For anyone who holds both of these opinions, why do you think it's ok to train diffusion models on copyrighted work, but not co-pilot on GPL code?
1. Training an AI model on code (so far) makes it regurgitate code line-for-line (with comments!). This is like "learning to code" by just cut and pasting working code from other codebases, you have to follow the license. The AI doesn't "understand the algorithm" at all (or it hasn't been told "don't export the input you fool"). Obviously a bog-simple AI could make all licenses moot by dumping out what it input, and the courts wouldn't permit that.
2. Training an AI model on illustrations so far produces "style parodies" which may look similar to an untrained eye (the artist here is annoyed because she'd not art like that, even though to us it looks similar enough). Drawing a picture that looks like Mickey Mouse is a trademark violation, but tracing a picture of the Mouse is both a trademark and a copyright violation.
The first violates some pretty clear legal concepts; the second is closer to violating moral concepts but those are more flexible - if an artist spends years learning to paint in the style of Michelangelo is that immoral?
Art is in the eye of the beholder. If the output looks correct as per what you're looking for, it is correct. There's no additional layer of "Is it saying what I meant it to say" that is relevant to anyone who isn't an art critic.
That creator had a vision in mind that's unique to them because of their experiences, and I think it's wrong to say that this image can be quantified as a location in a abstract feature space.
So to say "there is no additional layer [to judge goodness] that is relevant to [most people]" assumes there is an algorithmic measure of "goodness" that can be applied to art, which is an assumption you need to make to believe that there's any similarities with AI generated art and human generated other than "they look kinda similar".
We hoomans love to think we're special, but quantum tubules etc besides, we really are just biological computers running a program developed over our evolutionary/personal histories.
Until then we are special and we're just pretending these specialized algorithms are replicating the things we don't even understand. Anthropomorphising the algorithms by saying they "learn" and "feel" and "experience" is us as humans trying to draw parallels to ourselves as we find our understanding inadequate to explain what's actually going on.
Artists are granted copyright for their work by default per the Berne Convention. These copyrighted works are then used without consent of the original author for these models.
Additionally, the argument that you can't copyright a style is playing fast and loose with most things that are proprietary, semantically.
Either a style is copyrightable or it's not. If it's not, then I can't see any argument that you can't use it yourself or by proxy.
You do not actively train your brain in remotely similar methods, and you, as an individual, are accountable to social pressures. An issue these companies are trying to avoid with ethically questionable scraping/training methods and research loop holes.
Additionally, many artists aren't purely learning from others to perfectly emulate them, and it's quickly spotted if they are, generally. Lessons learned do not implicitly mean you perfectly emulate that lesson. At each stage of learning, you bias things through your own filter.
Overall, the idea that these two things are comparable feels grotesque and reductionist, and feel quite similar to the "Well I wasn't going to buy it anyway" arguments we've been throwing around for decades to try to justify piracy of other materials.
At the end of the day, an argument that "style can't be copyrighted" is ignoring a lot of aspects of it's definition, including the means, and can be extrapolated into an argument that nothing proprietary should be allowed to exist...
I agree with you there but the alternative - that they’re not comparable - I find equally grotesque and full of convenient suppositions rooted in romanticism of “the artist”. We’re in uncharted territory with AI finally lapping at the heels of creative professionals and any analogy is going to fall apart.
This feels like something that we should leave to the courts on a case by case basis until there’s enough precedent for a legal test. The question at the end of the day should be about harm and whether an AI algorithm was used as run-around of a specific person’s copyright
I was actually just sitting in a AI Town Hall hosted by the Concept Art Association which had 2 US Copyright Lawyers who work at the USCO present, and their along similar lines, currently.
Basically, like you specified, legal precedent needs to be built up on a case by case basis, and harm can pretty readily be demonstrated, at least anecdotally, especially as copies are made during training of copyrighted work.
Unfortunately, historically, artists do not generally enjoy the same legal representation or resources that unionized industries with deeper pockets enjoy. It's probably one of the reasons Stability.Ai are being so considerate with their musical variant.
It would have been great if artists were asked before any of this. I could see this going in such a different direction if people were merely asked...
Call me a training-set vegan, any model made from opt-in and public domain images I'd use in a heartbeat.
no.
they are the same as asking a person to say a number between 1 and 6, then asking the same question to a dice and concluding that men and dice work the same.
> identify important features and replicate those features in new context
untrue
if you think that that's what people do, obviously you can conclude that AI and humans are similar.
But people don't identify features, people first of all learn how to replicate - mechanically - the strokes, using the same tools as the original artists until they are able to do it, most of the time people fail and reiterate the process until they find something they are actually very good at and only after that the good ones develop their own style.
based either on some artistic style or some artistic meaning.
But the first difference we learn here is that humans can fail to replicate something and still become renown artists.
An AI cannot do that.
Not on its own.
For example, many probably already know, but Michelangelo was a sculptor.
He was proficient as a painter too, but painting wasn't his strongest skill.
So artists, first of all, are creators, not mere replicators, in many different forms, they are not good at everything in the same way, but their knowledge percolates in other fields related to theirs: if you need to make preparatory drawings for a sculpture, you need to be good at drawing and probably painting (lights, shadows, mood, expressions, are all fundamental for a good sculpture)
Secondly, the features artists derive from other art pieces are not the technical ones, those needed to make an exact replica of the original, but those that make it special.
