AI is still a very new field, and we're still learning what types of AI techniques we're going to need in order to be at the cutting edge. Definitely, there are many techniques being developed at the universities that can be rolled into industry.
I'll just use one of my examples here in health care. There have been huge strides made in physician-assisted diagnostics. Whether it's on the imaging or clinical side, we need to find regulatory ways to encourage these kinds of techniques to be deployed in Canada. Health care costs in Canada are skyrocketing. AI has some potential. I think there are a lot of regulatory hurdles for trying out these techniques in Canada, and we should be looking at whether we can actually take a lead there.
The flip side of this is that because AI is so new, we don't actually know all the use cases. I'm a fan of figuring out how to build a meeting ground between industry and universities so we can see new kinds of data and challenges. We have not built those kinds of meeting grounds very effectively in Canada. In Germany, they have a number of institutes—the Fraunhofers, the Max Plancks—to create a place where people can work on these problems.
It has to be both ways: Take the techniques coming out of the universities and figure out how to more rapidly deploy them in industry, and also understand problems in industry and what kinds of research can be spawned from them to develop new techniques.