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I visited Winnipeg last week and there’s a new energy in the air. The election of Wab Kinew as Manitoba premier—one of the first Indigenous persons to lead any province—has put a new spotlight on the province, its role in reconciliation and leadership in the race to Net Zero.

I met with Premier Kinew to discuss his climate policies, insights from the RBC Climate Action Institute, and whether Manitoba could be a new model for Canada’s transition. He kept returning to a single word: hydrogen. His NDP government wants to make Manitoba a green hydrogen hub, even though the province is running short of surplus industrial power. Kinew is also keen to advance electric vehicle adoption, especially for the buses, trucks and farm machines that account for a third of Manitoba’s emissions. He has a hometown advantage in New Flyer Industries, a global player in electric and hydrogen buses, but needs a growing economy to finance the transition.

More electricity generation and transmission will be an added challenge for Kinew’s promise of reconciliation. His province’s population is 20% Indigenous, the highest in Canada, and new projects will face growing tests of “free, prior and informed consent.” The same challenge will face the NDP’s promise of critical minerals production. (The province claims to hold 29 of 31 key minerals, including lithium.) Kinew said he is hoping to see “enthusiastic consent” exhibited through business partnerships.

Manitoba’s other great climate opportunity? Agriculture. I visited the University of Manitoba’s Glenlea Research Station, south of Winnipeg, to see Canada’s oldest soil sequestration test site, aimed at capturing greenhouse gases. The station is also developing technologies to capture gases from the province’s four million cows, hogs and pigs.

Manitoba is home to only 1.4 million people. It will need all the climate tech it can develop to harness their—and the province’s—energy.

The backdrop for the 27th United Nations Climate Conference was always going to be an odd one. Sharm El-Sheikh is a beach resort town built by the Israelis during their occupation of the Sinai Peninsula in the 1970s, and dedicated pretty much to the hedonistic pursuits of European and Arab charter groups. Picture a faux Roman amphitheater, a Hollywood theme park and 10-lane highways through the desert. And then picture 30,000 climate actors, advocates and activists crowding into the Tonino Lamborghini International Convention Centre to tackle, without a hint of irony, the future of our consumption-based society. From the get-go, COP27 had to be a kind of Truman Show of climate conferences—a conceit wrapped in a bubble, cloaked in a narrative at odds with reality outside. In the centre of that bubble, in a “blue zone” of temporary hangars that gave the feel of a military encampment, climate visitors tried their best to draw in the world and project their intentions back. The stakes were otherwise too high. But the odds of success were also daunting. During a year of economic disruption, this was a critical chance to reconcile the growing tensions between energy security, climate security and economic security. Here are some of my takeaways of what was achieved and what was not.

1. The “Implementation COP” needs more implementers

COP26 in Glasgow was all about ambition, with nations committing to deeper emissions cuts by 2030 to ensure the world meets its Paris agreements. Sharm El-Sheikh was meant to be about implementation plans, and how countries can do what they say. Five G7 leaders came: France’s Emmanuel Macron, Germany’s Olaf Scholz, Italy’s Giorgia Meloni, Britain’s Rishi Sunak and America’s Joe Biden. They each must have noticed a sign, “Act Now,” on their way into the main hall. The European Union upped its goals—remarkable given its energy crisis. So, too, did Indonesia. Canada noted the prevalence of climate action, from carbon capture projects in Alberta to green steel mills in Ontario and manure methane plants in Quebec. Biden, in his keynote speech to COP27, recommitted the U.S. to its promise of cutting emissions by 50% from 2005 levels by 2030, a key part of his presidency. Businesses, too, came with greater commitments; there’s been a ten-fold increase in companies with science-based climate targets since 2019. But those implementers are still a minority. Among 196 countries, only 29 came to Egypt with revised action plans.

2. 1.5 may not be alive for long

A signal achievement from Glasgow was the endorsement of the 1.5-degrees-Celsius imperative–that is, all climate action needs to contribute to containing global warming to that threshold, after which catastrophic results accelerate. “Keep 1.5 alive” was the Glasgow mantra, as it’s the threshold at which, according to the UN climate scientists, we can say goodbye to coral reefs such as the ones off the beach at Sharm. To contain temperature increases, the world needs to cut emissions by roughly 50% this decade. Instead, we saw emissions rise 1% last year (even more in the U.S.) and are on course for a 10% increase this decade. A draft Sharm declaration maintained the rhetorical commitment to 1.5, but in the corridors there was a striking number of questions about the authenticity of such commitments, and whether the world should begin focusing on a more realistic ambition, such as “well below 2.0 degrees.”

