The government’s consultation on copyright and generative AI closed last week. The submissions are not yet public, but I am pleased to post my submission, which focused on an exception for text and data mining, the inclusion of copyrighted works in large language models, and the copyright implications of outputs from generative AI systems. My submission noted that the consultation raises several questions related to generative AI and copyright. I focused on three:
(1) Should Canada proceed with a text and data mining exception as recommended in the 2019 Copyright Act review?
(2) Should Canada introduce legislative reforms to address the use of copyright works in large language models (LLMs) that are central to the development of generative AI technologies?
(3) Should Canada introduce legislative reforms to address copyright-related questions arising from the outputs of generative AI systems?
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Prime Minister Justin Trudeau met earlier this week with Jean-Francois Gagné, the CEO of Element AI, the Montreal-based applied artificial intelligence lab. Trudeau tweeted that the two men “talked about what Canadians are doing in AI in Montreal & across the country, and how we can keep the industry thriving.”
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The federal government placed a big bet in this year’s budget on Canada becoming a world leader in artificial intelligence (AI), investing millions of dollars on a national strategy to support research and commercialization. The hope is that by attracting high-profile talent and significant corporate support, the government can turn a strong AI research record into an economic powerhouse.
Funding and personnel have been the top policy priorities, yet other barriers to success remain. For example, Canada’s restrictive copyright rules may hamper the ability of companies and researchers to test and ultimately bring new AI services to market.
What does copyright have to do with AI?
My Globe and Mail column notes that making machines smart – whether engaging in automated translation, big data analytics, or new search capabilities – is dependent upon the data being fed into the system. Machines learn by scanning, reading, listening or viewing human created works. The better the inputs, the better the output and the reduced likelihood that results may be biased or inaccurate.
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