No time to rest on laurels. Here's where Canada needs to focus next

When I first met with the RBC leadership in 2016, we discussed a place that would provide a draw for homegrown AI talent. We felt it was our corporate responsibility to create the right opportunities to retain the gifted graduates coming out of our Canadian universities, and to help the country build IP around this truly transformational technology – a science which was largely nurtured and advanced in our schools.

The place we envisioned would exist at the edge of the bank, so that we would have the flexibility to think a little differently about financial services. It would be a place where we tackled fundamental and applied research problems in machine learning and contributed that knowledge to the world. As an organization that prospers when Canada prospers, it was important that we were not just using science, but that we were giving back by putting it in the public domain.

Wednesday night, when we officially launched our fifth research centre in the heart of Vancouver, I looked around the room and saw we had made good on that dream.

In a conversation with John Stackhouse, Dave McKay and Greg Mori, I spoke to Borealis AI’s future-facing focus around education, commercialization and using our vast AI resources for good. Here are a few highlights from the panel.

1. Support public education around AI

I was recently honoured, along with Yoshua Bengio, to be named co-chair of the Canadian Advisory Council on AI. The council is currently advising the government on a number of fronts, but one of the primary ones is education. While much ink has been spilled around Canada’s academic achievements, the education I’m referring to in this case is public education.

It is imperative that we ensure the right information is out there, that it is well represented and that we pay attention to how the future of AI may or may not look. At present, there’s a lot of public fear. Some of it is justified. But unfortunately, most of the good stories around AI - the great advances that come from it and the enormous opportunities they present for good – are getting lost in the noise.

We have our work cut out for us: It’s been a difficult year for AI from a PR perspective. In fact, you could say we have a branding problem altogether. This is justified as many things have happened as a result of data mismanagement and misuse and, unfortunately, much of that blowback has been attributed to AI. Fortunately, we see communities coming together and taking these issues seriously. Governments also play a very important role here, establishing standards and guidelines for how AI should be used.

2. Commercialization needs to be our next focus

Canada is currently ranked #3 in the world for scientific papers published in AI and #3 in public policy. When it comes to IP, we’re #9. There’s a gap here between our intellectual contributions and our intellectual property that we need to close. The bridge lies in successful commercialization of our AI applications.

At Borealis AI and at RBC, we’re looking for ways to help Canadian companies and entrepreneurs succeed. This involves thinking beyond our own borders, in being really clear about what problem you’re trying to solve and identifying where that opportunity lies. From where we sit within the bank, we’re constantly working on communication and collaboration between the business and our research opportunities. We have the opportunity to solve a business problem with a completely different, research-based approach. There are so many great examples of it already.

But to go beyond the basic challenges of commercialization in Canada, we have to be willing to take a hard look at where we could improve. As Canadians, we tend to sell early, and perhaps prove risk averse when it comes to monetizing and scaling internationally. It’s not always the fault of our business leadership. We also have less-than-patient capital that wants to cash in early. There are many factors and complexities involved, but this is an achievable goal if we are willing to become more comfortable with being uncomfortable – the key ingredient in becoming better risk takers.

3. Training and supporting the next generation of AI talent

We’ve built up huge reputational capital, both on the strength of our academic performance and our commitment to AI for Good. We’ve also managed to leverage these homegrown advantages to create real value for Canadians. But how do we make sure future generations of machine learning innovation continue to see Canada as the front seat? We can’t take for granted the fact that our academic community got us here once. It requires sustained commitment to ensure continuity.

The students currently enrolled in high school and undergraduate programs will form part of that crucial next generation. They will be the future thought leaders, policymakers, marketers, researchers and entrepreneurs that accelerate Canada’s AI momentum. They will also be the leaders who can contribute to closing the AI education gap. The more people who actually understand how these algorithms work, the faster we can make that happen.

In Vancouver, we’re lucky to count on two world-class computer science universities – Simon Fraser University and University of British Columbia. This is very rare, but also a prime advantage: Our future success depends on this clustering of talent and mentorship. We also need to keep supporting great organizations like the Creative Destruction Lab and Next Canada that can guide them along the path to entrepreneurship. 

Canada is packed with extreme high-density talent, is community-minded, and has so much good to contribute to the world. I’ve seen first-hand what we can build when we work toward the same goal. It’s not just in the room I sat in this week, but in every corner of the country.