The output of a LLM is analogous to re-saving an image as a lo res JPEG. Data is being processed and altered using statistics, but nothing "new" is being created, only lower quality derivatives. That's why you can't train a LLM on the output of a LLM.
Unpopular Opinion
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This shouldn't need an explanation. If your post or comment is made just to get a rise with no real value, it will be removed. You do this too often, you will get a vacation to touch grass, away from this community for 1 or more days. Repeat offenses will result in a perma-ban.
Instance-wide rules always apply. https://legal.lemmy.world/tos/
This is actually a decent argument, but there has to be a threshold. For instance, if I take the average of all RGB values in an image, and distribute a pixel with the average, is that breaking copyright or somehow immoral?
I recently looked into the speculated model-size and speculated training set size of GPT and Stable Diffusion, and it does appear that if you thought of them as compression algorithms, they'd only be doing something like 1:7 compression. These ratios aren't outlandish for lossy compression.
Compression and redistribution isn't the (stated) goal of these models. Hypothetically, these models are learning patterns and associations of things like styles and how humans write text. And they appear to do things a little beyond just copying and pasting. So, hypothetically, a lot of the model size could mostly consist of learned styles and human preferences, rather than just a compressed database of the images it was trained on. I guess the real test is trying to prompt the models to reproduce an item in its training set, and evaluating how similar it is.
Disagree. Let's say the government makes a big mistake and never outlaws some activity that it really should have long ago. Sure it's legal to lean into it and amp it up by trillions of dollars, but that doesn't make the situation any more ethical.
For personal or public use, I'm fine with it. If you use it to make money, that's when I get upsetti spaghetti.
Ok. Devil's Advocate: how is a software engineer profiting from his AI model different from an artist who leans to draw by mimicking the style of public works? Asking for a friend.
Good question.
Ok, so let's say the artist does exactly what the AI does, in that they don't try to do anything unique, just looking around at existing content and trying to mix and mash existing ideas. No developing of their own style, no curiosity of art history, no humanity, nothing. In this case I would say that they are mechanically doing the exact same thing as an AI is doing. Do I think I they should get payed. Yes! They spent a good chunk of their life developing this skill, they are a human, they deserve to get their basic needs met and not die of hunger or exposure. Now, this is a strange case because 99.99% of artists don't do this. Most develop a unique style and add life experience in their art to generate something new.
A Software Engineer can profit off their AI model by selling it. If they are make money by generating images, then they are making money off of hard working artists that should be payed for their work. That isn't great. The outcome of allowing this is that art will no longer be something you can do to make a living. This is bad for society.
It also should be noted that a Software Engineer making an AI model from scratch is 0.01% of the AIs being used. Most people, lay people, who have spent very little time developing art or Software Engineering skills can easily use an existing model to create "art". The result of this is that many talented artists that could bring new and interesting ideas to world are being out competed by one guy with a web browser producing sub-par sloppy work.
Good question!
First, that artist will only learn from a few handful of artists instead of every artist's entire field of work all at the same time. They will also eventually develop their own unique style and voice--the art they make will reflect their own views in some fashion, instead of being a poor facsimile of someone else's work.
Second, mimicking the style of other artists is a generally poor way of learning how to draw. Just leaping straight into mimicry doesn't really teach you any of the fundamentals like perspective, color theory, shading, anatomy, etc. Mimicking an artist that draws lots of side profiles of animals in neutral lighting might teach you how to draw a side profile of a rabbit, but you'll be fucked the instant you try to draw that same rabbit from the front, or if you want to draw a rabbit at sunset. There's a reason why artists do so many drawings of random shit like cones casting a shadow, or a mannequin doll doing a ballet pose, and it ain't because they find the subject interesting.
Third, an artist spends anywhere from dozens to hundreds of hours practicing. Even if someone sets out expressly to mimic someone else's style, teaches themselves the fundamentals, it's still months and years of hard work and practice, and a constant cycle of self-improvement, critique, and study. This applies to every artist, regardless of how naturally talented or gifted they are.