For example, in the case of Michelangelo, the Pietà has some features that an AI would surely miss.
First of all the way he shaped the marble that was unheard of, it doesn't mean much if you don't contextualize the opera and immerse it in the historical period it was created.
An AI could think that Michelangelo and Canova were contemporary, while they were separated by 3 centuries, which make a lot of difference in practice and in spirit.
But more importantly, Michelangelo's Pietà is out of proportion, he could not make the two figures in the correct scale, proving that even a genius like he was could not easily create a faithful reproduction of two adults one in the lap of the other, with the tools of the 16th century.
The Virgin Mary is very, very young, which was at odds with her role as a grieving mother and, the most important of them all, the Christ figure is not suffering, because Michelangelo did not want to depict death.
An AI would assume that those are all features of Michelangelo's way of sculpting, but in reality it's the result of a mix of complexity of the opera, time when it was created, quality and technology of the tools used and the artist intentions, which makes the opera unique and, ultimately, irreproducible.
If you use an AI to reproduce Michelangelo, everybody would notice, because it's literally something a complete noob or someone with a very bad taste would do.
So to not say the difference, you should copy the works of lesser known artists, making it even more unethical.
>But if I train my own neural network inside my skull using some artist's style, that's ok?
This post and others uses a lot of flowery language to point out that we train artificial neural networks and real neural networks in different ways. OK, great. I don't think anyone is saying that's not true. What I am saying is that it's irrelevant.
If I am an exceptional imitator of the style of Jackson Pollock and i make a bunch of paintings that are very much in that style but clearly not his work I'm not going to be sued. My work will be labeled, rightfully so, as derivative but I have the right to sell it because it's not the same thing. Is that somehow more acceptable because I can only do it slowly and at a low volume? What if I start an institute whose sole purpose is training others to make Jackson Pollock-like paintings? What if I skip the people and make a machine that makes a similar quality of paintings with a similarly derivative style? Is that somehow immoral / illegal? Why?
There's a whole lot of hand-wavey logic going on in this thread about context and opera and special human magic that only humans can possibly do and that somehow makes it immoral for an AI to do it. I am yet to see a simple, succinct argument of why that is the case.
Maybe I was too aulic.
The point is: you don't train "your artificial intelligence", because you're not an artificial intelligence, you train your whole self, that is a system, a very complex system.
So you can think in terms of "I don't like death, I don't want to display death"
You can learn how to paint using your feet, if you have no hands.
You can be blind and still paint and enjoy it!
An AI cannot think of "not displaying death" in someone's face, not even if you command it to do it, because it doesn't mean anything, out of context.
> Jackson Pollock
Jackson Pollock is the classic example to explain the concept: of course you can make the same paintings Jackson Pollock made.
But you'll never be Jackson Pollock, because that trick works only the first time, if you are a pioneer.
If you create something that look like Pollock, everybody will tell you "oh... it reminds of Jackson Pollock..." and no one will say "HOW ORIGINAL!"
Like no one can ever be Armstrong again, land on the Moon and say "A small step for man (etc etc)"
Pollock happened, you can of course copy Pollock, but nobody copies Pollock not because it's hard, but because it's cheap AF
So it's the premise that is wrong: you are not training, you are learning.
They are very different concepts.
AIs (if we wanna define the "intelligent") are currently just very complex copy machines trained on copyrighted material.
Remove the copyrighted material and their output would be much less than unimpressive (probably a mix of very boring and very ugly).
Remove the ability to watch copyrighted material from people and some of them will come up with an original piece of art.
It happened many times throughout history.
sorry for writing in capital letters, maybe that way they will stand out enough for you to focus on what's important.
WE ARE NOT AIS
an AI is the equivalent of a photocopier or sampling a song to make a new song, there are limits on how much you can copy/use copyrighted material, that do not apply TO YOUR HEARS, because you hearing a song does not AUTOMATICALLY AND MECHANICALLY translates into a new song. You still need to LEARN HOWTO MAKE MUSIC, which is not about the features of the song, it's about BEING ABLE TO COMPOSE MUSIC.
which is not what these AI do, they cannot compose music, they can mix and match features taken from copyrighted material into new (usually not that new, nor good) material.
If we remove the copyrighted material from you, you can still make music.
You could be deaf and still compose music.
If we remove copyrighted material from AIs they cannot compose shit.
Because the equivalent of a deaf person for an AI that create music CANNOT EXIST - for obvious reasons.
So AIs DEPEND ON copyrighted material, they don't just learn from it, they WOULD BE USELESS WITHOUT IT.
and morally the difference is that THEY DO NOT PAY for the privilege of accessing the source material.
They take, without giving anything back to the artists.
They do not even ask for the permission.
is it clearer now?
I think you are right to be skeptical and cautious in the face of claims of AI progress. From as far back as the days of the Mechanical Turk, many such claims have turned out to be puffery at best, or outright fraud at worst.
From time to time, however, inevitably, some claims have actually proven to be true, and represent an actual breakthrough. More and more, I'm beginning to think that the current situation is one of those instances of a true breakthrough occurring.