3. Coal’s not dead

Glasgow declared a death knell for coal. What a difference a year makes. Germany is using more coal. China and India, too. But it’s not inevitable. If COP27 can claim meaningful success, it might be through the curiously named JET Partnership, for a Just Energy Transition. The partnership of wealthy nations and financial institutions is designed to help developing countries wind down coal. JETP had its first partner in South Africa, and moved quickly at COP27 to sign on Indonesia, to help it reach peak power sector emissions by 2030 and get to Net Zero by 2050. Vietnam may be next. The costs are enormous, and raise concerns about burdening poorer countries with more debt–and likely seeing them shift from coal to natural gas, which still warms the planet. But the effort also misses the elephants in the room. China consumes 50% of the world’s thermal coal; India close to 20%. Neither is moving quickly away from it. In fact, India sidetracked the COP discussions with a provocative challenge of its own, to cancel coal when the rest of the world agrees to cancel oil and gas. There weren’t many takers. African nations were among the most vocal at COP27 for an enhanced role for gas, which they see as an essential energy source as they transition away from coal and wood.

4. The oil COP?

Sharm El-Sheikh proved to be a good summit for the oil industry. For proof, you only needed to look out your window on the drive in from the airport. A 10-lane road, financed by the Saudis and named for King Salman, took COP-goers past another striking display of Saudi swagger. The green-lit, twin-dome Saudi Innovation Park, built in a patch of desert next to the main conference centre, was an early indication of how the oil world, led by OPEC, has shifted to its front foot. UN Secretary General António Guterres kicked off COP with a provocative metaphor—“a highway to climate hell with our foot on the accelerator”—that captured the point, but his PR machine met its match on the test track. The Saudis, who share the Red Sea with Egypt, vowed to increase oil production and intend to produce oil past 2100. The United Arab Emirates, who will host COP28 in Dubai, described the region as “superheroes.” The Arabs argue they will develop carbon capture and storage (CCS) technologies that will bring their net emissions to zero. Indeed, the Saudis plan to open the world’s biggest CCS facility by 2027. Environmentalists have fought to marginalize so-called abatement technologies, to ensure they don’t facilitate more fossil fuel production. Expect that debate—reduction versus abatement—to define the Dubai COP.

5. A loss for developing countries; damage for the UN

If host Egypt had one ambition for COP27, it was to win global support for “loss and damages”—a popular term that essentially translates as compensation for countries hardest hit by climate change and least able to pay for it. Pakistan was a poster child for Egypt’s campaign; the diplomatically savvy South Asian country used pretty much its entire COP presence to advocate for a mechanism to compensate it for some of the estimated US$30 billion in damages it has suffered from this year’s floods caused by global warming and early snowmelt. Sadly, the Egyptians didn’t think through the levels of concern from the wealthy countries they hoped would pay. The U.S. has a deep allergy to anything in the UN that hints at reparations, not least because of legal fears (never underestimate the influence of government lawyers) over unlimited liabilities. A draft agreement recognized Egypt’s concern, but offered only pennies to the dollars that developing countries were pushing for.

6. America’s back. China’s not

A striking feature of COP27: America’s climate ambitions. Fresh from midterm elections that kept the Senate in Democrat hands, Joe Biden landed in Sharm El-Sheikh en route to the G20 summit in Bali, Indonesia, to promote his Inflation Reduction Act and the US$370 billion it will allocate to climate. His administration has a tech-forward approach, betting on five key technologies: batteries, heating and cooling systems, electricity grids, aviation fuel and de-carbonization of the chemical, steel and cement industries. It’s clear the U.S. is going to use more carrots than sticks to get to its 50% emissions cut and assert itself globally as a clean-tech superpower. A few years ago, China wanted that mantle. Today, it’s a diminished power as the Xi regime struggles with a hostile relationship with Washington, rapidly aging demographics and COVID lockdowns. China has not abandoned its green ambitions as it’s still one of the world’s leading developers of wind and solar power, and electric vehicles. But Beijing’s no longer the climate champion it was during the years of US President Donald Trump, nor is it a leader of nations. The rest of the world may be more dependent on the U.S. than ever. For better and worse.

