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
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.
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.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 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 ( or I go to a library who paid for it). If it’s similar to how humans learn, it should cost equally much.
You’re on Lemmy where people casually says “piracy is morally the right thing to do”, so I’m not sure this argument works on this platform.
I know my way around the Jolly Roger myself. At the same time using copyrighted materials in a commercial setting (as OpenAI does) shouldn’t be free.
Only if they are selling the output. I see it as more they are selling access to the service on a server farm, since running ChatGPT is not cheap.
The usual cycle of tech-bro capitalism would put them currently on the early acquire market saturation stage. So it’s unlikely that they are currently charging what they will when they are established and have displaced lots of necessary occupations.
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.
That seems more like an argument for free higher education rather than restricting what corpuses a deep learning model can train on
Porque no los dos? Allowing major corps to put even more downward pressure on workers doesn’t help anyone but the rich. LLMs aren’t going to save the world or become sentient.
Tomato, tomato…
If ChatGPT was free I might see their point but it’s not so no. If you’re making money from someone’s work you should pay them.
You’re making an indie movie on your iPhone with friends. You sell one ticket. You now owe: Apple, Joseph Nicéphore Niépce’s estate (inventor of the camera), every cinematographer who first devised the type of shots you’re using, the writers since the beginning of time that created the types of story elements in the script, the mathematicians and scientists that developed lense technology, the car manufacturers that aided your ability to transport you to the set, the guy who’s YouTube tutorial you watched to figure out lighting, etc, etc, etc.
Your black and white framing appears to provide a clear ethical framework until you dig a millimeter into it. The reality is that society only exists because of the work that all of the individuals within it produce. Things like copyright are an adapter to our capitalistic economy to ensure people’s work that can be copied, are protected enough that they have the opportunity to make money off of it. It exists so somebody else can’t immediately turn around and sell the same book someone else wrote, or just change a few words and do as such. This protection was meant to last 15 to 20 years. Then enter the public domain for anyone to copy and rewrite as they please.
Current copyright is an utter bastardization of its intended use. Massive corporations are trying to act like they’re fighting for the little guy to own their IP forever. But they buy up all that IP for pennies compared to how they turn around and commoditize it. Then they own all of what society produces in perpetuity. They can sit on their dragon hoards and laugh as they gobble up any new creation that strays too close. And people wonder why everything is a sequel of a sequel of a sequel owned by massive corporations.
I was trying to keep it simple.
I would have paid them by purchasing the iphone and whatever software I used. I paid for the car that transported me. I would have paid for my education. People can also give their work away for free if they want, or be compensated by ads as in the case of Youtube or FOSS.
Current copyright is an utter bastardization of its intended use. Massive corporations are trying to act like they’re fighting for the little guy to own their IP forever. But they buy up all that IP for pennies compared to how they turn around and commoditize it. Then they own all of what society produces in perpetuity. They can sit on their dragon hoards and laugh as they gobble up any new creation that strays too close. And people wonder why everything is a sequel of a sequel of a sequel owned by massive corporations.
What do you think ChatGPT is trying to do? It’s already being used to churn out shitloads of garbage content. They’re not making things better.
By that rationalization, OpenAI is paying their Internet bill, and for a copy of Dune, so they’re free to use any content they acquired to make their product better. Your original argument wasn’t akin to, “Shouldn’t someone using an iPhone pay for one?” It was “Shouldn’t Apple get a cut of everything made with the iPhone?”
You could make the argument that people use ChatGPT to churn out garbage content, sure, but a lot of cinephiles would accuse your proverbial indie movie of being the same and blame Apple for creating the iPhone and enabling it. If you want to make that argument, go ahead. But don’t pretend it has anything to do with people getting paid fairly for what they made.
ChatGPT is enabling people to make more things, easier, to get paid. And people, as always, are relying on everything that was created before them as a basis for their work. Same as when I go to school and the professor shows me lots of different works to learn from. The thousands of students in that class didn’t pay for any of that stuff. The professor distilled it and presented it and I paid him to do it.