To the surface point: I do not think the current proliferation of generative AI/ML models are unoriginal per se. If you ask them for something unoriginal, you will naturally(?) get something unoriginal. However, if you ask them for something original, you may indeed get something original.
I wonder in what way you mean that? In any case the latest stable diffusion model file itself is 3.5 GB, which is several of orders of magnitude less than the training dataset.
It probably doesn't contain much literal copyrighted data.
I would take the position that it's self evident that if you take the 'training data' away from humans they also can't compose music. If you take a baby, put it in a concrete box for 30 years (or until whatever you consider substantial biological maturity), and then put it in front of a piano it's not going to create Chopin. It might figure out how to make some dings and boops and will quickly lose interest.
Humans also need a huge amount of training data and we, at best, make minor modifications to these ideas to place them into new context to create new things. The difference between average and world class is vanishingly small in terms of the actual basic insight in some domain. Take the greatest composers that have ever lived and rewind them and perform our concrete box experiment and you'll have a wild animal, barely capable of recognizing cause and effect between hitting the piano and the noise it makes.
That world class composer, when exposed to modern society, consumed an awful lot of media for 'free' just by existing. Should they be charged for it? Did they commit a copyright infraction? Why or why not?
How well the network inside your skull can manipulate your limbs to reproduce good-quality work in some artist's style?
Our current framework for thinking about "fair use", "copyright", "trademark" and similar were thought about into existence during an era when the options for "network inside the skull" were to laboriously learn a skill to draw or learn how to use a machine like printing press/photocopier that produces exact copies.
Availability of a machine that automates previously hand-made things much more cheaply or is much more powerful often requires rethinking those concepts.
If I copy a book putting ink on paper letter by letter manually, that's ok, think of those monks in monasteries who do that all the time. And Mr Gutenberg's machine just makes that ink-on-paper process more efficient...
An experienced artist can probably do this in a couple weeks, depending on how complex the style is.
>If I copy a book putting ink on paper letter by letter manually, that's ok, think of those monks in monasteries who do that all the time.
According to copyright, no, that's not okay. Copyright does not care about the method of reproduction, it just distinguishes between authorized and unauthorized reproduction. A copyist copying a book by hand without authorization is just as illegal as doing it with a photocopier. Likewise, if you decide to copy a music CD using a hex editor and lots of patience, at the end of the process you will end up with a perfectly illegal copy of the original CD.
So the question stands. Why is studying artwork with eyeballs and a brain and reproducing the style acceptable, but doing the same with software isn't?
This has been true since copyright existed, Braque couldn’t copyright cubism — Picasso saw what he was doing and basically copied the style with nothing to be done aside from not letting him into the studio.
You do not need an author's consent to dissect or analyze their work or to train a ML model on it, they do not have an exclusive right on that. You do not need an authors consent to make a different work in their style, they do not have an exclusive right on that.
Generally speaking, this supposition skirts around the concept of monetizing from the work of others, and seems at odds with what the Berne Convention seems to stipulate in that context, and arguably seems in violation of points 2 and 3 of the three-step test.
That's to say nothing regarding the various interpretations on data scraping laws that preclude monetizing outputs.
I don't feel it's that black and white, personally...
The Berne three-step test specifies when reproduction is permitted, however, any use that does not involve reproducing the work is not restricted, and monetization does not matter. It's relevant for data-scraping laws because you are making copies of the protected work.
Switching the rules on people during the game is what annoys/angers people, and is basically what these AIs have done (because they've introduced a new player at low effort).
I'd say that artist has gained a lot by studying Michaelangelo, including an appreciation for what Michaelangelo himself accomplished and insights into how to paint as well or better, and maybe even how to teach that to other people. I don't think we get those benefits from AI models doing that (at least not yet!)
On the other hand, I would say that traditional mediums (especially large format paintings) are relatively safe from AI generation/automation - for now.
Why do you think that? I think large format paintings might be in just as much danger.
There’s a large industry of talented artists in China, Vietnam, etc who copy famous artworks by hand for very low prices. They’re easily accessible online: you upload an image and provide some stylistic details and the artist does the hard work of turning the image into brush strokes. It’s not “automated” but I’ve already ordered one 4’x2’ AI generated painting in acrylic relief for less than the cost of a 1’x1’ from a local community gallery. I put in quite a bit of work inpainting the image to get what I want but it would have been completely impossible to get what I want even six months ago.
I’ve only ever purchased half a dozen artworks in my life and they were all under a few hundred bucks but with this new tech, it just doesn’t make sense to buy an artists’ original work unless it’s for charity. The AI can do the creative work the way I want and there are plenty of artists who are excellent at the mechanical translation (which still requires a lot of creativity, mind)
Want someone to paint the ceiling in your mega mansion? Sure.
But now the creative bit can be done by you - or your 8 year old - if you like.
It looked great and all I had to do was pay for supplies, which was still less than the cost of the framing.
Using the "brain in a jar" thought experiment, you're making the assumption that the iterative denoising process is equivalent to the way the "brain in the jar" would generate art. Since the question is whether or not the processes are equivalent, it seems nonsensical to have to assume their equivalence for your argument.
Copilot can produce code verbatim, but it doesn't unless you specifically set up a situation to test it. It requires things like "include the exact text of a comment that exists in training data" or "prefix your C functions the same way as the training data does".