7. #WTF: Where’s the finance?

There are not a lot of economists at a COP, which is too bad, because economics drive political action. No more so than when money’s getting tight. The sharp rise in interest rates this year is quietly becoming a drag on climate policies, especially in developing countries. Few appreciate this more than Mark Carney, the former central banker who helped launch the Glasgow Financial Alliance for Net Zero at COP26. Carney’s alliance now consists of 550 financial institutions in 50 countries, representing trillions of dollars in assets. It’s a grand coalition with a grand promise to mobilize capital for Net Zero—and it’s leading to a grand array of criticisms. Carney came under fire at COP27 for overpromising and under-delivering; for most developing countries, the capital hasn’t arrived. The concern even fuelled a COP-meme, #WTF, as in “where’s the finance?” One reason is a lack of sufficiently large projects. Egypt tried to bend that curve at this COP, announcing a massive renewables project. Carney believes the world needs US$1 trillion a year of projects like that to quadruple the ratio of renewable energy to non-renewable investments to 4:1. The capital is there. But a challenge lies in the U.S. Federal Reserve’s aggressive campaign against inflation, which has jacked up U.S. interest rates and attracted a lot of capital to, well, the U.S.

8. Agriculture, the new climate champion

Believe it or not, this was the first COP where agriculture took centre stage. Pretty surprising when you realize the food supply system accounts for roughly a quarter of global emissions. The UN, and many of its members, have shied away from tackling agriculture as they don’t want to alienate farmers, who are central to global development. But increasingly, agriculture is viewed as a climate solution—perhaps even a net positive to the world if farmers can turn their soil into profitable carbon sinks. With a newfound spirit of ag innovation, the conference devoted a day to agriculture, and the “blue zone” of pavilions had plenty on display from every continent. More sustainable fertilizer practices and lower emitting fertilizers will be key. So, too, will new technologies like anaerobic digesters that turn animal emissions into energy. China, which accounts for 20% of the world’s methane, needs to be a leader on that front. But the most contentious opportunity may be regenerative agriculture—a series of practices like cover cropping and no-till farming that ensure soils capture and sequester greenhouse gases. The U.S. is racing ahead with voluntary markets that will allow companies and investors to pay farmers for harnessing their soil in return for carbon credits. Other countries are more cautious, knowing soil science isn’t quite advanced enough to prove how much has been captured or stored.

9. Hello, atmosphere. The ocean’s calling. Rainforests, too

This was also the first COP where oceans got a serious look. That’s appropriate as Sharm El-Sheikh is not just a desert town; it sits next to some of the Red Sea’s finest coral reefs, which face extinction if more progress isn’t made. I sat in on a session with Prince Albert of Monaco, Sylvia Earle, the great oceans champion, and Johan Rockström, a pre-eminent climate scientist with the Potsdam Institute for Climate Research. Rockström explained what rising temperatures are doing to the world, and to oceans. The Arctic is already 2C degrees warmer, which is not only leading to ice shelves disappearing into the sea; it’s disrupting air currents and leading to heat waves like the one that engulfed Western Canada in 2021. Some 93% of that excess heat is absorbed by oceans in a massive energy transfer that’s changing life deep below the surface. Global ocean heat was at a record high in 2021. Cue the storm surges. This kind of interplay between oceans, land and air has been appreciated by scientists for centuries but lost a bit of its imperative in recent years. That’s changing, as biodiversity and climate are again seen as two sides of the same coin. It will be a central theme of the UN Biodiversity Conference in Montreal in December, and got a big endorsement in the final days of COP27 from the incoming Brazilian president, Luiz Inácio Lula da Silva. Lula, as he is universally known, got a bigger cheer than Biden, pledging to renew the fight to save the Amazon. It won’t be easy, not when an anxious world is looking for economic growth more than natural growth. But Lula’s message on biodiversity was clear: “There is no climate security for the world without a protected Amazon.”

