Jean-François Gagné is a serial entrepreneur in the software industry, founder and CEO of the company Element AI.

Mapping the Canadian AI Ecosystem

Mapping the Canadian AI Ecosystem

UPDATE April 25, 2018: New report for 2018 available here.

UPDATE June 13, 2017: Last week I posted V1 of our Map of the Canadian AI Ecosystem, and since then I’ve been inundated with additions. While it’s still incomplete, I thought it was important to update the map currently being sent around and reinforce the idea that this ecosystem is in constant flux. Already, our list has mushroomed from about 160 startups to over 550. Goes to show how much is happening just below our attention.

I’ve been putting together a map on Canada’s AI ecosystem, which I first revealed last week in my keynote on C2 Montreal’s main stage. As promised, I’m publishing that map at the bottom of this post.

Given the speed at which the industry is progressing, this map is constantly evolving, so I’ll be sharing updates as we add them. If you have an addition to make, drop me a line!

The talent pool for AI research is tiny; at the office, we’ve tracked about 5600 researchers globally who are making an impact in the field. This size makes it critical that the industry players build relationships and share knowledge, to create an ecosystem that helps facilitate progress in AI so that we’re able to better specialize.

In the last couple of years, the Canadian AI ecosystem was pretty fractured, each cluster trying to win the race and get ahead of the pack. Cities like Montreal, Vancouver or Toronto would announce how their city was the place to be: great quality of life, financial incentives, a flourishing venture capital scene, some of the best researchers in the world, etc. The message tended to be that we have the ingredients to be the next hot spot.

Up until recently Canada was not a heavyweight in the artificial intelligence market. The United States dominated (some even calling it a “strategic monopoly”), with China and Japan holding second and third place. Where Canada shined was fundamental research. Thanks to the Canadian government’s willingness to invest in long, difficult pursuits, our universities are the source of some of the big breakthroughs that lead to deep learning and reinforcement learning, two of the most important innovations in AI up to now. Having some of the best universities also means that we train some of the best artificial intelligence specialists in the world. But, without much serious competition here in Canada, much of that talent went south to the tech giants in Silicon Valley.

This is where the new influx in research funding from Ottawa as well as Ontario and Quebec comes into play. All three governments have chosen to offer massive support for the artificial intelligence scene, giving $125M, $50M and $100M, respectively, to keep this research engine running. Indeed, Edmonton received $35M from Ottawa while Vancouver received none of that federal money, despite having 5x the number of startups that Edmonton has.

The reason is that Edmonton is home to Richard Sutton’s lab, Amii, where he’s done a lot of very influential research in Reinforcement Learning. Meanwhile, it’s been no secret that Vancouver is just too expensive for a research professor, causing many to leave for other universities or companies. The balance of funding will soon be leaning towards building up tech companies, but those are built on the solid research coming out of our Universities. Edmonton has a great opportunity to build their startup ecosystem before the venture funding really kicks in.

For now, research is what gave Canada its edge and it’s important we don’t lose that before our startup ecosystem matures. But if we want to go beyond research and become big players in the AI market, research is not enough. That’s why we didn’t want to ignore Vancouver in our map as the startup scene heats up, and they have historical economic links to East Asia where a lot of the action is for the AI industry.

 Click for pdf

Click for pdf


$1B+ invested in the last year

If we sum up all the available numbers for AI research investments (including other government funding like the $93.5M awarded to IVADO by the Canada Research Excellence Fund last September, as well as private funds invested in public or semi-public labs) we end up with close to $500M in funding across the country.

There is also growing investment in startups this past year, according to Pitchbook and Crunchbase, adding about $350M in venture capital invested. To that we only need to add estimates for undisclosed acquisitions and investments in private labs, which run around $280M, and we are well above the Billion Dollar mark for investments in the last year.

Beyond that, when we look up other domains that work hand in hand with AI, such as Big Data, cloud infrastructure and the like, that number grows even higher. Big data alone is said to have about $900M in R&D investment per year, while investment in infrastructure relating to AI in the last year is estimated at around $350M.

For a country of our size, this is huge. We are not the only ones in the AI race; the British government has announced $20m to accelerate their already bustling AI sector, France has organised a national strategy for artificial intelligence, China has pledged more than $2B towards AI, and the US has budgeted $175M in direct funding, to name only a few.

Reversing the Brain Drain

One of the most common critiques of the Canadian ecosystem is that all of our talent has already gone South. Silicon Valley seems to pull talent from around the world like an irresistible force. Although this was never entirely true, I think it will be even less so in the coming years. What made Silicon Valley's talent pump work up to now was its ecosystem of large firms and venture capital feeding startups, as well as research who in turn generate the innovations to push the large firms forward. Up to a couple of years ago, Canada didn’t really have an ecosystem that could compete with that; our talent pump was strong enough to pull talent from all over the world but not enough to keep the best and brightest that came out of our universities.

With investments from the federal and provincial governments in research, as well as from Big Tech, the Canadian talent pump is growing quickly. I would love to boost the total number of researchers in Canada, but we’re also focused on raising the number of collaborators. We’re ultimately limited in how many people we can convince to physically move here, so collaborative research and creating a global ecosystem will become the theme.

AI has an awesome culture of open research and collaboration. The more we focus collectively on big problems, the more people will  benefit as a whole. The big value is going to come when we start seeing ecosystem maps like the one above that not just connect cities within countries, but across borders, too.

Big thanks to Yoan Mantha for his work on this.

Photo by Dmitriy Burlakov.

What Happens When Noise Becomes Information? - Pt. 2

What Happens When Noise Becomes Information? - Pt. 2

What happens when noise becomes information?

What happens when noise becomes information?