Those claiming AI training on copyrighted works is “theft” misunderstand key aspects of copyright law and AI technology. Copyright protects specific expressions of ideas, not the ideas themselves. When AI systems ingest copyrighted works, they’re extracting general patterns and concepts - the “Bob Dylan-ness” or “Hemingway-ness” - not copying specific text or images.
This process is akin to how humans learn by reading widely and absorbing styles and techniques, rather than memorizing and reproducing exact passages. The AI discards the original text, keeping only abstract representations in “vector space”. When generating new content, the AI isn’t recreating copyrighted works, but producing new expressions inspired by the concepts it’s learned.
This is fundamentally different from copying a book or song. It’s more like the long-standing artistic tradition of being influenced by others’ work. The law has always recognized that ideas themselves can’t be owned - only particular expressions of them.
Moreover, there’s precedent for this kind of use being considered “transformative” and thus fair use. The Google Books project, which scanned millions of books to create a searchable index, was ruled legal despite protests from authors and publishers. AI training is arguably even more transformative.
While it’s understandable that creators feel uneasy about this new technology, labeling it “theft” is both legally and technically inaccurate. We may need new ways to support and compensate creators in the AI age, but that doesn’t make the current use of copyrighted works for AI training illegal or unethical.
For those interested, this argument is nicely laid out by Damien Riehl in FLOSS Weekly episode 744. https://twit.tv/shows/floss-weekly/episodes/744
If they can base their business on stealing, then we can steal their AI services, right?
Here’s an experiment for you to try at home. Ask an AI model a question, copy a sentence or two of what they give back, and paste it into a search engine. The results may surprise you.
And stop comparing AI to humans but then giving AI models more freedom. If I wrote a paper I’d need to cite my sources. Where the fuck are your sources ChatGPT? Oh right, we’re not allowed to see that but you can take whatever you want from us. Sounds fair.
It’s not a breach of copyright or other IP law not to cite sources on your paper.
Getting your paper rejected for lacking sources is also not infringing in your freedom. Being forced to pay damages and delete your paper from any public space would be infringement of your freedom.
I mean, you’re not necessarily wrong. But that doesn’t change the fact that it’s still stealing, which was my point. Just because laws haven’t caught up to it yet doesn’t make it any less of a shitty thing to do.
It’s not stealing, its not even ‘piracy’ which also is not stealing.
Copyright laws need to be scaled back, to not criminalize socially accepted behavior, not expand.
The argument that these models learn in a way that’s similar to how humans do is absolutely false, and the idea that they discard their training data and produce new content is demonstrably incorrect. These models can and do regurgitate their training data, including copyrighted characters.
And these things don’t learn styles, techniques, or concepts. They effectively learn statistical averages and patterns and collage them together. I’ve gotten to the point where I can guess what model of image generator was used based on the same repeated mistakes that they make every time. Take a look at any generated image, and you won’t be able to identify where a light source is because the shadows come from all different directions. These things don’t understand the concept of a shadow or lighting, they just know that statistically lighter pixels are followed by darker pixels of the same hue and that some places have collections of lighter pixels. I recently heard about an ai that scientists had trained to identify pictures of wolves that was working with incredible accuracy. When they went in to figure out how it was identifying wolves from dogs like huskies so well, they found that it wasn’t even looking at the wolves at all. 100% of the images of wolves in its training data had snowy backgrounds, so it was simply searching for concentrations of white pixels (and therefore snow) in the image to determine whether or not a picture was of wolves or not.
Even if they learned exactly like humans do, like so fucking what, right!? Humans have to pay EXORBITANT fees for higher education in this country. Arguing that your bot gets socialized education before the people do is fucking absurd.
I am also not really getting the argument. If I as a human want to learn a subject from a book I buy it ( or I go to a library who paid for it). If it’s similar to how humans learn, it should cost equally much.
