British Columbia proposed legislation to limit how much electricity will be available to artificial intelligence data centers, and moved to permanently ban new cryptocurrency mining projects.
The government of Canada’s third-most populous province will prioritize connections to its power grid for other purposes like mines and natural gas facilities because they provide more jobs and revenue for people in BC, the energy ministry said Monday.
“Other jurisdictions have been challenged to address electricity demands from emerging sectors and, in many cases, have placed significant rate increases on the backs of ratepayers,” the department said Monday.
That’s a reference to US states like Virginia and Maryland, where a proliferation of the power-hungry data centers needed for AI appears to be pushing up citizens’ power bills, according to a Bloomberg analysis. BC “is receiving significant requests for power” from these industries, Energy Minister Adrian Dix said at a press conference.
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I can give you one use case that has a public benefit. My brother works in research informatics at a children’s hospital. They use ai to identify children with rare diseases. My understanding is it tracks patterns of appointments and symptoms and matches the patients with specialists. Typically these patients wouldn’t be identified for years because doctors are looking for common ailments before any exotic disease.
There is lots of uses for urban planning related to population growth and census statistics as well.
I’d be curious to see data on the benefits, but assuming what you say is true: this example in medicine sounds like a pretty basic kind of machine learning and not something that requires massive energy-hungry data centers.
Same with the urban planning example. These are not the applications that require “sovereign AI compute” at scale. Those would be the generative AI applications like chatbots and image/video generators, as far as I understand these things.
AI data centres are usually about giant LLMs and agentic bots. “Ai” as in machine learning doesn’t need giant data centres and has been progressing quite well without them.
The term “AI” tends to get thrown around to claim all the benefits of the entire field to excuse the excesses of a very narrow slice.
These applications are great, but they’re not what these compute centers are for. For those applications, a regular supercomputer will do. Those gigantic and power hungry data centers are used for LLM training, which is a VC-funded arms race that we don’t actually need to partake in.