this post was submitted on 08 Jan 2024
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I've seen and heard your argument made before, not just for LLM's but also for text-to-image programs. My counterpoint is that humans learn in a very similar way to these programs, by taking stuff we've seen/read and developing a certain style inspired by those things. They also don't just recite texts from memory, instead creating new ones based on probabilities of certain words and phrases occuring in the parts of their training data related to the prompt. In a way too simplified but accurate enough comparison, saying these programs violate copyright law is like saying every cosmic horror writer is plagiarising Lovecraft, or that every surrealist painter is copying Dali.
Machines aren’t people and it’s fine and reasonable to have different standards for each.
But is it reasonable to have different standards for someone creating a picture with a paintbrush as opposed to someone creating the same picture with a machine learning model?
Yes, given that one is creating art and the other is typing words into the plagiarism machine.
This is called assuming the consequent. Either you're not trying to make a persuasive argument or you're doing it very, very badly.
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Well, machine learning algorithms do learn, it's not just copy paste and a thesaurus. It's not exactly the same as people, but arguing that it's entirely different is also wrong.
It isn't a big database full of copy written text.
The argument is that it's not wrong to look at data that was made publicly available when you're not making a copy of the data.
It's not copyright infringement to navigate to a webpage in your browser, even though that makes your computer download it, process all of the contents of the page, render the content to the screen and hold onto that download for a finite but indefinite period of time, while you perform whatever operations you like on the downloaded data.
You can even take notes on the data and keep those indefinitely, including using that derivative information to create your own similar works.
The NYT explicitly publishes articles in a format designed to be downloaded, processed and have information extracted from that download by a computer program, and then to have that processed information presented to a human. They just didn't expect that the processing would end up looking like this.
The argument doesn't require that we accept that a human and a computers system for learning be held to the same standard, or that we can't differentiate between the two, it hinges on the claim that this is just an extension of what we already find it reasonable for a computer to do.
We could certainly hold that generative AI is a different and new category for copyright law, but that's very different from saying that their actions are unacceptable under current law.
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Lemmy users in general loves to steal IP, no shock this post didn't get the love it deserved
Username checks out, sorta.
It doesn't work that way. Copyright law does not concern itself with learning. There are 2 things which allow learning.
For one, no one can own facts and ideas. You can write your own history book, taking facts (but not copying text) from other history books. Eventually, that's the only way history books get written (by taking facts from previous writings). Or you can take the idea of a superhero and make your own, which is obviously where virtually all of them come from.
Second, you are generally allowed to make copies for your personal use. For example, you may copy audio files so that you have a copy on each of your devices. Or to tie in with the previous examples: You can (usually) make copies for use as reference, for historical facts or as a help in drawing your own superhero.
In the main, these lawsuits won't go anywhere. I don't want to guarantee that none of the relative side issues will be found to have merit, but basically this is all nonsense.
Generally you're correct, but copyright law does concern itself with learning. Fair use exemptions require consideration of the purpose character of use, explicitly mentioning nonprofit educational purposes. It also mentions the effect on the potential market for the original work. (There are other factors required but they're less relevant here.)
So yeah, tracing a comic book to learn drawing is totally fine, as long as that's what you're doing it for. Tracing a comic to reproduce and sell is totally not fine, and that's basically what OpenAI is doing here: slurping up whole works to improve their saleable product, which can generate new works to compete with the originals.
I meant "learning" in the strict sense, not institutional education.
I think you are simply mistaken about what AI is typically doing. You can test your "tracing" analogy by making an image with Stable Diffusion. It's trained only on images from the public internet, so if the generated image is similar to one in the training data, then a reverse image search should turn it up.
It doesn’t matter how it “”learns””