The issue is of course that it’s not at all similar to how humans learn. It needs VASTLY more data to produce something even remotely sensible. Develop AI that’s truly transformative, by making it as efficient as humans are in learning, and the cost of paying for copyright will be negligible.
If I as a human want to learn a subject from a book, I buy it
xD
That’s good.Imagine if you had blinders and earmuffs on for most of the day, and only once in a while were you allowed to interact with certain people and things. Your ability to communicate would be truncated to only what you were allowed to absorb.
Basing your argument around how the model or training system works doesn’t seem like the best way to frame your point to me. It invites a lot of mucking about in the details of how the systems do or don’t work, how humans learn, and what “learning” and “knowledge” actually are.
I’m a human as far as I know, and it’s trivial for me to regurgitate my training data. I regularly say things that are either directly references to things I’ve heard, or accidentally copy them, sometimes with errors.
Would you argue that I’m just a statistical collage of the things I’ve experienced, seen or read? My brain has as many copies of my training data in it as the AI model, namely zero, but “Captain Picard of the USS Enterprise sat down for a rousing game of chess with his friend Sherlock Holmes, and then Shakespeare came in dressed like Mickey mouse and said ‘to be or not to be, that is the question, for tis nobler in the heart’ or something”. Direct copies of someone else’s work, as well as multiple copyright infringements.
I’m also shit at drawing with perspective. It comes across like a drunk toddler trying their hand at cubism.Arguing about how the model works or the deficiencies of it to justify treating it differently just invites fixing those issues and repeating the same conversation later. What if we make one that does work how humans do in your opinion? Or it properly actually extracts the information in a way that isn’t just statistically inferred patterns, whatever the distinction there is? Does that suddenly make it different?
You don’t need to get bogged down in the muck of the technical to say that even if you conceed every technical point, we can still say that a non-sentient machine learning system can be held to different standards with regards to copyright law than a sentient person. A person gets to buy a book, read it, and then carry around that information in their head and use it however they want. Not-A-Person does not get to read a book and hold that information without consent of the author.
Arguing why it’s bad for society for machines to mechanise the production of works inspired by others is more to the point.Computers think the same way boats swim. Arguing about the difference between hands and propellers misses the point that you don’t want a shrimp boat in your swimming pool. I don’t care why they’re different, or that it technically did or didn’t violate the “free swim” policy, I care that it ruins the whole thing for the people it exists for in the first place.
I think all the AI stuff is cool, fun and interesting. I also think that letting it train on everything regardless of the creators wishes has too much opportunity to make everything garbage. Same for letting it produce content that isn’t labeled or cited.
If they can find a way to do and use the cool stuff without making things worse, they should focus on that.I’m not the above poster, but I really appreciate your argument. I think many people overcorrect in their minds about whether or not these models learn the way we do, and they miss the fact that they do behave very similarly to parts of our own systems. I’ve generally found that that overcorrection leads to bad arguments about copyright violation and ethical concerns.
However, your point is very interesting (and it is thankfully independent of that overcorrection). We’ve never had to worry about nonhuman personhood in any amount of seriousness in the past, so it’s strangely not obvious despite how obvious it should be: it’s okay to treat real people as special, even in the face of the arguable personhood of a sufficiently advanced machine. One good reason the machine can be treated differently is because we made it for us, like everything else we make.
I think there still is one related but dangling ethical question. What about machines that are made for us but we decide for whatever reason that they are equivalent in sentience and consciousness to humans?
A human has rights and can take what they’ve learned and make works inspired by it for money, or for someone else to make money through them. They are well within their rights to do so. A machine that we’ve decided is equivalent in sentience to a human, though… can that nonhuman person go take what it’s learned and make works inspired by it so that another person can make money through them?
If they SHOULDN’T be allowed to do that, then it’s notable that this scenario is only separated from what we have now by a gap in technology.
If they SHOULD be allowed to do that (which we could make a good argument for, since we’ve agreed that it is a sentient being) then the technology gap is again notable.