In everyday use, my experience has been that Copilot draws extensively from files that I've opened in my codebase. If I give Copilot a function body to fill in in a class I've already written, it will use my internal APIs (which aren't even hosted on GitHub) correctly as long as there are 1-2 examples in the file and I'm using a consistent naming convention. This isn't copypasta, it really does have a clear understanding of the semantics of my code.
This is why I'm not in favor of penalizing Microsoft and GitHub for creating Copilot. I think there needs to be some regulation on how it is used to make sure that people aren't treating it as a repository of copypasta, but the AI itself is pretty clearly capable of producing non-infringing work, and indeed that seems to be the norm.
We most likely don't need new laws, because infringement is infringement and how you made the infringing work is irrelevant. Accidental infringement is already illegal in the US.
My own personal opinion is that the AI generated code (or pictures in the case of the article) should be under a new category of literary works, such that it does not receive copyright protection, but also does not violate existing copyright.
Humans tweaking the image is immaterial to this determination - if the human tweaked it so that it no longer seem to violate copyright, then that said panel would also make the same determination.
Of course you have no way to prove whether any image was or was not generated by AI so welcome to a new scam for law firms to aggressively sue artists claiming they suspect AI was used in their works.
An example of DP in image generation (using GANs): https://par.nsf.gov/servlets/purl/10283631
It does not matter what a service can or cannot do. We do not regulate based on ability, but on action.
The service has an obligation to the license holders of the training data to not violate the license. The mechanism for which the license is violated is irrelevant. The only thing that matters is the code ended up somewhere it shouldn’t, and the service is the actor in the chain of responsibility that dropped the ball.
The prompting of the service is irrelevant. If I ask you to reproduce a block of GPL code in my codebase and you do it, you violated the license. It does not matter that I primed you or lead you to that outcome. What matters is the legally protected code is somewhere it shouldn’t be.
This isn't accurate. If I reproduce GPL code in your codebase, that's perfectly acceptable as long as you obey the terms of the GPL when you go to distribute your code. In this hypothetical, my act of copying isn't restricted under the GPL license, it's your subsequent act of distribution that triggers the viral terms of the GPL.
The big question that is still untested in court is whether Copilot itself constitutes a derivative work of its training data. If Copilot is derivative then Microsoft is infringing already. If Copilot is transformative then it is the responsibility of downstream consumers to ensure that they comply with the license of any code that may get reproduced verbatim. This question has not been ruled on, and it's not clear which direction a court will go.
Microsoft has a license to distribute the code used to train Copilot, and isn't distributing the Copilot model anyway, so it doesn't matter whether the model itself infringes copyright.
Whereas that same question probably does matter for Stable Diffusion.
Tangent to that is the "who gets sued and needs to fix it when a code audit is done?"
Ultimately, the question is then "who is responsible for verifying that the code submitted to production isn't copying from sources that have incompatible licensing?"
and how is that different from a student learning how to code off stackoverflow (or anywhere else for that matter), then reproducing some snippets/learnt code structure, in their employment?
Or a random employee copies some art work that is then published ( https://arstechnica.com/tech-policy/2018/07/post-office-owes... ). You will note all the people that didn't get in trouble there - neither the photographer who created the image, nor Getty in making it available, nor the random employee who used it without checking its provenance.
In all of these cases, it is (or would be) the organization that published the copyrighted work without doing the appropriate diligence on checking what it is, if it would be useable, and how it should be licensed.
> The Post Office says it has new procedures in place to make sure that it doesn't make a mistake like this again.
... which is what companies who make use of AI models for generating content (be it art or code) should be doing to ensure that they're not accidentally infringing on existing copyrighted works.
If Microsoft is so confident in Pilot doing sufficient remixing then why not train it on their own internal code? And why put the burden of IP vetting on clients who have less info than Pilot?
https://docs.github.com/en/site-policy/github-terms/github-t...
As in " including improving the Service over time...parse it into a search index or otherwise analyze it on our servers" is the provision that grants them the ability to train CoPilot.
(also, in case you're wondering what happens if you upload someone else's code: "If you're posting anything you did not create yourself or do not own the rights to, you agree that you are responsible for any Content you post; that you will only submit Content that you have the right to post; and that you will fully comply with any third party licenses relating to Content you post.")
It'll have to be tested in court, but likely nobody actually gives a shit.
Which is why that second provision is there to shift liability to you. You MUST have the ability to grant GitHub that license to any code you upload. If you don't, and MS is sued for infringing upon the GPL, presumably Microsoft can name you as the fraudster that claimed to be able to grant them a license to code that ended up in copilot.
Whether we agree with it or not, intellectual property laws have historically been regulated by ability as well as action. Hence why blank multimedia formats would often have additional taxes in some jurisdictions just in case someone chose to record copyrighted content onto them. And why graphics cards used to include an MPEG royalty in their consumer cost, regardless of whether that user planned to watch DVDs on their computer.
Not saying I agree with this principle. Just that there is already a long history of precedence in this area.
Like a lot of politics, ultimately it just comes down to who has the bigger lobbying budget.
Original: https://static-cdn.jtvnw.net/ttv-boxart/460636_IGDB-272x380....
