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At home in his spacious but unpretentious 1950s residence on the edge of Mount-Royal Park, the AI guru clearly feels time is of the essence, both when it comes to his own to-do list for 2024 and for governments to rein in increasingly powerful artificial intelligence systems.

That infrastructure includes building much more powerful computers, stacked with thousands of graphics processing units (GPUs), components that are ideal for training or testing AI large language models like ChatGPT.

Siva Reddy, an assistant professor of linguistics and computer science at McGill University who is also a core academic member at Mila, estimates the total combined resources available for public AI research in Canada add up to about one-tenth of what a single big U.S. tech company has — just for itself.

While he “absolutely” supports Bengio’s idea of building one or more public supercomputers for work on large language models, Reddy points out it’s important to recognize the environmental impact of the energy required to operate them.

But Peltier says when it comes to spending on AI infrastructure, private companies who are focused on profit aren’t subject to the same fiscal constraints as governments, who have other public policy priorities to pay for, like fighting COVID-19 or dealing with the opioid crisis.

In Canada, Bengio has called out the federal government for moving too slowly to adopt Bill C-27, which includes provisions to partially regulate AI and is currently under consideration before the House’s standing committee on industry and technology.

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