I don’t think the size of the technology gap actually matters here, logically; I think you can hand-wave it away pretty easily and apply it to our current situation rather than a future one. My guess, though, is that the size of the gap is of intuitive importance to anyone thinking about it (I’m no different) and most people would answer one way or the other depending on how big they perceive the technology gap to be.
Another good question is why AIs do not mindlessly regurgitate source material. The reason is that they have access to so much copyrighted material. If they were trained on only one book, they would constantly regurgitate material from that one book. Because it’s trained on many (millions) books, it’s able to get creative. So the argument of OpenAI really boils down to: “we are not breaking copyright law, because we have used sufficient copyrighted material to avoid directly infringing on copyright”.
Eeeh, I still think diving into the weeds of the technical is the wrong way to approach it. Their argument is that training isn’t copyright violation, not that sufficient training dilutes the violation.
Even if trained only on one source, it’s quite unlikely that it would generate copyright infringing output. It would be vastly less intelligible, likely to the point of overtly garbled words and sentences lacking much in the way of grammar.
If what they’re doing is technically an infringement or how it works is entirely aside from a discussion on if it should be infringement or permitted.
Arguing why it’s bad for society for machines to mechanise the production of works inspired by others is more to the point.
I agree, but the fact that shills for this technology are also wrong about it is at least interesting.
Rhetorically speaking, I don’t know if that’s useless.
I don’t care why they’re different, or that it technically did or didn’t violate the “free swim” policy,
I do like this point a lot.
If they can find a way to do and use the cool stuff without making things worse, they should focus on that.
I do miss when the likes of cleverbot was just a fun novelty on the Internet.
Devil’s Advocate:
How do we know that our brains don’t work the same way?
Why would it matter that we learn differently than a program learns?
Suppose someone has a photographic memory, should it be illegal for them to consume copyrighted works?
Because we’re talking pattern recognition levels of learning. At best, they’re the equivalent of parrots mimicking human speech. They take inputs and output data based on the statistical averages from their training sets - collaging pieces of their training into what they think is the right answer. And I use the word think here loosely, as this is the exact same process that the Gaussian blur tool in Photoshop uses.
This matters in the context of the fact that these companies are trying to profit off of the output of these programs. If somebody with an eidetic memory is trying to sell pieces of works that they’ve consumed as their own - or even somebody copy-pasting bits from Clif Notes - then they should get in trouble; the same as these companies.
Given A and B, we can understand C. But an LLM will only be able to give you AB, A(b), and B(a). And they’ve even been just spitting out A and B wholesale, proving that they retain their training data and will regurgitate the entirety of copyrighted material.
I’ll train my AI on just the bee movie. Then I’m going to ask it “can you make me a movie about bees”? When it spits the whole movie, I can just watch it or sell it or whatever, it was a creation of my AI, which learned just like any human would! Of course I didn’t even pay for the original copy to train my AI, it’s for learning purposes, and learning should be a basic human right!
In the meantime I’ll introduce myself into the servers of large corporations and read their emails, codebase, teams and strategic analysis, it’s just learning!
learning should be a basic human right!
Education is a basic human right (except maybe in Usa, then it should be one there)
Yeah. A human right.
I am thrilled to see the output you get!
Look… All I have to say is… Support the Internet Archive!
(please)
Heh. Funny that this comment is uncontroversial. The Internet Archive supports Fair Use because, of course, it does.
This is from a position paper explicitly endorsed by the IA:
Based on well-established precedent, the ingestion of copyrighted works to create large language models or other AI training databases generally is a fair use.
By
- Library Copyright Alliance
- American Library Association
- Association of Research Libraries
The problem with your argument is that it is 100% possible to get ChatGPT to produce verbatim extracts of copyrighted works. This has been suppressed by OpenAI in a rather brute force kind of way, by prohibiting the prompts that have been found so far to do this (e.g. the infamous “poetry poetry poetry…” ad infinitum hack), but the possibility is still there, no matter how much they try to plaster over it. In fact there are some people, much smarter than me, who see technical similarities between compression technology and the process of training an LLM, calling it a “blurry JPEG of the Internet”… the point being, you wouldn’t allow distribution of a copyrighted book just because you compressed it in a ZIP file first.