Copies: https://lexica.art/?q=bloodborne
this is not true, smh. See Oracle vs. Google.
I suspect the courts will treat AI more like a "black box" - they won't care how or why your black box can perfectly play Metallica, only that it does.
Which are which has been lost to time in some cases, and the art world is filled with dissertations on it.
This is a deceptive comparison. A human learns a style and adds their own ideas. They ideas are affected by their mood, schooling, beliefs and coffee this morning.
AI has only the training dataset. If you trained AI on 1000 copyrighted pictures, AI cant add its own ideas, it can only remix pixels from stolen work of other artists.
This is basically like money laundering, if you melted down stolen gold coins, and minted new coins, and then claimes this gold is yours because you made them.
The difference between human an AI model is that AI focuses on seeing completely but human has to do non-art stuff as well.
The decisions leading up to the human art is the entire human experience leading up to the creation of the art(and possible context afterwards), which we as people tend to put value on.
The "decisions" leading up to the AI art are a series of iterative denoising steps that attempt to recover an image from noisy data by estimating how much the noise differs from the "good looking" image.
So for your "vespa fenders as a helmet" drawing, I don't think that constitutes an algorithm being "creative". If a human were to make the same picture we could rationalize that they're being creative because we can imagine a path where their human experiences led to a new idea. Since the algorithm was only ever made to denoise an image based on its abstract feature-space representation I don't see any way we could rationalize that it created a new idea. The algorithm never "thought" it should use a fender as a helmet, it only found that the best way to denoise the current image to the one described in feature-space was to remove pixels that resulted in the image.
Don't humanize algorithms. They're applied statistics, not a sum of human experiences.
When an idea "pops" into your head, how was that made? Couldn't it also be a similar denoising of patterns in synaptic potentials? We know from many experiments that what something feels like can be quite different from what it actually is.
Is it only that we don't know the exact brain process that makes humans special? And once we inevitably do figure it out, does all human art become meaningless too? I think we need to learn to disconnect process from result and just enjoy the result, wherever it came from.
It is clear to anyone that understands this tech that it is not simply "memorizing" or "copying" the training data, even if they can be coaxed into doing this for certain inputs (in the current iteration of the tools).
Ultimately, I think the problem of reproducing certain popular works or code snippets will be solved. One interesting direction here are the tools of information theory and differential privacy. e.g) proving that certain training inputs cannot be recovered from the weights, or that there is a threshold of how much information can be gleaned from a single input.
It is easy to imagine (because its nearly there already) a future version of StableDiffusion (or CoPilot) which provably compresses all training data beyond any possibility of recovery, and yet still produces extremely convincing results which disrupt the creative professions of art and programming.
Until we get to that point, it feels like the only consistent and sensible place to apply regulations is with the model end user. When I use CoPilot, I accept the small (honestly overblown) risk that _maybe_ I won't have the license to use some small snippet of code it spits out. But I'm happy to wear that responsibility, because the boost to productivity is so great that I dread a return to pre-CoPilot world. That is, a world where everyone keeps reinventing the same solution to simple problems over and over and over again.
As for actually using the model... personally I still find that unacceptable. Even if the risks are low, we have lots of works in the model, so the risk can still add up.
The idea you have in your head of training without regurgitation is likely not possible. The underlying technology treats the training set as gospel: the system is trained to regurgitate first, and generalization is a happy accident. Likewise, we can't look into a model to check and see what it's memorized, nor can we trace back an output to a particular training example. Which has ethical implications with the way that AI companies crawl the web to get training data; such models almost certainly hold someone's personal information and there's no way to get it out of there aside from curating your training set to begin with.
When you have to model 100TB of images with 4gb of weights there is no way this is possible without learning some kind of patterns and regularity that generalise outside the training set. Most generated items will be novel, and most training items will not be reproducible.
It doesn’t seem radical to suggest that the copying issue will continue to recede as we get better models.
And there are areas of research specifically concerned with _provably_ showing that you cannot identify what items a model was trained was.
Lots of reasons to be optimistic in my view.
> the original images themselves aren’t stored in the Stable Diffusion model, with over 100 terabytes of images used to create a tiny 4 GB model
Is jpeg compression transformative then? Should a compressed image of something not be copyrightable because “it doesn’t store” the “real image”? How about compressed video? Where do we draw the line?
I wager 100 TB => 4GB is different from JPEG compression and more similar to what happens in our brains. "Neural compression" so to speak
Good thing this is already the case! https://en.wikipedia.org/wiki/Berne_Convention
> The Berne Convention formally mandated several aspects of modern copyright law; it introduced the concept that a copyright exists the moment a work is "fixed", rather than requiring registration. It also enforces a requirement that countries recognize copyrights held by the citizens of all other parties to the convention.
I was making the point that stable diffusion != JPEG compression
With Stable Diffusion it's storing the style, but can't reproduce any single input image-- there aren't enough bits [0]. (except by luck, but that's really true for any storage).
Rank all generated images by similarity to the training data (etc.) and you can see what's stored.
The Shannon-Hartley theorem isnt relevant. A 4GB zip of 100TB text data can exactly reproduce the initial 100TB for some distributions of that initial dataset.
