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.
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.
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.
And yet, we know that the work is mechanically derivative.
So is your comment. And mine. What do you think our brains do? Magic?
edit: This may sound inflammatory but I mean no offense
No, I get it. I’m not really arguing that what separates humans from machines is “libertarian free will” or some such.
But we can properly argue that LLM output is derivative because we know it’s derivative, because we designed it. As humans, we have the privilege of recognizing transformative human creativity in our laws as a separate entity from derivative algorithmic output.
So is literally every human work in the last 1000 years in every context.
Nothing is “original”. It’s all derivative. Feeding copyrighted work into an algorithm does not in any way violate any copyright law, and anyone telling you otherwise is a liar and a piece of shit. There is no valid interpretation anywhere close.
Every human work isn’t mechanically derivative. The entire point of the article is that the way LLMs learn and create derivative text isn’t equivalent to the way humans do the same thing.
It’s complete and utter nonsense and they’re bad people for writing it. The complexity of the AI does not matter and if it did, they’re setting themselves up to lose again in the very near future when companies make shit arbitrarily complex to meet their unhinged fake definitions.
But none of it matters because literally no part of this in any way violates copyright law. Processing data is not and does not in any way resemble copyright infringement.
This issue is easily resolved. Create the AI that produces useful output without using copyrighted works, and we don’t have a problem.
If you take the copyrighted work out of the input training set, and the algorithm can no longer produce the output, then I’m confident saying that the output was derived from the inputs.
From Wikipedia, “a derivative work is an expressive creation that includes major copyrightable elements of a first, previously created original work”.
You can probably can the output of an LLM ‘derived’, in the same way that if I counted the number of 'Q’s in Harry Potter the result derived from Rowling’s work.
But it’s not ‘derivative’.
Technically it’s possible for an LLM to output a derivative work if you prompt it to do so. But most of its outputs aren’t.
a derivative work is an expressive creation that includes major copyrightable elements of a first, previously created original work
What was fed into the algorithm? A human decided which major copyrighted elements of previously created original work would seed the algorithm. That’s how we know it’s derivative.
If I take somebody’s copyrighted artwork, and apply Photoshop filters that change the color of every single pixel, have I made an expressive creation that does not include copyrightable elements of a previously created original work? The courts have said “no”, and I think the burden is on AI proponents to show how they fed copyrighted work into an mechanical algorithm, and produced a new expressive creation free of copyrightable elements.
I think the test for “free of copyrightable elements” is pretty simple - can you look at the new creation and recognize any copyrightable elements in it? The process by which it was created doesn’t matter. Maybe I made this post entirely by copy-pasting phrases from other people, who knows (well, I didn’t, only because it would be too much work), but it does not infringe either way…
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.
it’s basically impossible to tell where parts of the model came from
AIs are deterministic.
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Train the AI on data without the copyrighted work.
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Train the same AI on data with the copyrighted work.
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Ask the two instances the same question.
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The difference is the contribution of the copyrighted work.
There may be larger questions of precisely how an AI produces one answer when trained with a copyrighted work, and another answer when not trained with the copyrighted work. But we know why the answers are different, and we can show precisely what contribution the copyrighted work makes to the response to any prompt, just by running the AI twice.
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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
When you figure out how to train an AI without bias, let us know.
You’re confusing ai with chatgpt, but to answer your question: if it’s my own bias, why would I care that it’s in my personal ai? That’s kind of the point: using my personal lens (bias) to determine what info I would be interested in being alerted of
oooh I dunno man having an AI feed you shit based on what fits your personal biases is basically what social media already does and I do not think that’s something we need more of.
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).
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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.
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There’s a lot of opinion in here written in as if it’s fact.
Here I was thinking I could trust Mr Tom
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Unfortunately, many people believe that AI bots should be allowed to grab, ingest and repurpose any data that’s available on the public Internet whether they own it or not, because they are “just learning like a human would.” Once a person reads an article, they can use the ideas they just absorbed in their speech or even their drawings for free.
Iris van Rooj, a professor of computational cognitive science at Radboud University Nijmegen in The Netherlands, posits that it’s impossible to build a machine to reproduce human-style thinking by using even larger and more complex LLMs than we have today.
NY Times Tech Columnist Farhad Manjoo made this point in a recent op-ed, positing that writers should not be compensated when their work is used for machine learning because the bots are merely drawing “inspiration” from the words like a person does.
“When a machine is trained to understand language and culture by poring over a lot of stuff online, it is acting, philosophically at least, just like a human being who draws inspiration from existing works,” Manjoo wrote.
In his testimony before a U.S. Senate subcommittee hearing this past July, Emory Law Professor Matthew Sag used the metaphor of a student learning to explain why he believes training on copyrighted material is usually fair use.
In fact, Microsoft, which is a major investor in OpenAI and uses GPT-4 for its Bing Chat tools, released a paper in March claiming that GPT-4 has “sparks of Artificial General Intelligence” – the endpoint where the machine is able to learn any human task thanks to it having “emergent” abilities that weren’t in the original model.
Saved 93% of original text.
