector Institute, 2020.

^[15] “Ontario AI Snapshot: The state of the province’s AI ecosystem in 2019/20,” Produced by Deloitte on behalf of the Vector Institute, 2020.

^[16] Pan-Canadian AI Strategy Impact Assessment Report,” Accenture & CIFAR, October, 2020; Ministry of Employment and Social Development of Canada. (2019) Canadian Occupational Projection System (COPS). Note: Technology occupations include NOC 0213 Computer and information systems managers, NOC 2161 Mathematicians, statisticians and actuaries, NOC 2147 Computer engineers (except software engineers and designers), NOC 2171 Information systems analysts and consultants, NOC 2172 Database analysts and data administrators, NOC 2173 Software engineers and designers, NOC 2174 Computer programmers and interactive media developers.

^[17] “Global AI Talent Report,” JF Gagne, 2019, jfgagne.ai/talent-2019/.

^[18] “The AI Index 2019 Annual Report, ” AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, December, 2019, hai.stanford.edu/.

^[19] Global AI Talent Report 2020,” JF Gagne, 2020, fgagne.ai/talent-2020/.

^[20] Ibid.

^[21] “Future in the balance? How countries are pursuing an AI advantage,” Deloitte Center for Technology, Media & Communications, 2019, deloitte.com/content/. Note: countries include Canada, Australia, China, France, Germany, United Kingdom, United States

^[22] “Ontario AI Snapshot: The state of the province’s AI ecosystem in 2019/20,” Produced by Deloitte on behalf of the Vector Institute, 2020.

^[23]  Ibid.

^[24] “Pan-Canadian AI Strategy Impact Assessment Report,” Accenture & CIFAR, October, 2020.; Data Analyzed from LinkedIn, 2020.

^[25] “Global AI Index Report,” Tortoise Media, 2019, tortoisemedia.com/intelligence/global-ai/.

^[26] “Global AI Talent Report 2019,” JF Gagne, 2019, jfgagne.ai/talent-2019/; “Global AI Talent Report 2020,” JF Gagne, 2020, fgagne.ai/talent-2020/.

^[27] “Global AI Talent Report 2020,” JF Gagne, 2020, fgagne.ai/talent-2020/. Note: Canada increased its score on invites to talent by 2.285 points from 2017 to 2019, demonstrating an ability to win back talent from the United States.

^[28] “Magnetic North: How Canada Holds Its Own in the Global Race for Innovation Talent,”  Innovation Economy  Council, December,

2020, innovationeconomycouncil.com.

^[29] Ibid.

^[30] Ibid.

^[31]  “Global AI Talent Report,” JF Gagne, 2019, jfgagne.ai/talent-2019/.

^[32] “What investment trends reveal about the global AI landscape,” The Brookings Institution, September 29, 2020, brookings.edu/techstream/.

^[33] Tracxn data as of 11/1/2020 of publicly disclosed funding rounds. Excludes public companies, those backed by private equity funds, and companies with undisclosed funding rounds.

^[34] When comparing our analysis to data from the Canadian Venture Capital Association (CVCA) we note that AI investments in the first three quarters of 2020 (~US$743 million) represented ~23% of the total VC investment in the country over the same period (~US$3.5 billion), and was up slightly from 19% in 2019.

^[35] There were 87 funding events in 2019, a drop from 98 in 2018.

^[36] “What investment trends reveal about the global AI landscape,” The Brookings Institution, September 29, 2020, brookings.edu/techstream/.

^[37] Ibid.

^[38] “The AI Index 2019 Annual Report, ” AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, December, 2019, hai.stanford.edu/; “What investment trends reveal about the global AI landscape,” The Brookings Institution, September 29, 2020, brookings.edu/techstream/.

^[39] “Pan-Canadian AI Strategy Impact Assessment Report,” Accenture & CIFAR, October, 2020.

^[40] Ibid.

^[41] “Montreal Declaration for a Responsible Development of Artificial Intelligence,” Research Impact Canada & Université de Montréal, 2017, recherche.umontreal.ca/english.

^[42] “Global AI Index Methodology Report,” Tortoise Media, 2019; “Global AI Index Methodology Report,” Tortoise Media, 2020, tortoisemedia.com/intelligence/global-ai/.

^[43] “Sidewalk Labs announces it’s no longer pursuing Toronto waterfront development,” Global News, May 8, 2020, globalnews.ca.

^[44]  “Pan-Canadian AI Strategy Impact Assessment Report,” Accenture & CIFAR, October, 2020; Open Data Barometer, 2020, opendatabarometer.org.

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