Avram Piltch is the editor in chief of Tom’s Hardware, and he’s written a thoroughly researched article breaking down the promises and failures of LLM AIs.
They have the right to ingest data, not because they’re “just learning like a human would". But because I - a human - have a right to grab all data that’s available on the public internet, and process it however I want, including by training statistical models. The only thing I don’t have a right to do is distribute it (or works that resemble it too closely).
In you actually show me people who are extracting books from LLMs and reading them that way, then I’d agree that would be piracy - but that’d be such a terrible experience if it ever works - that I can’t see it actually happening.
You’re making two, big incorrect assumptions:
- Simply seeing something on the internet does not give you any legal or moral rights to use that thing in any way other than things which are, or have previously been, deemed to be “fair use” by a court of law. Individuals have personal rights over their likeness and persona, and copyright holders have rights over their works, whether they are on the internet or not. In other words, there is a big difference between “visible in public” and “public domain”.
- More importantly, something that might be considered “fair use” for a human being do to is not necessary “fair use” when a computer or “AI” does it. Judgements of what is and is not fair use are made on a case by case basis as a legal defense against copyright infringement claims, and multiple factors (purpose of use, nature of original work, degree and sustainability of use, market effect, etc.) are often taken into consideration. At the very least, AI use has serious implications on sustainability and markets, especially compared to examples of human use.
I know these are really tough pills for AI fans to swallow, but you know what they say… “If it seems too good to be true, it probably is.”
One the contrary - the reason copyright is called that is because it started as the right to make copies. Since then it’s been expanded to include more than just copies, such as distributing derivative works
But the act of distribution is key. If I wanted to, I could write whatever derivative works in my personal diary.
I also have the right to count the number of occurrences of the letter ‘Q’ in Harry Potter workout Rowling’s permission. This I can also post my count online for other lovers of ‘Q’, because it’s not derivative (it is ‘derived’, but ‘derivative’ is different - according to Wikipedia it means ‘includes major copyrightable elements’).
Or do more complex statistical analysis.
Two things:
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Many of these LLMs – perhaps all of them – have been trained on datasets that include books that were absolutely NOT released into the public domain.
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Ethically, we would ask any author who parrots the work of others to provide citations to original references. That rarely happens with AI language models, and if they do provide citations, they often do it wrong.
Is there a meaningful difference between reproducing the work and giving a summary? Because I’ll absolutely be using AI to filter all the editorial garbage out of news, setup and trained myself to surface what is meaningful to me stripped of all advertising, sponsorships, and detectable bias
I have yet to find an LLM that can summarize a text without errors. I already mentioned this in another post a few days back, but Google‘s new search preview is driving me mad with all the hidden factual errors. They make me click only to realize that the LLM told me what I wanted to find, not what is there (wrong names, wrong dates, etc.).
I greatly prefer the old excerpt summaries over the new imaginary ones (they‘re currently A/B testing).
I’m sick and tired of this “parrots the works of others” narrative. Here’s a challenge for you: go to https://huggingface.co/chat/, input some prompt (for example, “Write a three paragraphs scene about Jason and Carol playing hide and seek with some other kids. Jason gets injured, and Carol has to help him.”). And when you get the response, try to find the author that it “parroted”. You won’t be able to - because it wouldn’t just reproduce someone else’s already made scene. It’ll mesh maaany things from all over the training data in such a way that none of them will be even remotely recognizable.
Well, I think that these models learn in a way similar to humans as in it’s basically impossible to tell where parts of the model came from. And as such the copyright claims are ridiculous. We need less copyright, not more. But, on the other hand, LLMs are not humans, they are tools created by and owned by corporations and I hate to see them profiting off of other people’s work without proper compensation.
I am fine with public domain models being trained on anything and being used for noncommercial purposes without being taken down by copyright claims.