The problem with your argument is that it is 100% possible to get ChatGPT to produce verbatim extracts of copyrighted works.
Exactly! This is the core of the argument The New York Times made against OpenAI. And I think they are right.
The examples they provided were for very widely distributed stories (i.e. present in the data set many times over). The prompts they used were not provided. How many times they had to prompt was not provided. Their results are very difficult to reproduce, if not impossible, especially on newer models.
I mean, sure, it happens. But it’s not a generalizable problem. You’re not going to get it to regurgitate your Lemmy comment, even if they’ve trained on it. You can’t just go and ask it to write Harry Potter and the goblet of fire for you. It’s not the intended purpose of this technology. I expect it’ll largely be a solved problem in 5-10 years, if not sooner.
I agree. You can’t just dismiss the problem saying it’s “just data represented in vector space” and on the other hand not be able properly censor the models and require AI safety research. If you don’t know exactly what’s going on inside, you also can’t claim that copyright is not being violated.
It honestly blows my mind that people look at a neutral network that’s even capable of recreating short works it was trained on without having access to that text during generation… and choose to focus on IP law.
Right! Like if we could honestly further enhance that feature its an incredible increase in compression tech!
The problem with your argument is that it is 100% possible to get ChatGPT to produce verbatim extracts of copyrighted works.
What method still works? I’d like to try it.
I have access to ChatGPT 4, and the latest Anthropic model.
Edit: hm… no answers but downvotes. I wonder why that is.
This would be a good point, if this is what the explicit purpose of the AI was. Which it isn’t. It can quote certain information verbatim despite not containing that data verbatim, through the process of learning, for the same reason we can.
I can ask you to quote famous lines from books all day as well. That doesn’t mean that you knowing those lines means you infringed on copyright. Now, if you were to put those to paper and sell them, you might get a cease and desist or a lawsuit. Therein lies the difference. Your goal would be explicitly to infringe on the specific expression of those words. Any human that would explicitly try to get an AI to produce infringing material… would be infringing. And unknowing infringement… well there are countless court cases where both sides think they did nothing wrong.
You don’t even need AI for that, if you followed the Infinite Monkey Theorem and just happened to stumble upon a work falling under copyright, you still could not sell it even if it was produced by a purely random process.
Another great example is the Mona Lisa. Most people know what it looks like and if they had sufficient talent could mimic it 1:1. However, there are numerous adaptations of the Mona Lisa that are not infringing (by today’s standards), because they transform the work to the point where it’s no longer the original expression, but a re-expression of the same idea. Anything less than that is pretty much completely safe infringement wise.
You’re right though that OpenAI tries to cover their ass by implementing safeguards. Which is to be expected because it’s a legal argument in court that once they became aware of situations they have to take steps to limit harm. They can indeed not prevent it completely, but it’s the effort that counts. Practically none of that kind of moderation is 100% effective. Otherwise we’d live in a pretty good world.
Y’all should really stop expecting people to buy into the analogy between human learning and machine learning i.e. “humans do it, so it’s okay if a computer does it too”. First of all there are vast differences between how humans learn and how machines “learn”, and second, it doesn’t matter anyway because there is lots of legal/moral precedent for not assigning the same rights to machines that are normally assigned to humans (for example, no intellectual property right has been granted to any synthetic media yet that I’m aware of).
That said, I agree that “the model contains a copy of the training data” is not a very good critique–a much stronger one would be to simply note all of the works with a Creative Commons “No Derivatives” license in the training data, since it is hard to argue that the model checkpoint isn’t derived from the training data.
a much stronger one would be to simply note all of the works with a Creative Commons “No Derivatives” license in the training data, since it is hard to argue that the model checkpoint isn’t derived from the training data.