Fourth, there's a sort of natural bottleneck in how much art that artist can produce. The quality of a given piece of art scales roughly linearly with the time the artist spends on it, and even artists that specialize in speed painting can only produce maybe a dozen pieces of art a day, and that kind of pace is simply not sustainable for any length of time. So even in the least charitable scenario, where a hypothetical person explicitly sets out to mimic a popular artist's style in order to leech off their success, it's extremely difficult for the mimic to produce enough output to truly threaten their victim's livelihood. In comparison, an AI can churn out dozens or hundreds of images in a day, easily drowning out the artist's output.
And one last, very important point: artists who trace other people's artwork and upload the traced art as their own are almost universally reviled in the art community. Getting caught tracing art is an almost guaranteed way to get yourself blacklisted from every art community and banned from every major art website I know of, especially if you're claiming it's your own original work. The only way it's even mildly acceptable is if the tracer explicitly says "this is traced artwork for practice, here's a link to the original piece, the artist gave full permission for me to post this." Every other creative community writing and music takes a similarly dim views of plagiarism, though it's much harder to prove outright than with art. Given this, why should the art community treat someone differently just because they laundered their plagiarism with some vector multiplication?
if they're using creative commons licenses (or other sharing licenses) then it's fine! but the model is then alsp bound by the same licenses because that's how licenses work
Huh I read your headline in a sarcastic tone so was totally ready to argue with you. But I agree. Not sure if it's an unpopular opinion though.
This falls squarely into the trap of treating corporations as people.
People have a right to public data.
Corporations should continue to be tolerated only while they carefully walk an ever tightening fine line of acceptable behavior.
Sure but restricting open source efforts is restricting people.
Yes. Large groups of people acting in concert, with large amounts of funding and influence, must be held to the highest standards, regardless of whether they're doing something I personally value highly.
An individual's rights must be held sacred.
When those two goals are in conflict, we must melt the corporation-in-conflict down for scrap parts, donate all of its intellectual property to the public domain, and try again with forming a new organization with a similar but refined charter.
Shareholders should be, ideally, absolutely fucked by this arrangement, when their corporation fucks up, as an incentive to watch and maintain legal compliance in any companies they hold shares in and influence over.
Investment will still happen, but with more care. We have historically used this model to great innovative success, public good, and lucrative dividends. Some people have forgotten how it can work.
I think they are saying that preventing open source models being trained and released prevents people from using them. Trying to make training these models more difficult doesn't just affect businesses, it affects individuals too. Essentially you have all been trying to stand in the way of progress, probably because of fears over job security. It's not really different to being a luddite.
Essentially you have all been trying to stand in the way of progress,
Fuck progress from anyone who can't be bothered to do it right. There's justified risks where the cost of inaction is just as horrible as action. This isn't that, and everyone saying it is, is an asshole whose shouting about this we would all be better off without.
This work can be done correctly, and even reasonably quickly. Shortcuts aren't merited.
probably because of fears over job security. It's not really different to being a luddite.
My job is secure. I have substantially more than typical expertise in language models.
The emperor, today, is butt naked. Anyone telling you we are about to see fast new progress is full of shit, and isn't your friend.
I've seen this before, and I'll see it again.
I've given a polite warning, where it looked like folks might listen. The rest aren't my problem.
Never thought about it like that, that's a really good way of looking at it.
I can agree, but any output must be instantly public domain.
Yeah, I think that's the current precedent in the US.
Agree for these reasons:
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Legally: It's always been legal (in the US at least) to relay the ideas in a copywrited work. AI might need to get better at providing a bibliography, but that's likely a courtesy more than a legal requirement.
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Culturally: Access to knowledge should be free. It's one of the reasons public libraries exist. If AI can help people gain knowledge more quickly and completely, it's just the next evolution of the same idea.
