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.
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.
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).
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.
In the long run it doesn’t really matter if the LLM is or is not trained on all the information out there, as the LLM will be able to search the Web on demand and report back with what it finds. BingChat essentially already does that and we have a few summarizer bots doing similar jobs. The need to access Websites directly and wade through all the clickbait and ads in the hope you find the bit of information you are actually interested will be over.
The LLM will be Adblock, ReaderMode, SQL and a lot more rolled into one, a Swiss army knife for accessing and transforming information. Not sure where that leaves the journalists, but cheap clickbait might lose a lot of value.
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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.
Let’s be clear on where the responsibility belongs, here. LLMs are neither alive nor sapient. They themselves have no more “rights” than a toaster. The question is whether the humans training the AIs have the right to feed them such-and-such data.
The real problem is the way these systems are being anthropomorphized. Keep your attention firmly on the man behind the curtain.
You know, I think ChatGPT is way ahead of a toaster. Maybe it’s more like a small animal of some kind.
One could equally claim that the toaster was ahead, because it does something useful in the physical world. Hmm. Is a robot dog more alive than a Tamagotchi?
There are a lot of subjects where ChatGPT knows more than I do.
Does it know more than someone who has studied that subject their whole life? Of course not. But those people aren’t available to talk to me on a whim. ChatGPT is available, and it’s really useful. Far more useful than a toaster.
As long as you only use it for things where a mistake won’t be a problem - it’s a great tool. And you can also use it for “risky” decisions but take the information it gave you to an expert for verification before acting.
I like the point about LLMs interpolating data while humans extrapolate. I think that’s sums up a key difference in “learning”. It’s also an interesting point that we anthropomorphise ML models by using words such as learning or training, but I wonder if there are other better words to use. Fitting?
That’s not a limitation of ML, but just how it is commonly used. You can take every parameter that neural network recognizes and tweak it, make it bigger, smaller, recombine it with other stuff and marvel at the results. That’s how we got origami porn, (de)cartoonify AI, QR code art, Balenciaga, dancing statues or my 5min attempt at reinventing cubism (tell AI to draw cubes over a depthmap).
Isn’t interpolation and extrapolation the same thing effectively, given a complex enough system?
Depending on the geometry of the state space, very literally yes. Think about a sphere, there’s a straight line passing from Denver to Guadalajara, roughly hitting Delhi on the way. Is Delhi in between them (interpolation), or behind one from the other (extrapolation)? Kind of both, unless you move the goalposts to add distance limits on interpolation, which could themselves be broken by another geometry
No, repeated extrapolation results in eventually making everything that ever could be made, constant interpolation would result in creating the same “average” work over and over.
The difference is infinite vs zero variety.
I also like the point about interpolation vs extrapolation. It’s demonstrated when you look at art history (or the history of any other creative field). Humans don’t look at paintings and create something that’s predictable based on those paintings. They go “what happens when I take that idea and go even further?” An LLM could never have invented Cubism after looking at Paul Cezanne’s paintings, but Pablo Picasso did.