The problem is that they didn’t pay for the content they’ve acquired and they’re selling it to others. The creators are not being compensated and may not want to participate in AI development at all. If the creators agree to it then fine but most do not. Just look at what’s happening with art. People are scraping all of an artists work to create AI pictures in their style and impersonate them. That’s not okay.
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.
This process is akin to how humans learn by reading widely and absorbing styles and techniques, rather than memorizing and reproducing exact passages.
Like fuck it is. An LLM “learns” by memorization and by breaking down training data into their component tokens, then calculating the weight between these tokens. This allows it to produce an output that resembles (but may or may not perfectly replicate) its training dataset, but produces no actual understanding or meaning–in other words, there’s no actual intelligence, just really, really fancy fuzzy math.
Meanwhile, a human learns by memorizing training data, but also by parsing the underlying meaning and breaking it down into the underlying concepts, and then by applying and testing those concepts, and mastering them through practice and repetition. Where an LLM would learn “2+2 = 4” by ingesting tens or hundreds of thousands of instances of the string “2+2 = 4” and calculating a strong relationship between the tokens “2+2,” “=,” and “4,” a human child would learn 2+2 = 4 by being given two apple slices, putting them down to another pair of apple slices, and counting the total number of apple slices to see that they now have 4 slices. (And then being given a treat of delicious apple slices.)
Similarly, a human learns to draw by starting with basic shapes, then moving on to anatomy, studying light and shadow, shading, and color theory, all the while applying each new concept to their work, and developing muscle memory to allow them to more easily draw the lines and shapes that they combine to form a whole picture. A human may learn off other peoples’ drawings during the process, but at most they may process a few thousand images. Meanwhile, an LLM learns to “draw” by ingesting millions of images–without obtaining the permission of the person or organization that created those images–and then breaking those images down to their component tokens, and calculating weights between those tokens. There’s about as much similarity between how an LLM “learns” compared to human learning as there is between my cat and my refrigerator.
And YET FUCKING AGAIN, here’s the fucking Google Books argument. To repeat: Google Books used a minimal portion of the copyrighted works, and was not building a service to compete with book publishers. Generative AI is using the ENTIRE COPYRIGHTED WORK for its training set, and is building a service TO DIRECTLY COMPETE WITH THE ORGANIZATIONS WHOSE WORKS THEY ARE USING. They have zero fucking relevance to one another as far as claims of fair use. I am sick and fucking tired of hearing about Google Books.
EDIT: I want to make another point: I’ve commissioned artists for work multiple times, featuring characters that I designed myself. And pretty much every time I have, the art they make for me comes with multiple restrictions: for example, they grant me a license to post it on my own art gallery, and they grant me permission to use portions of the art for non-commercial uses (e.g. cropping a portion out to use as a profile pic or avatar). But they all explicitly forbid me from using the work I commissioned for commercial purposes–in other words, I cannot slap the art I commissioned on a T-shirt and sell it at a convention, or make a mug out of it. If I did so, that artist would be well within their rights to sue the crap out of me, and artists charge several times as much to grant a license for commercial use.
In other words, there is already well-established precedent that even if something is publicly available on the Internet and free to download, there are acceptable and unacceptable use cases, and it’s broadly accepted that using other peoples’ work for commercial use without compensating them is not permitted, even if I directly paid someone to create that work myself.
I recently visited a museum and i really loved it. Getting up close to an image and seeing none of the fuzziness, no AI “shimmer” on photos and every stroke made sense (as in you could see that an arm moved a brush and you could see the path it took etc.). Hands made sense. And while tryptichons were not exactly precise when it comes to the anatomy of humans, no humans had anything smeared etc.
If you put a gazillion monkeys on a typewriter they can write Shakespeare.
If you train one ai for a ton of epochs it can write Shakespeare.
All pure mathematical coincidence.
If you put a gazillion monkeys on a typewriter they can write Shakespeare.
This is a mathematical curiosity borne out of pure randomness. An LLM trained on a dataset to generate similar content is quite the opposite of randomness.
