Thank you for inviting me to join you today.
As you've heard, I'm Gail Murphy, vice-president of research and innovation and a professor of computer science at the University of British Columbia. I currently serve as the vice-chair of the board of the Digital Research Alliance of Canada, which is a national not-for-profit organization with a mandate to provide compute, data and software for Canadian researchers. I recently had the honour of serving as a member of the Government of Canada's AI strategy task force.
UBC is the second-largest research university in Canada, with nearly 80,000 students and more than 17,000 faculty and staff at campuses in Vancouver and Kelowna and sites throughout British Columbia. Today, over 100 UBC faculty members and hundreds of students are focused on fundamental and applied AI research in the vibrant B.C. AI ecosystem, establishing and growing companies, integrating AI into the health system and furthering critical industry sectors like the life sciences, critical minerals and forestry.
AI is a research field that's moving at warp speed with new discoveries every day. Keeping apace is not only imperative; it's essential.
Only three years ago, the power of large language models born from Canadian ingenuity became evident to the public. Already, those at the forefront of AI are thinking about how to move beyond the limitations of these models. We cannot rely on letting others invent the future of AI. We need to keep inventing it here in Canada with Canadian values, but we also need to do more to ensure that these discoveries find their way into applications that benefit Canadians. Moving AI research into applications is challenging, in part because of the many pathways by which AI can be used.
One pathway is through the formation of AI-first companies that place AI at the core of their strategy and operations. For example, Variational AI, a Vancouver-based company working with UBC researchers, is focused on providing a foundational model to speed up drug discovery. AI-first companies often require significant infrastructure to build the models on which they rely.
Another pathway is when AI is embedded into products or services. For example, UBC researchers have shown how diagnosis in rural, remote and indigenous communities such as Haida Gwaii can be improved through the right combination of training, hand-held ultrasound devices, cloud platforms and AI. Making this pathway a reality requires support to bring AI experts together with domain experts.
Yet another pathway is to develop AI solutions for critical points in the supply chains of different economic sectors. For instance, UBC professor Frank Wood has created the UBC spinoff Inverted AI, which is building predictive human behaviour models for autonomous driving. Supporting these companies to be successful can require rethinking how to integrate Canadian companies into global trade supply chains.
There are four key areas in which we can start to better support the continuum and these pathways.
The first is through research. Canada can stay at the forefront of AI discovery by enhancing and expanding the AI institute model. The institutes can be enhanced to serve as hubs that bring talent from universities, colleges, technical institutes and industry together to address mission-driven, dual-purpose challenges. The institutes should be expanded to fully deploy world-class talent that is currently underutilized, including in B.C.
The second is focus. Canada should aim to focus on and accelerate specific areas of global strength. Focus could be brought through missions or challenges posed to the AI institutes. Potential areas of focus that could broadly benefit Canadians and the Canadian economy include AI for health and AI for robotics.
The third is AI supply chains. Canada should aim to globally lead in critical parts of the global AI supply chain for particular sectors. In selected areas, Canada can establish precompetitive industrial research and development centres based on successful models in other countries for different kinds of technologies. These centres accelerate industry within the country and create new companies and a highly trained workforce.
The fourth is compute infrastructure. Canada lags its peers on AI research infrastructure in multiple rankings. To support discovery and application related to Canadians, large-scale sovereign compute with access to Canadian data is needed for both researchers and SMEs. The Digital Research Alliance of Canada is well positioned to lead on this infrastructure for Canada.
I hope the committee finds these contributions to its study helpful. I thank you for the opportunity to speak with you and look forward to addressing any questions you may have.