But if you reproduced an image whose style matches another copyrighted image (e.g., blah in the style of starry night), then how does that new image (which didn't exist before) violate existing copyright? You cannot copyright a style.
The NN containing information which _could_ be used to reconstruct an exact image doesn't itself constitute copyright violation - because the right to use information for training NN is not an exclusive right that the original holder of the training set has.
So either a new law has to come into existence, vis a vis the right to use copyrighted works to train a NN, or the current copyright laws should apply (which implies that NN generated images which are not "exact" copies of existing works don't violate copyright).
And if you can come up with a model that can reproduce images exactly without first getting trained on them, it wouldn't be a derived work, either.
but why is the set of numbers in a matrix considered derived?
I can trivially derive the number pi.
My original point is that just because something contains the information does not imply that it violates copyright.
You're correct that merely containing the information would not violate copyright - it's all about how that information was produced.
("If a human artist...")
A NN has not learnt to paint: it doesn't coordinate its sensory-motor system with its environment through play, it hasn't developed any taste, it does not discern the aesthetic good from the bad, it has no judgement, and so on ad infinitum.
A NN is just a kNN with an extra compression step. The way all gradient based "learners" work is to compute distances to pregiven training data. In the case of kNN that data is used exactly, in NN its compressed.
There is no intelligence here, there is no learning: it's a trick. It turns out that interpolating a point between prior examples can often look novel and often fool a human observer.
This is largely due to how incredibly tolerant to flaws we are in the cases where NNs are used to perform this trick. We go to great lengths to impart intention, fix communicative flaws, etc. and this is exploited by "AI" to make simple crap seem great by having the observer fill-in the details, perceptually. I see it as a kind of proto-schizophrenia that all people have which usually works if we're dealing with a human, but on everything else produces religions.
In any case, a NN is just a case of a kNN -- which is capable of fooling people exactly the same way, and clearly violates copyright and is a case of theft of intellectual work to make a product you can sell. Adding compression seems irrelevant.
Stable Diffusion is certainly capable of differentiating good from bad. That's why you can tell it to draw good or draw bad.
Not that this point is relevant to my comment. "Play", "taste" and "judgment" can be just as deterministic as a sequence of large matrix operations interspersed with nonlinear layers.
Interpolation is forced in the case of NNs, it's a training condition.
And the "kNN interpretation" isnt an interpration, kNNs define what "ideal learning" is in the case of statistical learning, and hence show, it doesnt count as actual learning.
In actual learning we're not interested in whether you can solve prespecified problems but how well you cope when you can't. This is, by definition, not a problem which can be formulated in statistical learning terms and the particular "learning" algorithm here is irrelevant.
In other words accuracy isnt a test of learning. Accuracy is a "non-modal condition" in being fit to a history that actually took place. Learning "in the usual sense" is strictly a modal, "what if" phenomenon, and is assessed by the quality of failure under adverse conditions, not of success.
If one gave any AI/ML system in existence adverse conditions, posed relevant "what ifs" and observed the results, they'd be exposed as the catastrophe they are. None survive any even basic test of "coping well" in these cases.
This is why all breathless AI public relations, ie., academic papers published in the last decade, do not perform any such tests.
you can create compressed copy of a file containing 100TB of the letter "A" in much less than 4GB
there could be enough bits in there to reproduce some of the inputs.
This is what we have courts and legislation for. I expect there's existing legislation here about what constitutes a different work versus an exact copy but it may need some updates for AI.
The loudest voices complaining they were directly hurt by Copilot's training are open source maintainers. These are exactly the kind of people who we love to root for on here. They're the little guy involved in a labor of love, giving away their work for free (with terms).
On the other hand, the highest-profile victims of Stable Diffusion and DALL-E are Getty Images and company. They're in most respects the opposite of open source maintainers: big companies worth millions of dollars for doing comparatively little work (primarily distributing photos other people took).
Because in the case of images the victim is most prominently faceless corporations, I think our collective bias towards "information wants to be free" shows through more clearly when regarding DALL-E than it does with Copilot.
It's puzzling to me you acknowledge people are taking these photos and getting a cut from their use through the marketplace, yet still see Getty as the biggest victim.
If Getty could AI-generate their whole portofolio and keep 100% of the sales to themselves they'd do it in heartbeat (and I'd expect them to partially go that route). The most screwed people are the photographs ("the little guy" in your comparison)
Copyleft is a license that weakens copyright (and thus inherently good :)), so using machine learning to weaken copyleft by allowing you to copyright "clones" of copyleft code is bad.
If I try to generalize here, the problem in both cases is only if you produce copyrighted works, especially if you trained on copyleft works. If instead both models would stipulate that all produced works are copyleft I would be much more fine with it (and I feel it would respect the license of the copyleft works it was trained on, even if that may be legally shaky).
Bonus points if you have any actionable scheme beyond waving your hands and talking vaguely about "basic income".
Keep in mind that the life of a professional artist is currently very perilous, anyone working freelance is constantly battling against the social media giants' desire to keep everyone scrolling their site forever. Words like "patreon" and "commission" and links off-site to places an artist can exchange their works for money are poison to The Algorithm and will be hidden.
And also if I am reading this right, you have absolutely no problem with an image generator that's been trained on copyrighted work producing work that's either copyrighted or copylefted? You are utterly fine with disregarding the copyrights of the original artist and/or whoever they may have assigned the copyright to as part of their contract?