Like fuck it is. An LLM “learns” by memorization and by breaking down training data into their component tokens, then calculating the weight between these tokens.
But this is, at a very basic fundamental level, how biological brains learn. It’s not the whole story, but it is a part of it.
there’s no actual intelligence, just really, really fancy fuzzy math.
You mean sapience or consciousness. Or you could say “human-level intelligence”. But LLM’s by definition have real “actual” intelligence, just not a lot of it.
Edit for the lowest common denominator: I’m suggesting a more accurate way of phrasing the sentence, such as “there’s no actual sapience” or “there’s no actual consciousness”. /end-edit
an LLM would learn “2+2 = 4” by ingesting tens or hundreds of thousands of instances of the string “2+2 = 4” and calculating a strong relationship between the tokens “2+2,” “=,” and “4,”
This isn’t true. At all. There are math specific benchmarks made by experts to specifically test the problem solving and domain specific capabilities of LLM’s. And you can be sure they aren’t “what’s 2 + 2?”
I’m not here to make any claims about the ethics or legality of the training. All I’m commenting on is the science behind LLM’s.
Generative AI is not ‘influenced’ by other people’s work the way humans are. A human musician might spend years covering songs they like and copying or emulating the style, until they find their own style, which may or may not be a blend of their influences, but crucially, they will usually add something. AI does not do that. The idea that AI functions the same as human artists, by absorbing influences and producing their own result, is not only fundamentally false, it is dangerously misleading. To portray it as ‘not unethical’ is even more misleading.
Production AI is highly tuned by training data selection and human feedback. Every model has its own style that many people helped tune. In the open model world there are thousands of different models targeting various styles. Waifu Diffusion and GPT-4chan, for example.
Sure, training data selection impacts the output. If you feed an AI nothing but anime, the images it produces will look like anime. If all it knows is K-pop, then the music it puts out will sound like K-pop. Tweaking a computational process through selective input is not the same as a human being actively absorbing stimuli and forming their own, unique response.
AI doesn’t have an innate taste or feeling for what it likes. It won’t walk into a second hand CD store, browse the boxes, find something that’s intriguing and check it out. It won’t go for a walk and think “I want to take a photo of that tree there in the open field”. It won’t see or hear a piece of art and think “I’d like to be learn how to paint/write/play an instrument like that”. And it will never make art for the sake of making art, for the pure enjoyment that is the process of creating something, irrespective of who wants to see or hear the result. All it is designed to do is regurgitate an intersection of what it knows that best suits the parameters of a given request (aka prompt). Actively learning, experimenting, practicing techniques, trying to emulate specific techniques of someone else - making art for the sake of making art - is a key component to humans learning from others and being influenced by others.
So the process of human learning and influencing, and the selective feeding of data to an AI to ‘tune’ its output are entirely different things that cannot and should not be compared.
I thought the larger point was that they’re using plenty of sources that do not lie in the public domain. Like if I download a textbook to read for a class instead of buying it - I could be proscecuted for stealing. And they’ve downloaded and read millions of books without paying for them.
And they've downloaded and read millions of books without paying for them.Do you have a source on that?
Most AI models used Books3 as part of their dataset which is a collection of pirated books. Here are a few articles talking about it:
https://www.theverge.com/2024/8/20/24224450/anthropic-copyright-lawsuit-pirated-books-ai
https://www.theatlantic.com/technology/archive/2023/08/books3-ai-meta-llama-pirated-books/675063/
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.
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.
tweet is good, your body argument is completely wrong
Kids pay for books, openAI should also pay for the material access used for training.
OpenAI like other AI companies keep their data sources confidential. But there are services and commercial databases for books that people understand are commonly used in the AI industry.
OpenAI like other AI companies keep their data sources confidential.