10. Whose COP is it anyway?

This was the first COP in memory where Canada had a national pavilion. It wasn’t techie like India’s pavilion next door, or bold like America’s. But it did, in that Canadian way, stand out as inclusive. With a design that felt a bit like an upscale donut shop (there was even free coffee), the venue gave voice to more views and experiences than perhaps any other I saw. Activists, Indigenous leaders, corporate executives, mayors, entrepreneurs, investors—it was Canada in full. And in a way that’s an enduring challenge for COP. This one, in Egypt, adhered to the strict laws and security standards that barred any serious form of protest. Even Greta Thunberg, the young environmental activist, didn’t see the point in being there. Across a major road, a Green Zone was set up for community groups and activists, and was actually more interesting and enjoyable than the conference halls. But even there, one didn’t get the impression the world was on edge. COP benefits from a diversity of voices, which has grown over the years. Only the most arrogant or naïve delegates believe they have a clear answer to the world’s challenges, and only they would not welcome differing views. As the world adds Sharm El-Sheikh to a long and growing list of COP hosts, and turns its mind to Dubai, that curiosity will be needed more than ever. It may just be Canada’s best contribution to COP28, and beyond.
John Stackhouse is senior vice-president in the Office of the CEO at Royal Bank of Canada, leading the organization’s research and thought leadership on economic, technological and social change. Previously, he was editor-in-chief of the Globe and Mail and editor of Report on Business. He is a senior fellow at the C.D. Howe Institute and the Munk School of Global Affairs and Public Policy and sits on the boards of Queen’s University, the Aga Khan Foundation of Canada and the Literary Review of Canada.

This is turning into a one-issue election, in two very different parts.

Biden supporters feel COVID is the top issue, by a 42-23 margin over the economy. Trump supporters feel the economy is the top issue, by a 68-9 margin over COVID.

Which means Trump can’t afford for COVID to be the ballot question, especially since 48% of voters say they don’t trust him at all when it comes to the pandemic. In fact, the economy is the only issue in the top-10 list of voter concerns where he scores better than Biden.

These numbers are from the latest NBC-Wall Street Journal poll, conducted by Hart Research, and they show a consistent and strong lead for Biden.

But as Molly O’Rourke, a Hart Research partner, told a webinar hosted by RBC Capital Markets, there’s plenty of room for asterisks. The biggest risk is that “polling is weakest where it matters most” – among older, less educated voters in the swing states that Trump won narrowly in 2016.



On several counts, Trump faces challenges:

  • few voters (28%) feel the country is moving in the right direction, which tends to signal the mood for change;
  • interest in the election (81%) is at a new high;
  • voters are taking the election very seriously, with 83% saying the outcome matters (it’s usually about 55%);
  • his base is shifting, with his lead among white, non-college educated men falling from 38 points to 19 points in one month;
  • Biden’s lead with suburban women has grown from 9 points to 25 points in one month;
  • Biden’s lead with seniors has grown from 4 points to 27 points.

O’Rourke cautioned that polling has been disrupted by the pandemic, with serious limits on qualitative in-person interviews that give pollsters a truer sense of public sentiment.

But even in limited encounters, she’s been struck by the number of voters, including “ambivalent” Trump supporters, who use the word “exhausted” when they talk about the need for change. In a period of massive disruption, they see Trump as a source of disruption, and Biden as a source of calm.

She doesn’t see voters giving Biden a mandate to do much more than get rid of Trump and get rid of the virus. There’s much less interest in the bolder change agenda that many Democrats are hoping to implement if they secure control of both Congress and the White House.

Nearly 30 million votes have already been cast – five times as many as in 2016. And many of those ballots won’t be counted until after election day. That could be an advantage for Trump, whose supporters are more inclined to vote in person on November 3. O’Rourke suggested a “red mirage” on election night could prompt him to declare victory, even though Biden could be declared the winner days or weeks later.

The delayed results could also hang over the Senate, which is proving to be much more challenging for the Republicans. The Democrats need to flip only three of the 35 GOP seats up for grabs to secure control of the upper house, which O’Rourke said is “a very strong possibility.”

With so much up in the air, she expects the election to be “a major test of faith that people have in our institutions, and a test of our two-party system playing by the rules. I’m honestly nervous about it, and there’s plenty of opportunities to get derailed.”