Why isn't it a concern for any other automation?
How do we progress, exactly, if we randomly decide that nothing can disrupt any of the current ways of earning income?
What about, IDK, coal miners?
> And also if I am reading this right, you have absolutely no problem with an image generator that's been trained on copyrighted work producing work that's either copyrighted or copylefted? You are utterly fine with disregarding the copyrights of the original artist and/or whoever they may have assigned the copyright to as part of their contract?
Copyright maximalism is bad. It also doesn't make any sense. Someone learning to reproduce your capabilities by looking at your stuff isn't violating copyright. If we allowed copyright to somehow mean that someone's skills can't be reproduced...
If an economic loss is currently occurring, that means that, if copyright is abolished, an economic gain will accrue. Where that economic gain is captured is therefore the core focus. What we want to occur is for society and the author to share in this increased economic gain, what we don't want is for monopolistic rent-seekers to grab all of this value for themselves.
I am not yet sure of this, but I think a land value tax would accomplish this goal. One would then discover some method to find the weight for each artist or piece of software through a market means that might only be an approximation and take from the land value tax revenue and distribute it to artists and other creators. I think a decent way to find this value is through a revenue neutral(or slightly negative) opt-in sortition process in which, when you go to use an artists work, you are entered into an auction wherein the 50% who bid more than the median value get to use the work at the median price, and the 50% who bid less do not get to use the work, but receive their bid in cash. This is surely not the full system, but it is just me working out how we can move forward from such an unjust system like copyright.
[0]: https://en.wikipedia.org/wiki/Paul_Samuelson
[1]: https://courses.cit.cornell.edu/econ335/out/lighthouse.pdf - page 359, first paragraph
> How do you propose to keep artists able to pay their bills and live a decent life if you're completely cool with training AIs on them?
1. Software being able to imitate artists doesn't mean people will stop wanting art from other people.
2. If it does, I would say that it's unfortunate, but it's the world we live in. Nobody has a guarantee on being able to make a living doing any conceivable profession whatsoever, and artists are no different. I would very much like to make a living from working on my personal projects, but people don't seem to want to take me up on my offer.
Why do you hold that opinion? There's a few very clear benefits to creating artificial scarcity, mostly around incentivizing creation (and sharing!) of innovations.
If we can't create artificial scarcity around ideas, then ideas are in a sense less monetizable than, e.g., creating a piece of furniture. But this is just an accident of the way the world works - I can physically prevent you from taking a chair that I made, but I can't prevent you from taking an idea I had. Why does it make sense that the world works this way? Isn't it a whole lot better to encourage innovation, vs. encouraging more people to make physical objects, just because those are inherently scarce?
(The other side is that innovations also have the great property that copying them isn't depriving anyone else of use of the original idea, but that's a side issue to the encouraging innovation one, IMO.)
I think it is self evident why creating artificial scarcity is immoral, but the point you are trying to make is that from a utilitarian standpoint you think it is preferable to behave in a way that is immoral in the micro scale since it will create greater good in the macro scale. If you agree, then I don't think there's any need to justify my belief here :)
That said, I also think that it is unclear that copyright is a net positive increase to creation and sharing of innovations. The current state of monetization is not inspiring since the actual creators usually are not well compensated and money tends to stay with large corporations that are essentially just "right holders". There's also many factors that actively stifle innovation and creativity.
Patents are the most well known example, but being unable to borrow chord progressions or characters or storylines from other works is also stifling (you can't exactly release your "edanm's cut of Spiderman Homecoming" publicly, nor can you create your own sequel or alternate interpertation of the story). Quite a few fan games or fan remakes have also met their demise at the hands of aggressive copyright enforcement.
My own suspicion is that if the current models of creation will be disrupted by copyright abolition, we'll just end up seeing that a lot of the money that was spent on them will move to other avenues of funding like Patreon style or Kickstarter style funding for works. We may even see some new models created. I'd also expect that it will actually shift the balance away from large corporations (whose primary value is having lots of money that allows them to hoard rights) to smaller creators which will now have more direct funding available to them.
I also think an interesting case to look at is Video Games, where the concensus is that a game's mechanics aren't copyrightable, and so whenever a new interesting Indie game comes out on steam, there is a rash of other cool Indie games with their own takes and remixes of the same concept, like the glut of Roguelike deckbuilders after Slay the Spire or the current glut of "Vampire Survivors"-likes that have their own interesting takes on the core idea. Eventually when there's enough buzz around such ideas they can even penetrate the AAA sphere (where adding "Roguelike" elements has started to appear slightly). It is also quite common to see games that are in Early Access for a very very long time, essentially letting the community fund the future creation and expansion of the game.
> If we can't create artificial scarcity around ideas, then ideas are in a sense less monetizable than, e.g., creating a piece of furniture. But this is just an accident of the way the world works - I can physically prevent you from taking a chair that I made, but I can't prevent you from taking an idea I had. Why does it make sense that the world works this way? Isn't it a whole lot better to encourage innovation, vs. encouraging more people to make physical objects, just because those are inherently scarce?