“We trained on absolutely everything, but we won’t tell them that because it will get us in a lot of trouble”
Considering that original works are discarded, it’s strange how effective they’re at plagiarizing them
In the same way that a person can learn the material and also use that knowledge to potentially plagiarize it, though. It’s no different in that sense. What is different is the speed of learning and both the speed and capacity of recall. However, it doesn’t change the fundamental truths of OP’s explanation.
Also, when you’re talking specifically about music, you’re talking about a very limited subset of note combinations that will sound pleasing to human ears. Additionally, even human composers commonly struggle to not simply accidentally reproduce others’ work, which is partly why the music industry is filled with constant copyright litigation.
Yep, its definitely not possible that nice small businesses like universal and sony would sue without an actual case in order to try and crush competitors with costs.
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.
Ok, ignore this specific company and technology.
In the abstract, if you wanted to make artificial intelligence, how would you do it without using the training data that we humans use to train our own intelligence?
We learn by reading copyrighted material. Do we pay for it? Sometimes. Sometimes a teacher read it a while ago and then just regurgitated basically the same copyrighted information back to us in a slightly changed form.
And that’s all paid for. Think how much just the average high school graduate has has invested in them, ai companies want all that, but for free
It’s not though.
A huge amount of what you learn, someone else paid for, then they taught that knowledge to the next person, and so on. By the time you learned it, it had effectively been pirated and copied by human brains several times before it got to you.
Literally anything you learned from a Reddit comment or a Stack Overflow post for instance.
If only there was a profession that exchanges knowledge for money. Some one who “teaches.” I wonder who would pay them
We learn by reading copyrighted material.
We are human beings. The comparison is false on it’s face because what you all are calling AI isn’t in any conceivable way comparable to the complexity and versatility of a human mind, yet you continue to spit this lie out, over and over again, trying to play it up like it’s Data from Star Trek.
This model isn’t “learning” anything in any way that is even remotely like how humans learn. You are deliberately simplifying the complexity of the human brain to make that comparison.
Moreover, human beings make their own choices, they aren’t actual tools.
They pointed a tool at copyrighted works and told it to copy, do some math, and regurgitate it. What the AI “does” is not relevant, what the people that programmed it told it to do with that copyrighted information is what matters.
There is no intelligence here except theirs. There is no intent here except theirs.
This model isn’t “learning” anything in any way that is even remotely like how humans learn. You are deliberately simplifying the complexity of the human brain to make that comparison.
I do think the complexity of artificial neural networks is overstated. A real neuron is a lot more complex than an artificial one, and real neurons are not simply feed forward like ANNs (which have to be because they are trained using back-propagation), but instead have their own spontaneous activity (which kinda implies that real neural networks don’t learn using stochastic gradient descent with back-propagation). But to say that there’s nothing at all comparable between the way humans learn and the way ANNs learn is wrong IMO.
If you read books such as V.S. Ramachandran and Sandra Blakeslee’s Phantoms in the Brain or Oliver Sacks’ The Man Who Mistook His Wife For a Hat you will see lots of descriptions of patients with anosognosia brought on by brain injury. These are people who, for example, are unable to see but also incapable of recognizing this inability. If you ask them to describe what they see in front of them they will make something up on the spot (in a process called confabulation) and not realize they’ve done it. They’ll tell you what they’ve made up while believing that they’re telling the truth. (Vision is just one example, anosognosia can manifest in many different cognitive domains).
It is V.S Ramachandran’s belief that there are two processes that occur in the Brain, a confabulator (or “yes man” so to speak) and an anomaly detector (or “critic”). The yes-man’s job is to offer up explanations for sensory input that fit within the existing mental model of the world, whereas the critic’s job is to advocate for changing the world-model to fit the sensory input. In patients with anosognosia something has gone wrong in the connection between the critic and the yes man in a particular cognitive domain, and as a result the yes-man is the only one doing any work. Even in a healthy brain you can see the effects of the interplay between these two processes, such as with the placebo effect and in hallucinations brought on by sensory deprivation.
I think ANNs in general and LLMs in particular are similar to the yes-man process, but lack a critic to go along with it.
