Thank you, Dr. Murphy.
Thank you, Mr. Chair.
As Dr. Murphy said, I'm with the Canada research chair in trustworthy AI at Ontario Tech University, and director of our recently launched Mindful AI Research Institute, MAIRI.
We championed, through MAIRI, a vision for a thoughtful, intentional and inclusive approach to AI research and innovation, bringing together more than 70 professors with expertise spanning the social and the technical, providing us with the ability to tackle pressing challenges around AI in a deeply interdisciplinary way.
I'd like to express our thanks to the Canada research chair program, the tri-agency research program, the new frontiers in research fund and other federal research programs that have enabled us, for example, to do the research to inform the recently released new federal standard for accessible and inclusive AI—the first of its kind in the world. This standard ensures that the eight million Canadians living with disabilities will have barriers to using AI removed or reduced.
These programs enable us to do the research needed to understand the trustworthiness of the AI systems being promoted and considered for use in courtrooms to analyze forensic evidence, ensuring that any use of AI enhances objectivity and justice and doesn't undermine or threaten them. They're also enabling us to develop a new generation of reflective AI systems with enhanced social intelligence and safety based on new architectures.
I'd like to make three main points.
The first is that, in Canada, I believe we should diversify our efforts. Today, AI is deeply sociotechnical, and research and innovation must reflect this. We need to hear from all sectors of Canadian society, ensure that we include diverse perspectives and lived experiences, and bring multiple disciplines to bear on developing our understanding of the trustworthiness of AI, its impact and how we develop it well.
The days of AI research being mostly about machine learning are over. Canada's national strategy of focusing on three large machine-learning intensive institutes anchored in major urban centres worked well when the challenge was to get machine learning out of the lab and into businesses and people's lives. Now, as we move to the next phase, the challenge is a different, perhaps a more complex one that requires a new, more diversified approach.
My second point is about regulation and values. How should Canada lead globally in AI? What should AI leadership look like? What is the distinguishing feature in the Canadian approach to AI? I would argue that AI leadership is about values. It's about showing how to do it in the right way—a way that respects dignity and inclusivity, and a way that promotes equity, sustainability and truth. It's not about speed.
It is right to acknowledge the pressure to act quickly and to be agile and forward thinking, and we should acknowledge that the world looks to Canada to show how to do this with a values-first approach. Regulation is needed—and it's important—but it's also not enough.
Finally, I would like us to consider what the post-LLM world looks like. There is a question being asked as to whether we should be investing in competing to build the biggest and best LLMs. The LLM boom may be completing its arc, but through history we see that AI development continues with a new architecture, building on what's gone before and opening up new paths and breakthroughs.
How do we position ourselves well for the future? I would argue that, just as Canada did in the early stages of deep learning, without necessarily knowing that it would come to all this, we should be investing in the seeds of what's to come next and placing some bets.
Thank you. I look forward to your questions.