O’Rourke urged one word for election night: “patience.”

For more, you can see Molly O’Rourke’s latest polling slides here.

 
Until now. The lower mainland is catching up with Montreal, Toronto and Edmonton as its universities and entrepreneurs bring some west coast savvy to the artificial intelligence race. And in British Columbia, that means computer vision, Cascadia and the confidence of Hollywood North. Vancouver is home to 130 AI startups, as well as four major investor groups, four incubators and four public research labs focused on AI. The hometown companies include Vision Critical, an OMERS-funded venture focused on consumer patterns; Finn.ai, which is applying AI to day-to-day banking; and Generation R Consulting, which is based at the University of British Columbia and pioneered an AI ethics assessment tool for Technical Safety BC, an organization that oversees the safe installation of technical systems and equipment across the province. The word is out, and investors are in. In 2017, Vancouver’s own Kindred closed a $35.4 million Series B funding round led by Chinese investor Tencent. In the past month alone, Mitsubishi invested $5.8m in Spare, an AI-enabled platform for on-demand mobility and smart transportation networks and Fujitsu opened its global AI headquarters, Fujitsu Intelligence Technology, in Vancouver. RBC added to the ecosystem this week with the official opening of a Borealis AI lab where 20 researchers are hard at work. It’s run by Greg Mori, a computer vision specialist and former head of computing science at Simon Fraser University. Mori said the research at such universities on ethical issues like AI bias and “explainability” is working its way into the commercial development of AI in Canada. Borealis is thought to be the largest AI initiative by a Canadian company, with other labs in Montreal, Toronto, Waterloo and Edmonton. Mori noted that Vancouver is rare among cities to have two world-class computing science schools, at SFU and UBC. Moreover, “there’s an interplay here between universities and industries that’s really important,” he said at the opening of the Borealis lab in Yaletown. With more than 300 graduate and 2,000 undergraduate students, SFU is ranked among the world’s top 50 computing science schools, and among Canada’s top 5 based on citations per faculty member. UBC’s main AI research organization, Centre for AI Decision-making and Action, involves more than 50 researchers across five faculties. Its Computer Vision and Robotics research group created Scale-invariant feature transform (SIFT), a widely-used feature detection algorithm in computer vision. There’s even a new player in town: Northeastern University is adding a Vancouver location to its expanding global campus network, planning graduate programs and work integrated learning opportunities that support B.C.’s rapidly growing expertise in AI and data visualization. Mori has been developing the Vancouver lab since last September, with a particular focus on computer vision, his academic specialty. It’s a subfield of machine learning that trains computers to see, process, and understand images such as videos and photographs, to identify and distinguish objects – a cat versus a dog, for instance – and even track motion to predict outcomes, such as the likelihood of a player taking a shot in basketball. The computer vision movement is building on Vancouver’s natural ties with the Seattle area – Microsoft, Amazon, Boeing – and the larger Cascadia region that reaches down to Silicon Valley. It’s also deeply rooted in the lower mainland’s history in video games, moviemaking and post-production, which earned it the nickname of “Hollywood North.” Vancouver is the world’s largest economic cluster for visual effects and animation, with more than 60 studios, including Sony Picture Imageworks, Industry Light & Magic (ILM), DHX Media and Image Engine, which was behind Drogon’s iconic attack on the loot train in season seven of Game of Thrones. The city is also thriving with video game clusters built around Electronic Arts, Microsoft and Nintendo Education. As visual media transitions to virtual and augmented realities – and brings with it new orders of data that can feed AI applications – the digital power to transform entire industries may be profound. It’s part of the logic of the federal government’s $153-million commitment to a Digital Technology Supercluster that is projected to generate more than $5 billion in economic output and 13,500 new jobs over the next decade. Canada could use that kind of integration between academic research and commercial application in AI:
  • While Canada was third in the world (after the US and China) for AI scholars with academic publications, we were ninth for patents filed
  • We ranked last out of 10 countries for AI deployment, with just 31% of adopters claiming success compared to 51% globally
  • Only 17% of Canadian businesses reported using AI technologies over the last year – a number that has barely changed since 2014, when it was 16%, according to the 2018 Deloitte survey of 1,000 Canadian citizens and 2,500 businesses found
  • 67% of those firms spent less than $5 million on AI in the 2017-2018 fiscal year
  • Only 8% of companies planned to increase AI spending by more than 20% in upcoming year

Now, can we lead on tackling its ethical and societal implications?