What implications does that have on copyright law? I don’t know. Real neurons in a petri dish have already been trained to play games like DOOM and control the yoke of a simulated airplane. If they were trained instead to somehow draw pictures what would the legal implications of that be?
There’s a belief that laws and political systems are derived from some sort of deep philosophical insight, but I think most of the time they’re really just whatever works in practice. So, what I’m trying to say is that we can just agree that what OpenAI does is bad and should be illegal without having to come up with a moral imperative that forces us to ban it.
We are human beings. The comparison is false on it’s face because what you all are calling AI isn’t in any conceivable way comparable to the complexity and versatility of a human mind, yet you continue to spit this lie out, over and over again, trying to play it up like it’s Data from Star Trek.
If you fundamentally do not think that artificial intelligences can be created, the onus is on yo uto explain why it’s impossible to replicate the circuitry of our brains. Everything in science we’ve seen this far has shown that we are merely physical beings that can be recreated physically.
Otherwise, I asked you to examine a thought experiment where you are trying to build an artificial intelligence, not necessarily an LLM.
This model isn’t “learning” anything in any way that is even remotely like how humans learn. You are deliberately simplifying the complexity of the human brain to make that comparison.
Or you are over complicating yourself to seem more important and special. Definitely no way that most people would be biased towards that, is there?
Moreover, human beings make their own choices, they aren’t actual tools.
Oh please do go ahead and show us your proof that free will exists! Thank god you finally solved that one! I heard people were really stressing about it for a while!
They pointed a tool at copyrighted works and told it to copy, do some math, and regurgitate it. What the AI “does” is not relevant, what the people that programmed it told it to do with that copyrighted information is what matters.
“I don’t know how this works but it’s math and that scares me so I’ll minimize it!”
If we have an AI that’s equivalent to humanity in capability of learning and creative output/transformation, it would be immoral to just use it as a tool. At least that’s how I see it.
I think that’s a huge risk, but we’ve only ever seen a single, very specific type of intelligence, our own / that of animals that are pretty closely related to us.
Movies like Ex Machina and Her do a good job of pointing out that there is nothing that inherently means that an AI will be anything like us, even if they can appear that way or pass at tasks.
It’s entirely possible that we could develop an AI that was so specifically trained that it would provide the best script editing notes but be incapable of anything else for instance, including self reflection or feeling loss.
The things is, they can have scads of free stuff that is not copyrighted. But they are greedy and want copyrighted stuff, too
We all should. Copyright is fucking horseshit.
It costs literally nothing to make a digital copy of something. There is ZERO reason to restrict access to things.
You sound like someone who has not tried to make an artistic creation for profit.
You sound like someone unwilling to think about a better system.
Better system for WHOM? Tech-bros that want to steal my content as their own?
I’m a writer, performing artist, designer, and illustrator. I have thought about copyright quite a bit. I have released some of my stuff into the public domain, as well as the Creative Commons. If you want to use my work, you may - according to the licenses that I provide.
I also think copyright law is way out of whack. It should go back to - at most - life of author. This “life of author plus 95 years” is ridiculous. I lament that so much great work is being lost or forgotten because of the oppressive copyright laws - especially in the area of computer software.
But tech-bros that want my work to train their LLMs - they can fuck right off. There are legal thresholds that constitute “fair use” - Is it used for an academic purpose? Is it used for a non-profit use? Is the portion that is being used a small part or the whole thing? LLM software fail all of these tests.
They can slurp up the entirety of Wikipedia, and they do. But they are not satisfied with the free stuff. But they want my artistic creations, too, without asking. And they want to sell something based on my work, making money off of my work, without asking.
Better system for WHOM? Tech-bros that want to steal my content as their own?
A better system for EVERYONE. One where we all have access to all creative works, rather than spending billions on engineers nad lawyers to create walled gardens and DRM and artificial scarcity. What if literally all the money we spent on all of that instead went to artist royalties?
But tech-bros that want my work to train their LLMs - they can fuck right off. There are legal thresholds that constitute “fair use” - Is it used for an academic purpose? Is it used for a non-profit use? Is the portion that is being used a small part or the whole thing? LLM software fail all of these tests.
