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.

If that were to happen, it’d only be for tech companies, not people. lol.

Yeah, I think that’s the current precedent in the US.

Yeah, because corporations never make money off things they make available free of charge. There’s no way this could go wrong.

Could the copywrited material consumed potentially fall under fair use? There are provisions for research purposes.

Incidentally, I read this a while ago, because I was training a classifier on mostly Creative Commons licensed works: https://creativecommons.org/2023/08/18/understanding-cc-licenses-and-generative-ai/

… we believe there are strong arguments that, in most cases, using copyrighted works to train generative AI models would be fair use in the United States, and such training can be protected by the text and data mining exception in the EU. However, whether these limitations apply may depend on the particular use case.

Creative Commons would not actually help here. Even the most permissive licence, CC-BY, requires attribution. If using material for training material requires a copyright licence (which is certainly not a settled question of law), CC would likely be just the same as all rights reserved.

(There’s also CC-0, but that’s basically public domain, or as near to it as an artist is legally allowed to do in their locale. So it’s basically not a Creative Commons licence.)

There are a lot of problems with it. Lots of people could probably tell you about security concerns and wasted energy. Also there’s the whole comically silly concept of them marketing having AI write your texts and emails for you, and then having it summarize the texts and emails you get. Just needlessly complicating things.

Conceptually, though, most people aren’t too against it. In my opinion, all the stuff they are labeling “generative AI” isn’t really “AI” or “generative”. There are lots of ways that people define AI, and without being too pedantic about definitions, the main reason I think they call it that, other than marketing, is that they are really trying to sway public opinion by controlling language. Scraping all sorts of copywritten material, and re-jumbling it to spit out something similar, is arguably something we should prohibit as copyright infringement. It’s enough of a gray area to get away with short term. By convincing people with the very language they use to describe it that they aren’t just putting other people’s material in a mixer, they are “generating new content”, they hope to have us roll over and sign off on what they’ve been doing.

Saying that humans create stories by jumbling together previous stories is a BS cop out, too. Obviously, we do, but humans have not, and do not have to give computers that same right. Also, LLMs are very complex, but they are also way way less complex than human minds. The way they put together text is closer to running a story through Google translate 10 times than it is to a human using a story for inspiration.

There are real, definite benefits of using LLMs, but selling it as AI and trying to force it into everything is a gimmick.

I hate it because it’s a gigantic waste of time and resources. Big tech has poured hundreds of billions of dollars, caused double digit percentage increases in data center emissions, and fed almost the entire collective output of humanity into these models.

And what did we get for it? We got a toy that is at best mildly amusing, but isn’t really all that actually useful for anything; the output provided by generative AI is too unreliable to trust outright and needs to be reviewed and tweaked by hand, so at best you’re getting a minor productivity gain, and more likely you’re seeing a neutral or negative impact on your productivity (or producing low-quality crap faster and calling it “good enough”). At worst, it’s put a massive force multiplier in the hands of the bad actors using disinformation to tear apart modern society for their personal gain. Goldman Sachs released a report in late June where they pointed this out: if tech companies are planning on investing a trillion dollars into AI, what is the trillion dollar problem that AI is going to solve? And so far as I can tell, it seems that the answer to the question is either “it will eliminate millions of jobs and wipe out entire industries without any replacement or safety net, causing untold human suffering” or (more likely to be the case) “there is no trillion dollar problem AI can solve and the entire endeavor is pointless.”

Even ignoring the opportunity cost–the money spent could have literally solved the entire homelessness crisis, world hunger, lifted entire countries out of poverty, or otherwise funded solutions for real, intractable, pressing problems for all of humanity–even ignoring that generative AI has single-handedly erased years of progress in reducing our C02 emissions and addressing the climate crisis, even ignoring the logistical difficulty of the scale of build-out being discussed requiring a bigger improvement in our power grid than has been done basically ever, even ignoring the concerns over IP theft and everything else, fundamentally generative AI just isn’t worth the hype. It’s the crypto craze and NFT craze and metaverse craze (remember Zuckerberg burning 36 billion to make a virtual meeting space featuring avatars without legs?) all over again, except instead of only impacting the suckers who bought into the hype, this time it’s getting shoved in everybody’s face even if they want nothing to do with it.

But hey, at least it gave us “I Glued My Balls To My Butthole Again.” That totally makes the hundred billion investment worth it, right?

As someone who doesn’t hate AI, I hate a few things about how it’s happening:

• If I want to make a book, and I want to use other books for reference, I need to obtain them legally. Purchase, rent, loan… Else I’m a pirate. Multimillion companies say for them it’s fine as long as somebody posted it on the internet. Their version of annas-archive is suddenly legal and moral, while I’m harming the authors if I use it.
• They are stuffing everything with AI, which generally means internet connection and sending unknown data.
• It’s an annoying marketing gimmick. While incredible useful in some places, the insistence that it solves all the problems make it seem as a failure.

To expand on what you wrote, I’d equate the LLM output as similar to me reading a book. From here on out until I become senile, the book is part of memory. I may reference it, I may parrot some of its details that I can remember to a friend. My own conversational style and future works may even be impacted by it, perhaps even subconsciously.

In other words, it’s not as if a book enters my brain and then is completely gone once I’m finished reading it.

So I suppose then, that the question is moreso one of volume. How many works consumed are considered too many? At what point do we shift from the realm research to the one of profiteering?

There are a certain subset of people in the AI field who believe that our brains our biological forms of LLMs, and that, if we feed an electronic LLM enough data, it’ll essentially become sentient. That may be for better or worse to civilization, but I’m not one to get in the way of wonder building.

You are equating traing an LLM with a person learning, but an LLM is not a person. It is not given the same rights and privileges under the law. At best it is a computer program and you can certainly infringe copyright by writing a program.

This argument is more along the lines of what is actually being argued by AI companies in court. Style cannot be copyrighted. They argue AI is simply recreating a style.

The problem with this is that, in order to recreate a style, AI needs to be trained on that content. So if an AI starts reproducing art in the same style as a popular artist, it must have inherently been fed a whole bunch of that artist’s work. Artists claim this is a violation of copyright since they never agreed for their art to be used in that way. The AI companies argue fair use also allows use of copyrighted works for teaching or training. An art class can use a popular artist’s work as examples of how to recreate a certain style. Of course, training AI is different than training a group of students. Is it different enough that fair use doesn’t apply is the question being decided on in court.

dowlo’t