The news is flooded with examples of AI fails: algorithms that favour male job applicants over women, or image recognition software failing to correctly identify people of colour.

Dr. Foteini Agrafioti, the Head of Borealis AI and one of the country’s strongest voices on ensuring AI is ethical, was announced last week as the co-chair of Canada’s new Advisory Council on AI. She led the latest RBC Disruptors conversation about battling bias in AI with Dr. Elissa Strome, Executive Director, Pan-Canadian AI Strategy at CIFAR, and Dr. Layla El Asri, Research Manager, Microsoft Research Montréal.

Here are their thoughts on what the scientific community, governments and ordinary citizens can do to confront bias in artificial intelligence, and position Canada as a leader in ethical AI.

1. Use Technology to Expose Bias

Bias has long existed in our society – and so it exists in our data. El Asri sees this as an opportunity. Unlike our own unconscious bias, we can at least uncover bias in an algorithm. To do this, companies need to be auditing their AI for bias every step of the way, as the major labs are now doing. El Asri credited Canadian leaders, such as AI pioneer Yoshua Bengio, for developing a will in Canada’s tech community to develop AI in a responsible way.

2. Diversify the Industry

Right now, artificial intelligence is being developed by a very narrow subset of society: mainly highly-educated men who went to the same schools, and now live in the same cities. Only 18% of AI researchers are women, a fact that Strome called “terrible.” Organizations like CIFAR are working to bring more voices into the development of AI, with initiatives such as the AI for Good Summer Lab, a seven-week training program for undergraduate women in AI.

3. Diversify the Data

AI is only as good as the data it’s trained on. “If your data is not representative enough, your model is not going to work,” El Asri said. There needs to be more vigilance in ensuring data is representative — an area where Canada has a homegrown advantage. If you’re working with data collected in a multicultural country like ours, you’re likely working with data that represents different ethnic backgrounds. This kind of data will be essential to building technology that works for everyone, especially when it comes to something like health care.

4. Talk to the Social Scientists

Right now, it’s really just the tech community and policy-makers talking about issues that are going to transform our society. We need to broaden that perspective, building in consultation with social scientists as an integral part of the development process. A recent CIFAR initiative brought together computer scientists and social scientists for a day to discuss the social, legal and ethical implications of AI. “The computer scientists were so eager to get their advice and insights,” Strome said. Similarly, at Microsoft, El Asri noted that their AI and ethics committees are made up of people from different disciplines, including anthropologists and historians.

5. Educate the Public

“There’s a lot of fear and misunderstanding and myths about AI,” Strome said. Over the next few years, it’s going to be critical to bring the public into the AI conversation. People need to be aware of the positive implications, as well as the risks, that AI will have on their lives. The better the next generation understands AI and its societal and ethical implications, the better prepared they’ll be to ask tough questions of their leaders. Agrafioti suggested that Canadian culture is particularly attuned to ensuring fairness, casting a critical eye on technology before implementing it. Our balance of technical expertise and social values is exactly what’s needed to make sure the product that gets to market is ethical.

6. Strong Governance

AI has been advancing much faster than any government can regulate it — so it was big news this week when the OECD adopted a set of AI principles, which set values-based standards for developing AI. Our leaders have an incredibly important role to play in developing policy and regulations around the use of AI, both domestically and internationally. Strome noted that Canada’s solid international reputation could go a long way in urging the world to play catch-up. Last summer, Prime Minister Trudeau and President Macron announced a joint Canada-France initiative on an International Panel on AI to support and guide the responsible adoption of AI, grounded in human rights. The first symposium will be in Paris this fall.

Solving bias in machines will take a human touch — and there’s no country better positioned than Canada to take the reins.

Listen to our conversation on the RBC Disruptors podcast about the potential of artificial intelligence.