No. It doesn’t.
They can literally pass all of those tests.
You are confusing OpenAI keeping their LLM closed source and charging access to it, with LLMs in general. The open source models that Microsoft and Meta publish for instance, pass literally all of the criteria you just stated.
Making a copy is free. Making the original is not. I don’t expect a professional photographer to hand out their work for free because making copies of it costs nothing. You’re not paying for the copy, you’re paying for the money and effort needed to create the original.
Making a copy is free. Making the original is not.
Yes, exactly. Do you see how that is different from the world of physical objects and energy? That is not the case for a physical object. Even once you design something and build a factory to produce it, the first item off the line takes the same amount of resources as the last one.
Capitalism is based on the idea that things are scarce. If I have something, you can’t have it, and if you want it, then I have to give up my thing, so we end up trading. Information does not work that way. We can freely copy a piece of information as much as we want. Which is why monopolies and capitalism are a bad system of rewarding creators. They inherently cause us to impose scarcity where there is no need for it, because in capitalism things that are abundant do not have value. Capitalism fundamentally fails to function when there is abundance of resources, which is why copyright was a dumb system for the digital age. Rather than recognize that we now live in an age of information abundance, we spend billions of dollars trying to impose artificial scarcity.
Studied AI at uni. I’m also a cyber security professional. AI can be hacked or tricked into exposing training data. Therefore your claim about it disposing of the training material is totally wrong.
Ask your search engine of choice what happened when Gippity was asked to print the word “book” indefinitely. Answer: it printed training material after printing the word book a couple hundred times.
Also my main tutor in uni was a neuroscientist. Dude straight up told us that the current AI was only capable of accurately modelling something as complex as a dragon fly. For larger organisms it is nowhere near an accurate recreation of a brain. There are complexities in our brain chemistry that simply aren’t accounted for in a statistical inference model and definitely not in the current gpt models.
That knowledge is out of date and out of touch. While it’s possible to expose small bits of training data, that’s akin to someone being able to recall a portion of the memory of the scene they saw. However, those exercises essentially took what sometimes equates to weeks or months of interrogation method knowledge gained over time employed by people looking to target specific types of responses. Think of it like a skilled police interrogator tricking a toddler out of one of their toys by threatening them or offering them something until it worked. Nowadays, that’s getting far more difficult to do and they’re spending a lot more time and expertise to do it.
Also, consider how complex a dragonfly is and how young this technology is. Very little in tech has ever progressed that fast. Give it five more years and come back to laugh at how naive your comment will seem.
Dammit, so my comment to the other person was a mix of a reply to this one and the last one… not having a good day for language processing, ironically.
Specifically on the dragonfly thing, I don’t think I’ll believe myself naive for writing that post or this one. Dragonflies arent very complex and only really have a few behaviours and inputs. We can accurately predict how they will fly. I brought up the dragonfly to mention the limitations of the current tech and concepts. Given the worlds computing power and research investment, the best we can do is a dragonfly for intelligence.
To be fair, Scientists don’t entirely understand neurons and ML designed neuron-data structures behave similarly to very early ideas of what brains do but its based on concepts from the 1950s. There are different segments of the brain which process different things and we sort of think we know what they all do but most of the studies AI are based on is honestly outdated neuroscience. OpenAI seem to think if they stuff enough data into this language processor it will become sentient and want an exemption from copyright law so they can be profitable rather than actually improving the tech concepts and designs.
Newer neuroscience research suggest neurons perform differently based on the brain chemicals present, they don’t all always fire at every (or even most) input and they usually present a train of thought, I.e. thoughts literally move around in the brains areas. This is all very different to current ML implementations and is frankly a good enough reason to suggest the tech has a lot of room to develop. I like the field of research and its interesting to watch it develop but they can honestly fuck off telling people they need free access to the world’s content.
