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Alan Bernstein is president and CEO of CIFAR, the Canadian Institute for Advanced Research

Not a month goes by without another report about Canada’s lagging track record in innovation. The most recent report from the Council of Canadian Academies was typical, detailing how business-led research and development is low and declining. Our dismal track record matters. Canada’s economic well being and the fiscal room to maintain the social programs that we value, look after an aging population, and add new programs such as a national pharmacare program, will be very challenging unless we increase our productivity. That will require private sector investment in innovation, risk taking and entrepreneurship.

At the same time, not a day goes by without another story about Canada’s success in artificial intelligence (AI), or a visit by a foreign delegation eager to understand the reasons behind our success. Canada is an acknowledged world leader in AI and we are attracting significant domestic and international investment. If we want to know how to increase business-led investments in research and innovation and improve our productivity and competitiveness, one place we could start is to understand the underlying reasons behind our AI success.

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What are those lessons? Canada’s global excellence in AI didn’t just happen. It started with the Canadian Institute for Advanced Research’s (CIFAR) support of AI research, going back to the 1980s. That support was key in attracting, retaining and training AI talent. Geoffrey Hinton, universally regarded as the godfather of AI, came to the University of Toronto from the United States because he knew about Canada and U of T through his CIFAR connections. Dr. Hinton became a magnet for exceptionally bright students such as Yoshua Bengio (University of Montreal), now the acknowledged leader of the vibrant AI community in Montreal.

But great minds, like seeds in the ground, need fertile soil. Great research-intensive universities are key. McGill, the Universities of Montreal, Toronto and Alberta have been vital to Canada’s AI success. Nascent AI communities are now also emerging at UBC and Waterloo. Together, these universities have produced an extraordinary pool of talent that has been key in encouraging the private sector to be unusually early adopters of AI.

Recognizing the importance of AI to the economy, the government asked CIFAR to develop a $125-million Pan-Canadian AI strategy to attract, retain and train AI research in the country, building on the three centres of AI excellence in Montreal, Toronto and Edmonton. In addition, with international partners, CIFAR will also be addressing the ethical, legal, economic, and labour force issues of AI.

Risk taking, great academic institutions, exceptional scientists and government support are all key. But that is not enough. Canada’s success in AI has also depended on the willingness of all the players in this ecosystem to align for common purpose. That alignment has created the excitement that has attracted more talent, brought large firms such as Google, Google Deep Mind, Facebook, Uber and Microsoft here, unlocked significant pools of capital from Real Ventures, Caisse de dépot et placement du Québec and others and encouraged some of Canada’s largest companies such as RBC, TD and Magna to invest. The Creative Destruction Lab (CDL) and its cloning in several cities across Canada, has catalyzed many successful AI startups. And successful scale-up companies such as Element AI have sent a signal to students, government and business that Canada is an attractive place to invest time, talent and money.

There are three important lessons from Canada’s success in AI: First, great innovation starts with great science, science that initially may have no clear application but rather is focused on trying to understand the natural, biological or social world around us. Too often, our science policies are geared toward short-term needs of industry rather than focusing on long-term, uncertain questions in fundamental research. The AI story shows that disruptive game-changing science is ultimately what is most of value to industry.

Second, the private and public sectors should play complementary not duplicative roles in the science and innovation value chain. That’s the great lesson that the United States has taught the world. The U.S. biotech and high-tech sectors, the backbone of their innovation economy, both emerged from decades-long public investments in the underlying fundamental research. From those prior investments have come new precision drugs, DNA diagnostics, computers, smartphones and the Web – all textbook examples of the enormous leverage that comes from publicly funded fundamental research.

What about the third lesson? For established companies to be an early adopter of new technologies such as AI requires talent. AI is not an off-the-shelf product. The presence of a vibrant scientific community means that the private sector has the young talent nearby that is critical to understand, adapt and use new science. And so the third lesson, which relates directly back to the first lesson, is that young talent always develops around great science, and is key to innovation. And having that talent up the street makes an enormous difference to a company deciding whether to invest in a new technology.

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If we want to succeed as an innovative country (and the AI story shows that we can), we need to study closely the lessons from our success, not just our failures: Focus our public investments in fundamental science, ensure that our science policies respect and build on the complementary roles of the public and private sector in science and innovation and ensure that we develop and retain a critical mass of scientific and entrepreneurial talent.

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