Listen on Apple Podcasts, Spotify or Simplecast

The New Artificial Intelligence

Artificial Intelligence has been around for decades, but is the hottest area of technology today because of vast improvements in computing power and data availability. For business, AI offers the potential to greatly improve operational efficiencies, predict consumer preferences and reduce human error, through self-teaching algorithms that can transform business models with unprecedented speed and certainty. The promise of AI has led to a global surge in investment and a war for scarce talent that threatens to tilt the playing field in favour of a few nations and companies that have the money, datasets and computing power to win at scale. The Analysis Group estimates the global economic impact of AI over the next decade could be worth as much as US$3 trillion. For Canada, once a leader in the field, the surge in AI has presented a national challenge. The Canadian government recently committed $125 million to a national AI initiative; Quebec added $100 million and Ontario committed another $50 million, largely to retain academic talent in the face of aggressive hiring by Google and Microsoft, among others. Despite these investments, only a fraction of Canadian companies have announced AI programs and the Canadian AI start-up space remains nascent. If Canada is going to remain competitive in the age of artificial intelligence, a more collective ambition will be needed.

AI, Defined

The most common approach to artificial intelligence is known as machine learning, a general term used for teaching computers to do things for which they are not explicitly programmed. One way to understand machine learning is as a very advanced form of pattern recognition, and AI researchers seek to teach computers to make inferences and predictions from those patterns. One important subset of machine learning is called deep learning, where researchers build complex algorithms designed to mimic the reasoning process of the human brain. These so-called neural networks connect a huge number of small, simple processing units into a much larger whole. Take the common example of a cat picture. While no individual artificial neuron can understand what a cat looks like, a neural network can assemble the pieces and see the bigger picture. Another growing branch of AI is reinforcement learning, an advanced form of trial-and-error reasoning that would be familiar to anyone who’s ever played the board game Battleship. These AI algorithms learn behaviour based on feedback from the environment, thanks to designers who build in rewards for proper actions. Think Pavlov’s dog—with a digital bell.

An Academic Head Start

Canada had an early lead thanks to three noted researchers: Geoffrey Hinton, from the University of Toronto; Richard Sutton, from the University of Alberta, and Yoshua Bengio, from the Université de Montréal. This trio has been able to draw the leading talent from around the world to Canada, researchers who are now training post-graduates who will in turn be teaching the next generation of AI talent.
Montréal Toronto Edmonton
Basic researchers 11 6 11
Applied AI researchers 10 3 8
AI students 120 116 75
Estimated Total 140 140 107
To help retain and nurture academic talent, the federal government, Ontario and 30 corporate backers this year created the Vector Institute, a new AI research facility in Toronto that is seeded with $180 million over 10 years. The Montreal Institute for Learning Algorithms and the Alberta Machine Intelligence Institute follow similar models. But the research resources in other counties, especially the United States, tower over Canada’s. Former students and colleagues of Canada’s three AI leaders now lead AI divisions at Apple, OpenAI, Facebook and Google.

The Emergence of AI Superpowers

The concentration of AI research has grown sharply since 2012, with the United States and China emerging as AI superpowers. Share of machine-learning patents Deep learning papers published Deep learning publications cited

The Start-Up Challenge

  • Around the world, 650 AI start-ups raised US$5 billion in 2016.[1]
    • The number of AI deals (funding rounds and exits) has increased more than fivefold since 2012
  • Canada accounted for 18 of the 658 AI acquisitions in 2015[2]
    • No Canadian company ranks in the top acquirers for AI startups
AI Global Deal Share 2016

The Corporate Challenge

American tech companies have put millions into Canadian AI. Microsoft pledged to invest $7 million in AI research in Montreal as part of its January 2017 purchase of machine language start-up Maluuba, while Google donated $4.5 million to MILA in 2016 and has opened labs in Montreal and Toronto. In May, Uber hired University of Toronto professor Raquel Urtasun to run a new Toronto AI lab focusing on driverless car technology. Canadian companies have also invested in AI. NextAI, a partnership between RBC, Magna, BDC Capital and Scotiabank, was launched in 2016, with $5 million in initial funding to draw entrepreneurs to Canada to work on AI challenges. More than 20 Canadian companies have committed to funding the Vector Institute. Of the top 60 companies on the TSX, 22 have expressed interest in AI and 13 have publicly announced investments in AI. (See Appendix). Between the federal and provincial governments, academic networks and partnerships including the Vector Institute and MILA, and commitments from private corporations, nearly $500 million has been committed to developing Canada’s artificial intelligence ecosystem over the past 18 months. Number of AI Companies

Making Canada AI-Ready – 10 Ways for Government, Business and Academia to Build on Canada’s Success.