TL;DR dragonflies aren’t that complex and the tech has way more room to grow. However, they have to generate revenue to keep going so they’re selling a large inference machine that relies on all of humanities content to generate the wrong answer to 2+2.
Your first point is misguided and incorrect. If you’ve ever learned something by ‘cramming’, a.k.a. just repeating ingesting material until you remember it completely. You don’t need the book in front of you anymore to write the material down verbatim in a test. You still discarded your training material despite you knowing the exact contents. If this was all the AI could do it would indeed be an infringement machine. But you said it yourself, you need to trick the AI to do this. It’s not made to do this, but certain sentences are indeed almost certain to show up with the right conditioning. Which is indeed something anyone using an AI should be aware of, and avoid that kind of conditioning. (Which in practice often just means, don’t ask the AI to make something infringing)
I think you’re anthropomorphising the tech tbh. It’s not a person or an animal, it’s a machine and cramming doesn’t work in the idea of neural networks. They’re a mathematical calculation over a vast multidimensional matrix, effectively solving a polynomial of an unimaginable order. So “cramming” as you put it doesn’t work because by definition an LLM cannot forget information because once it’s applied the calculations, it is in there forever. That information is supposed to be blended together. Overfitting is the closest thing to what you’re describing, which would be inputting similar information (training data) and performing the similar calculations throughout the network, and it would therefore exhibit poor performance should it be asked do anything different to the training.
What I’m arguing over here is language rather than a system so let’s do that and note the flaws. If we’re being intellectually honest we can agree that a flaw like reproducing large portions of a work doesn’t represent true learning and shows a reliance on the training data, i.e. it cant learn unless it has seen similar data before and certain inputs provide a chance it just parrots back the training data.
In the example (repeat book over and over), it has statistically inferred that those are all the correct words to repeat in that order based on the prompt. This isn’t akin to anything human, people can’t repeat pages of text verbatim like this and no toddler can be tricked into repeating a random page from a random book as you say. The data is there, it’s encoded and referenced when the probability is high enough. As another commenter said, language itself is a powerful tool of rules and stipulations that provide guidelines for the machine, but it isn’t crafting its own sentences, it’s using everyone else’s.
Also, calling it “tricking the AI” isn’t really intellectually honest either, as in “it was tricked into exposing it still has the data encoded”. We can state it isn’t preferred or intended behaviour (an exploit of the system) but the system, under certain conditions, exhibits reuse of the training data and the ability to replicate it almost exactly (plagiarism). Therefore it is factually wrong to state that it doesn’t keep the training data in a usable format - which was my original point. This isn’t “cramming”, this is encoding and reusing data that was not created by the machine or the programmer, this is other people’s work that it is reproducing as it’s own. It does this constantly, from reusing StackOverflow code and comments to copying tutorials on how to do things. I was showing a case where it won’t even modify the wording, but it reproduces articles and programs in their structure and their format. This isn’t originality, creativity or anything that it is marketed as. It is storing, encoding and copying information to reproduce in a slightly different format.
EDITS: Sorry for all the edits. I mildly changed what I said and added some extra points so it was a little more intelligible and didn’t make the reader go “WTF is this guy on about”. Not doing well in the written department today so this was largely gobbledegook before but hopefully it is a little clearer what I am saying.
Those claiming AI training on copyrighted works is “theft” misunderstand key aspects of copyright law and AI technology.
Or maybe they’re not talking about copyright law. They’re talking about basic concepts. Maybe copyright law needs to be brought into the 21st century?
Disagree. These companies are exploiting an unfair power dynamic they created that people can’t say no to, to make an ungodly amount of money for themselves without compensating people whose data they took without telling them. They are not creating a cool creative project that collaboratively comments on or remixes what other people have made, they are seeking to gobble up and render irrelevant everything that they can, for short term greed. That’s not the scenario these laws were made for. AI hurts people who have already been exploited and industries that have already been decimated. Copyright laws were not written with this kind of thing in mind. There are potentially cool and ethical uses for AI models, but open ai and google are just greed machines.
Edited * THRICE because spelling. oof.