Not really. First of all, creative commons strictly loosens the copyright restrictions on a work. The strongest license is actually no explicit license i.e. “All Rights Reserved.” No derivatives is already included under full, default, copyright.
Second, derivative has a pretty strict legal definition. It’s not enough to say that the derived work was created using a protected work, or even that the derived work couldn’t exist without the protected work. Some examples: create a word cloud of your favorite book, analyze the tone of news article to help you trade stocks, produce an image containing the most prominent color in every frame of a movie, or create a search index of the words found on all websites on the internet. All of that is absolutely allowed under even the strictest of copyright protections.
Statistical analysis of copyrighted materials, as in training AI, easily clears that same bar.
Equating LLMs with compression doesn’t make sense. Model sizes are larger than their training sets. if it requires “hacking” to extract text of sufficient length to break copyright, and the platform is doing everything they can to prevent it, that just makes them like every platform. I can download © material from YouTube (or wherever) all day long.
Model sizes are larger than their training sets
Excuse me, what? You think Huggingface is hosting 100’s of checkpoints each of which are multiples of their training data, which is on the order of terabytes or petabytes in disk space? I don’t know if I agree with the compression argument, myself, but for other reasons–your retort is objectively false.
Just taking GPT 3 as an example, its training set was 45 terabytes, yes. But that set was filtered and processed down to about 570 GB. GPT 3 was only actually trained on that 570 GB. The model itself is about 700 GB. Much of the generalized intelligence of an LLM comes from abstraction to other contexts.
Table 2.2 shows the final mixture of datasets that we used in training. The CommonCrawl data was downloaded from 41 shards of monthly CommonCrawl covering 2016 to 2019, constituting 45TB of compressed plaintext before filtering and 570GB after filtering, roughly equivalent to 400 billion byte-pair-encoded tokens. Language Models are Few-Shot Learners
*Did some more looking, and that model size estimate assumes 32 bit float. It’s actually 16 bit, so the model size is 350GB… technically some compression after all!
The issue isn’t that you can coax AI into giving away unaltered copyrighted books out of their trunk, the issue is that if you were to open the hood, you’d see that the entire engine is made of unaltered copyrighted books.
All those “anti hacking” measures are just there to obfuscate the fact that that the unaltered works are being in use and recallable at all times.
This is an inaccurate understanding of what’s going on. Under the hood is a neutral network with weights and biases, not a database of copyrighted work. That neutral network was trained on a HEAVILY filtered training set (as mentioned above, 45 terabytes was reduced to 570 GB for GPT3). Getting it to bug out and generate full sections of training data from its neutral network is a fun parlor trick, but you’re not going to use it to pirate a book. People do that the old fashioned way by just adding type:pdf to their common web search.
Again: nobody is complaining that you can make AI spit out their training data because AI is the only source of that training data. That is not the issue and nobody cares about AI as a delivery source of pirated material. The issue is that next to the transformed output, the not-transformed input is being in use in a commercial product.
The issue is that next to the transformed output, the not-transformed input is being in use in a commercial product.
Are you only talking about the word repetition glitch?
tweet is good, your body argument is completely wrong
deleted by creator
I absolutely would download a car.
Bullshit. AI are not human. We shouldn’t treat them as such. AI are not creative. They just regurgitate what they are trained on. We call what it does “learning”, but that doesn’t mean we should elevate what they do to be legally equal to human learning.
It’s this same kind of twisted logic that makes people think Corporations are People.
As others have said, it isn’t inspired always, sometimes it literally just copies stuff.
This feels like it was written by someone who invested their money in AI companies because they’re worried about their stocks
Sometimes I’ve noticed Google’s AI overview is a nearly word for word copy of the highest reddit result, or any result.
