this post was submitted on 06 Sep 2024
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Those claiming AI training on copyrighted works is "theft" misunderstand key aspects of copyright law and AI technology. Copyright protects specific expressions of ideas, not the ideas themselves. When AI systems ingest copyrighted works, they're extracting general patterns and concepts - the "Bob Dylan-ness" or "Hemingway-ness" - not copying specific text or images.

This process is akin to how humans learn by reading widely and absorbing styles and techniques, rather than memorizing and reproducing exact passages. The AI discards the original text, keeping only abstract representations in "vector space". When generating new content, the AI isn't recreating copyrighted works, but producing new expressions inspired by the concepts it's learned.

This is fundamentally different from copying a book or song. It's more like the long-standing artistic tradition of being influenced by others' work. The law has always recognized that ideas themselves can't be owned - only particular expressions of them.

Moreover, there's precedent for this kind of use being considered "transformative" and thus fair use. The Google Books project, which scanned millions of books to create a searchable index, was ruled legal despite protests from authors and publishers. AI training is arguably even more transformative.

While it's understandable that creators feel uneasy about this new technology, labeling it "theft" is both legally and technically inaccurate. We may need new ways to support and compensate creators in the AI age, but that doesn't make the current use of copyrighted works for AI training illegal or unethical.

For those interested, this argument is nicely laid out by Damien Riehl in FLOSS Weekly episode 744. https://twit.tv/shows/floss-weekly/episodes/744

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[–] [email protected] 29 points 2 months ago (2 children)

Generative AI does not work like this. They're not like humans at all, it will regurgitate whatever input it receives, like how Google can't stop Gemini from telling people to put glue in their pizza. If it really worked like that, there wouldn't be these broad and extensive policies within tech companies about using it with company sensitive data like protection compliances. The day that a health insurance company manager says, "sure, you can feed Chat-GPT medical data" is the day I trust genAI.

[–] [email protected] 4 points 2 months ago (1 children)

Ha, ya know? I think I know some people who will just regurgitate whatever input they receive

........

:(

[–] [email protected] 2 points 2 months ago

I feel you man lmao

[–] [email protected] 1 points 2 months ago (1 children)

I've just asked Gemini about cheese that slides off pizza, it didn't recommend glue.

[–] [email protected] 6 points 2 months ago (1 children)

The last I had heard of this were articles months in saying it was still not fixed, but this doesn't invalidate my point. It may have been retrained to respond otherwise, but it spouts garbled inputs.

[–] [email protected] 0 points 2 months ago (1 children)

It wasn't Gemini, but the AI generated suggestions added to the top of Google search. But that AI was specifically trained to regurgitate and reference direct from websites, in an effort to minimize the amount of hallucinated answers.

[–] [email protected] 1 points 2 months ago (1 children)

Do you have a source for Search Generative Experience using a separate model? As far as I'm aware, all of Google's AI services are powered by the Gemini LLM.

[–] [email protected] 1 points 2 months ago

No mention of Gemini in their blog post on sge And their AI principles doc says

We acknowledge that large language models (LLMs) like those that power generative AI in Search have the potential to generate responses that seem to reflect opinions or emotions, since they have been trained on language that people use to reflect the human experience. We intentionally trained the models that power SGE to refrain from reflecting a persona. It is not designed to respond in the first person, for example, and we fine-tuned the model to provide objective, neutral responses that are corroborated with web results.

So a custom model.