1. Create an AI Council to Guide Policy: A private sector-led council could advise government on AI opportunities and challenges, and help track and benchmark Canada’s adoption of AI relative to global competitors. 2. Expand the AI Talent Pool: Set an ambitious national target for both graduation levels in AI and related fields, and immigration levels for global AI talent. 3. Make the Workforce AI-Ready: Equip students with AI-complementary skills, including work-integrated learning to ensure broad student exposure. This should include a focus on girls to ensure more gender balance in AI-related fields, including design, interface and impact. 4. Promote AI Across Business Sectors: With business groups (Business Council of Canada, chambers of commerce), diffuse understanding of AI across organizations and encourage its adoption by all key sectors to build Canadian competitiveness. 5. Focus Research Funding on Commercial Innovation: Ensure publicly-funded AI research focuses on commercial application— and require government funding agencies to better coordinate AI investments. 6. Develop an AI-Focused IP Strategy: Modernize the IP regime to support the monetization and commercial scale-up of ideas in Canada, and to guard against activities (e.g. patent trolling) that stymie Canadian commercial innovation. 7. Leverage Our Data: Establish a national data strategy, including a possible data bank for Canadian-owned companies and entrepreneurs to help them build scale in key areas. 8. Create a National Challenge: Pool government and private resources, including data, to help Canadian firms, entrepreneurs and researchers use AI to solve grand challenges such as carbon emissions and hospital wait times. 9. Pursue an AI Trade and Investment Agenda: Create a subject-expert AI representative in the federal government to work with multinational companies and investors. Apply an AI lens to trade negotiations. Review investment policies to consider the interests of Canadian firms. 10. Position Canada as a Global Leader in Advancing AI for Good: Play a constructive role, through the G20 and multilateral organizations, to convene and build global awareness about the social, economic and cultural consequences of AI.
Appendix
Public AI interest and investment, TSX60 companies [5]
AI interest AI investment
1.     Bank of Montreal 1.     Bank of Montreal
2.     Bank of Nova Scotia 2.     Bank of Nova Scotia
3.     Barrick Gold Corporation 3.     BlackBerry Limited
4.     BCE Inc. 4.     George Weston Limited
5.     BlackBerry Limited 5.     Loblaw Companies Limited
6.     Canadian Imperial Bank of Commerce 6.     Magna International Inc.
7.     CGI Group Inc. 7.     Manulife Financial Corporation
8.     George Weston Limited 8.     Power Corporation of Canada
9.     Goldcorp Inc. 9.     Royal Bank of Canada
10.  Loblaw Companies Limited 10.  Sun Life Financial Inc.
11.  Magna International Inc. 11.  Telus Corporation
12.  Manulife Financial Corporation 12.  Thomson Reuters Corporation
13.  National Bank of Canada 13.  Toronto-Dominion Bank
14.  Power Corporation of Canada
15.  Rogers Communications Inc.
16.  Royal Bank of Canada
17.  Sun Life Financial Inc.
18.  Suncor Energy Inc.
19.  Teck Resources Limited
20.  Telus Corporation
21.  Thomson Reuters Corporation
22.  Toronto-Dominion Bank
 
[1] The 2016 AI Recap: Startups See Record High In Deals And Funding. (CB Insights, Jan. 2017.)
[2] The 2016 AI Recap: Startups See Record High In Deals And Funding. (CB Insights, Jan. 2017.)
[3] The Geman Artificial Intelligence Landscape. Asgard.VC, February 2017.
[4] Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide. (IDC, October 2016.)
[5] Factiva press search
As Senior Vice President, Office of the CEO at RBC, John Stackhouse is responsible for interpreting trends for the executive leadership team and Board of Directors with insights on how these are affecting RBC, its clients and society at large. Prior to this, John was editor-in-chief of The Globe and Mail (2009-14), editor of Report on Business, the newspaper’s national editor, foreign editor and its foreign correspondent based in New Delhi, India (1992-99).