Borealis AI has big goals for 2019 and exceeding those goals requires exceptional leadership. Just two weeks into the new year, we’re well on our way with the addition of Dr. Kathryn Hume, who joins our team today as Director of Business Development. In her new role, Kathryn will oversee the application of our academic research within the bank, help inform our strategy and also tap into her broad experience to assist with driving Borealis AI's brand profile among key audiences.

Kathryn brings an unusually rich and varied background to the AI field. In addition to holding prior leadership positions at Integrate AI and Fast Forward Labs (Cloudera), she’s a prolific speaker and author on AI, has mastered seven languages and holds a PhD in Comparative Literature from Stanford. In her spare time, she teaches courses on enterprise adoption of AI, law and ethics at Harvard, MIT, Stanford and University of Calgary (just kidding, she doesn’t have any spare time).

As this introduction barely scratches the surface, we thought it would help to let Kathryn do the talking.

With so many options open to you, why choose a bank?

People underestimate how much of our lives are touched by banking as the substrate for the entire economy. Banking has macro impact—with risk management undergirding international market stability—and micro impact, where we all entrust banks with our financial assets to support our daily needs, like food, and our life aspirations, like education.

Now with AI, we’re able to use rich data that’s far more relevant to banking. People may not be aware that one of the first production deep learning applications was the use of computer vision to automatically recognize handwritten digits on cheques. This had been a rate limiter for the ATM and now, just a few years later, a customer can easily insert up to 50 cheques at a time and have the denominations read, analyzed and deposited within seconds. And now that we can also recognize and generate speech, what else might we do? What could payments look like? I’m most interested in AI applications like this where the tech hides behind the scenes but makes our lives so much easier. 

What drew you to Borealis AI?

First off, I really love the team and culture. I find there’s a mixture of curiosity and pure research talent. I also love that it’s a culture grounded in integrity. Everybody here takes the time to mean what they say and that’s very important to me. Apart from the culture, it’s exciting to return to my academic roots while pursuing my long-term career ambition, which is to be at the forefront of early commercialization of academic and scientific research. 

What are some of the long-term goals you’d like to achieve here? 

There are a lot of existing applications the team has already built and I’m excited to bring them out of the lab and into production across the bank. I’m also looking forward to solidifying the relationships with our academic partners and to use the success of Borealis AI as an example of how academia and business can work together to holistically bridge gaps between both worlds.

You’ve gained a strong foothold in responsible AI. What do people need to know about the potential for responsible AI frameworks within finance?

My approach to responsible AI comes from a firm belief that ethics occurs in the trenches. There are, obviously, aspects of ethics that tackle large questions about AI’s impact on society, but I think the rubber really hits the road when a group of people collaborating to build a machine learning system have come together from different departments to make a series of tactical choices together. I’m excited to put this into practice here at Borealis AI. What better place to be impacting the future of responsible AI than in one of the world’s largest banks?

What brought you to Canada and, more importantly, what’s kept you here?

I was working in New York in early 2017 and it was common knowledge in the American machine learning research community that Canada was the place to be. The Vector Institute had just been established in Toronto and it was interesting to observe this experiment in building a commercialization leg from a university research department. I originally moved here to join a company called Integrate AI. What’s kept me in Toronto is the excitement of working in an ecosystem that feels similar to what Silicon Valley was like 15 years ago. There are new companies popping up everywhere and I sense the right energy flowing between groups in academia, policy, government and business. It’s a unique place in time to be. I also love Amii (in Edmonton) and Mila (in Montreal). What’s going on in the Canadian ecosystem is just amazing to behold. 

Do you often get asked what someone with a Comparative Literature degree is doing in AI?  

I got my PhD in Comparative Literature, but I actually have a strong math and science background. In fact, my dissertation is about the use of habit (or repetitive action) as a technique to generate knowledge in 17th century mathematics, philosophy and literature. I’ve come to believe since then that I inadvertently wrote a history of supervised learning through this work. Supervised learning is an AI technique that starts with a set of labeled training examples. For example, we teach an algorithm to adequately identify that a picture of a cat is a cat by giving the images a “cat” label, then training the system over time. The “supervised learning” I wrote about in my thesis pertains to human self-transformation: that if we want to become a different type of person, we have to think a certain way, then practice those thoughts so we don’t default to our old habits.

How do those capabilities intersect in the real world?

Years ago, I gave a talk about why my background as an intellectual historian of math and philosophy actually makes me a great product marketer. My work doesn’t ask whether philosophers like Descartes or Leibniz or Newton were “right”; rather, it asks what did they think they were thinking? So, my task was to read everything they’d read and try to reconstruct what they thought so as to reinterpret what they were saying. It’s an excellent skill set for someone in business development because when you’re working as a translator between academic machine learning researchers and businesspeople, you have to do that work on both sides. How do the researchers think? What are they reading? How do they use language to express their point of view? Similarly, how do the bankers in the various divisions of the bank think? What do they read? How do they see the world? And, most importantly, can we make those two points meet at the intersection? These are the unique translation skills my background has provided, and I’ve seen it unfold to great effect in the boardroom. I’m really looking forward to adapting it to this next chapter of my career at Borealis AI.