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Also Culturally: Think about what's out on the internet. Millions of recipes, no doubt copied from someone else, with pages of bullshit about how the author "grew up on a farm that produced Mohitos". For decades now, "content creators" have gotten paid for millions of low quality bullshit click bait articles. There's that. Most of the real "knowledge" on the internet is freely accessible technical / product documentation, forum posts like StackOverflow, and scientific studies. All of it is stuff the authors would probably love to have out there and freely accessible. Sure, some accidental copywrite infringement might happen here and there, but I think it's a tiny problem in relation to the value that AI might bring society.
I don’t have a problem with tech companies doing statistics on publicly available data, I have a problem with them getting rich by charging money for the collective creative works of humanity. But if they want to share their work for free, I have no issue with that.
Yeah, because corporations never make money off things they make available free of charge. There's no way this could go wrong.
“They should pay their sources!”
Source is 600GB of raw copied website data mixed in a giant witches cauldron
Define "public".
Publicly available is not the same as public domain. You should respect the copyright, especially of small creators. I'm of the opinion that an ML model is a derivative work, and so if you've trawled every website under the sun for data to feed your model you've violated copyright.
There are multiple facets here that all kinda get mashed together when people discuss this topic and the publicly available/public domain difference kinda gets at that.
- An AI company downloading a publicly available work isn't a violation of copyright law. Copyright gives the owner exclusive right to distribute their work. Publishing it for anybody to download is them exercising that right.
- Of course, if the work isn't publicly available and the AI company got it, someone probably did violate copyright laws, likely the people who distributed the data set to the company because they're not supposed to be passing around the work without the owner's permission.
- All that is to say, downloading something isn't making a copy. Sending the work is making a copy, as far as copyright is concerned. Whether the person downloading it is going to use it for something profitable doesn't really change anything there. Only if they were to become the sender at some later point does it matter. In other words, there's no violation of copyright law by the company that can really occur during the whole "training" phase of AI development.
- Beyond that, AI isn't in the business of serving copies of works. They might come close in some specific instances, but that's largely a technical problem that developers want to fix than a fundamental purpose of these models.
- The only real case that might work against them is whether or not the works they produce are derivative... But derivative/transformative has a pretty strict legal definition. It's not enough to show that the work was used in the creation of a new work. You can, for example, create a word cloud of your favorite book, analyze the tone of news article to help you trade stocks, or produce an image containing the most prominent color in every frame of a movie. None of these could exist without deriving from a copyrighted work but none of them count as a legally derivative work.
- I chose those examples because they are basic statistical analyses not far from what AI training involves. There's a lot of aspects of a work that are not covered by copyright. Style, structure, factual information. The kinds of things that AI is mostly interested in replicating.
- So I don't think we're going to see a lot of success in taking down AI companies with copyright. We might see some small scale success when an AI crosses a line here or there. But unless a judge radically alters the bounds of copyright law, at everyone's detriment, their opponents are going to have an uphill battle to fight here.
An AI model could be seen as an efficient but lossy compression scheme, especially when it comes to images... And a compressed jpeg of an image is still seen as a copy so why would an AI model trained on reproducing it be different?
Are you suggesting that the model itself is a compressed version of its training data? I think it requires some stretches of how training works to accept that.
It depends on how much you compress the jpeg. If it gets compressed down to 4 pixels, it cannot be seen as infringement. Technically, the word cloud is lossy compression too: it has all of the information of the text, but none of the structure. I think it depends largely on how well you can reconstruct the original from the data. A word cloud, for instance, cannot be used to reconstruct the original. Nor can a compressed jpeg, ofc; that’s the definition of lossy. But most of the information is still there, so a casual observer can quickly glean the gist of the image. There is a line somewhere between finding the average color of a work (compression down to one pixel) and jpeg compression levels.
Is the line where the main idea of the work becomes obscured? Surely not, since a summary hardly infringes on the copyright of a book. I don’t know where this line should be drawn (personally, I feel very Stallman-esque about copyright: IP is not a coherent concept), but if we want to put rules on these things, we need to well-define them, which requires venturing into the domain of information theory (what percentage of the entropy in the original is part of the redistributed work, for example), but I don’t know how realistic that is in the context of law.