Lol
Generative AI does not work like this. They’re not like humans at all, it will regurgitate whatever input it receives, like how Google can’t stop Gemini from telling people to put glue in their pizza. If it really worked like that, there wouldn’t be these broad and extensive policies within tech companies about using it with company sensitive data like protection compliances. The day that a health insurance company manager says, “sure, you can feed Chat-GPT medical data” is the day I trust genAI.
I’ve just asked Gemini about cheese that slides off pizza, it didn’t recommend glue.
The last I had heard of this were articles months in saying it was still not fixed, but this doesn’t invalidate my point. It may have been retrained to respond otherwise, but it spouts garbled inputs.
It wasn’t Gemini, but the AI generated suggestions added to the top of Google search. But that AI was specifically trained to regurgitate and reference direct from websites, in an effort to minimize the amount of hallucinated answers.
Do you have a source for Search Generative Experience using a separate model? As far as I’m aware, all of Google’s AI services are powered by the Gemini LLM.
No mention of Gemini in their blog post on sge And their AI principles doc says
We acknowledge that large language models (LLMs) like those that power generative AI in Search have the potential to generate responses that seem to reflect opinions or emotions, since they have been trained on language that people use to reflect the human experience. We intentionally trained the models that power SGE to refrain from reflecting a persona. It is not designed to respond in the first person, for example, and we fine-tuned the model to provide objective, neutral responses that are corroborated with web results.
So a custom model.
Ha, ya know? I think I know some people who will just regurgitate whatever input they receive
…
:(
I feel you man lmao
“This process is akin to how humans learn… The AI discards the original text, keeping only abstract representations…”
Now I sail the high seas myself, but I don’t think Paramount Studios would buy anyone’s defence they were only pirating their movies so they can learn the general content so they can produce their own knockoff.
Yes artists learn and inspire each other, but more often than not I’d imagine they consumed that art in an ethical way.
Now I sail the high seas myself, but I don’t think Paramount Studios would buy anyone’s defence they were only pirating their movies so they can learn the general content so they can produce their own knockoff.
However, Paramount, itself, does pirate content specifically to learn its content so it can produce its own knockoff. As do all the other major studios.
No one engages in IP enforcement in good faith, or respects the IP of others if they can find benefit in circumventing it.
That’s part of the problem. None of the key stakeholders (other than the biggest stakeholder, the public) are interested in preserving the interests of the creators, artists and developers, rather are interested in boosting their own profit gains.
Which makes this not about big companies stealing from human art but from IP property of their own kin.
Yes, Generative AI very much does borrow liberally from the work of human creatives. But those artists mostly signed away their rights long ago to their publishing house masters. Since the ownership class controlled the presses, those contracts were far from fair.
Artists, today, routinely see their art stolen by their own publishing houses at length, and it’s embittering and soul-crushing. We’ve seen Hollywood accounting come into play throughout the last century. Famous actors are notoriously cheated out of residuals. (With the rise of the internet, and prior to that a few smart agents, we’ve seen a small but growing number of — usually pirate-friendly — exceptions.)
The artists were screwed long before AI ever came around.
Instead this fight is about IP-holding companies slugging it out with big computing companies, a kaiju match that is likely to leave Tokyo (that is, the rest of us, creators and consumers alike) in ruin. But we’re already in squalor, anyway.
Now I sail the high seas myself, but I don’t think Paramount Studios would buy anyone’s defence they were only pirating their movies so they can learn the general content so they can produce their own knockoff.
We don’t know exactly how they source their data (and that is definitely shady), but if I can gain access to a movie in a legal way, I don’t see why I would not be able to gather statistics from said movie, including running a speech to text model to caption it, then make statistics of how many times a few words were used, and followed by which ones. This is an oversimplified explanation of what a LLM does, but it’s the fairest I can come up, and it would be legal to do so. The models are always orders of magnitude smaller than the data they are trained on.
That said, I don’t imply that I’m happy with the state of high tech companies, the AI hype, the energy consumption, or the impact on the humble people. But I’ve put a lot of thought into this (and learning about machine learning for real), and I think this is not a ML problem, but a problem in the economic, legal and political system. AI hype is just a symptom.
Though I am not a lawyer by training, I have been involved in such debates personally and professionally for many years. This post is unfortunately misguided. Copyright law makes concessions for education and creativity, including criticism and satire, because we recognize the value of such activities for human development. Debates over the excesses of copyright in the digital age were specifically about humans finding the application of copyright to the internet and all things digital too restrictive for their educational, creative, and yes, also their entertainment needs. So any anti-copyright arguments back then were in the spirit specifically of protecting the average person and public-interest non-profit institutions, such as digital archives and libraries, from big copyright owners who would sue and lobby for total control over every file in their catalogue, sometimes in the process severely limiting human potential.
AI’s ingesting of text and other formats is “learning” in name only, a term borrowed by computer scientists to describe a purely computational process. It does not hold the same value socially or morally as the learning that humans require to function and progress individually and collectively.
AI is not a person (unless we get definitive proof of a conscious AI, or are willing to grant every implementation of a statistical model personhood). Also AI it is not vital to human development and as such one could argue does not need special protections or special treatment to flourish. AI is a product, even more clearly so when it is proprietary and sold as a service.
Unlike past debates over copyright, this is not about protecting the little guy or organizations with a social mission from big corporate interests. It is the opposite. It is about big corporate interests turning human knowledge and creativity into a product they can then use to sell services to - and often to replace in their jobs - the very humans whose content they have ingested.
See, the tables are now turned and it is time to realize that copyright law, for all its faults, has never been only or primarily about protecting large copyright holders. It is also about protecting your average Joe from unauthorized uses of their work. More specifically uses that may cause damage, to the copyright owner or society at large. While a very imperfect mechanism, it is there for a reason, and its application need not be the end of AI. There’s a mechanism for individual copyright owners to grant rights to specific uses: it’s called licensing and should be mandatory in my view for the development of proprietary LLMs at least.
TL;DR: AI is not human, it is a product, one that may augment some tasks productively, but is also often aimed at replacing humans in their jobs - this makes all the difference in how we should balance rights and protections in law.
AI are people, my friend. /s
But, really, I think people should be able to run algorithms on whatever data they want. It’s whether the output is sufficiently different or “transformative” that matters (and other laws like using people’s likeness). Otherwise, I think the laws will get complex and nonsensical once you start adding special cases for “AI.” And I’d bet if new laws are written, they’d be written by lobbiests to further erode the threat of competition (from free software, for instance).
What do you think “ingesting” means if not learning?
Bear in mind that training AI does not involve copying content into its database, so copyright is not an issue. AI is simply predicting the next token /word based on statistics.
You can train AI in a book and it will give you information from the book - information is not copyrightable. You can read a book a talk about its contents on TV - not illegal if you’re a human, should it be illegal if you’re a machine?
There may be moral issues on training on someone’s hard gathered knowledge, but there is no legislature against it. Reading books and using that knowledge to provide information is legal. If you try to outlaw Automating this process by computers, there will be side effects such as search engines will no longer be able to index data.
This process is akin to how humans learn…
I’m so fucking sick of people saying that. We have no fucking clue how humans LEARN. Aka gather understanding aka how cognition works or what it truly is. On the contrary we can deduce that it probably isn’t very close to human memory/learning/cognition/sentience (any other buzzword that are stands-ins for things we don’t understand yet), considering human memory is extremely lossy and tends to infer its own bias, as opposed to LLMs that do neither and religiously follow patters to their own fault.
It’s quite literally a text prediction machine that started its life as a translator (and still does amazingly at that task), it just happens to turn out that general human language is a very powerful tool all on its own.
I could go on and on as I usually do on lemmy about AI, but your argument is literally “Neural network is theoretically like the nervous system, therefore human”, I have no faith in getting through to you people.
Even worse is, in order to further humanize machine learning systems, they often give them human-like names.