So as I said, I'm not sure it really will make ideas less monetizable (or that if it will, that it will do so significantly). Even today I can get any video game I want for free (illegally, but that effectively doesn't matter since no one will persecute me for it), and yet I still buy video games. In fact, a strong reason for why I buy video games today is because as a child I had a friend that had easy access to pirated CDs and I'd play a lot of games at their house, fueling my passion for them.
And relatedly, it is no accident that "software is eating the world", it is exactly because it is so easy to share, the low marginal costs make it easy to have a strong worldwide impact without too much real world effort :)
I'll start with the end, I don't think the "immoral on the micro scale" idea makes much sense. Like, you can say it's "bad" or "annoying" on the micro scale, but if it is good for society as a whole to create artificial scarcity, it just isn't immoral for society to provide mechanisms to create it.
I also don't think it's self-evident that it's "immoral" on the small scale (though not sure what that means, since artificial scarcity is kind of a society-level mechanism.)
That said, maybe I'm just bumping on your use of the word immoral and we're not really disagreeing.
> That said, I also think that it is unclear that copyright is a net positive increase to creation and sharing of innovations. The current state of monetization is not inspiring since the actual creators usually are not well compensated and money tends to stay with large corporations that are essentially just "right holders". There's also many factors that actively stifle innovation and creativity.
This has been a talking point of people against copyright for a long time (I've been having these discussions for at least 20 years, personally).
But I think that you're basically wrong. It's pretty easy to see that you're wrong too - just look at the state of news, the state of music, etc. In most cases, artists today make far less money than they made before the rise of pirated alternatives. Patreon/other models/etc have helped some, but nowhere near where things were before.
In fact, you talk about the current state of compensation being bad, but I think that it would make more sense to listen to actual artists about whether or not copyright helps them or not. I've listened to a bunch, and most of them couldn't come close to doing what they love without copyright.
Also, personally, I'm a software dev. Most of what I do on a day-to-day basis is create IP. I'm fairly happy that someone can't just come along and repurpose everything I built, for free. Otherwise, I'm fairly sure I'd be out of a job.
(I'm fairly sure that without copyright/IP, most software we use wouldn't exist either.)
(See my other comment though, I am not sold on any of this being right).
you paid for it and enjoyed it in the way the artist intended.
> I am training my brain on copyrighted work.
too bad your brain alone is useless.
You need good hands to replicate much of the copyrighted works you "trained" your brain on.
> No artist has developed their style in a vacuum.
some absolutely did, indeed.
Just look at the school of film and animation that artists in USSR developed while separated from the rest of the World.
They are unique and completely different from what the west was used to (Disney)
So they were developed in a complete vacuum including traditional things like theater, poetry and story telling?
complete vacuum is a silly argument, we owe dinosaurs the oil we used to build up our modern societies, would you say that AI would be impossible without dinosaurs?
Do we owe the big bang a debt of gratitude?
Moreover, just like Czechoslovakia, Poland did not develop its animation style in a vacuum. They had strong artistic connections with many other European countries, especially France.
> They are unique and completely different from what the west was used to (Disney)
Not sure if you are an American... but Europe has a very old and very rich animation tradition. Growing up in Europe I was only vaguely familiar with Disney. The vast majority of animation I watched as a child was European.
> Not sure if you are an American... but Europe has a very old and very rich animation tradition
No, I am Italian.
I grew up with these kind of animations. [1] [2]
Nonetheless, the "animation studio" of the West, the one who won Oscars and was published virtually everywhere in the western block, was Disney
I think one of the differences is that people are seeing non-trivial amounts of copyrighted code being output by AI models.
If a 262,144 pixel image has a few 2x2 squares copied directly, you can't tell.
If a 300 line source file has 20 lines copied directly from a copyrighted source, well, that is more blatant.
That highly depends on the lines of code.
One of my (now abandoned) open source react components essentially does some smarter-than-it-probably-should state management in just a handful of LOCs. At least a few hundred people found the clever solution I came up with useful enough to integrate into their own projects.
I've seen talented a graphics programmer hand optimize routines to gain significant speed boosts, speed boosts that helped save non-trivial amounts of system resources.
And where do you draw the line? That same gfx programmer optimized maybe a dozen functions, each less than 20 lines, but all quite independent of each other. The sum total of his work gave us a huge performance boost over everyone else in the field at the time.
And of course you also have super terse languages like APL, where non-trivial algorithms can easily be implemented in 20 LOC.
But let's move to another medium, the written word, also one of the less controversial aspects of copyright (ignoring the USA's penchant for indefinite extension of copyright)
Start with poems, plenty of artistically significant poems that come in under 20 lines, deserving of copyright for sure.
https://tinhouse.com/miracles-by-lucy-corin/
There is a short story, around 22 lines.
The problem is, it is complicated, which is why these are the types of things that get litigated all the time.
Heck as a profession we cannot even agree on what a line of code is. A LOC in Java is, IMHO, worse less than a LOC in JavaScript, and if you jump to embedded C, wow that is super terse, unless you count the thousands of lines of #defines describing pinouts and such, but domain knowledge is needed to know that those aren't "real" lines of code.
Quoting US copyright law (but the same principle is global) "In no case does copyright protection for an original work of authorship extend to any idea, procedure, process, system, method of operation, concept, principle, or discovery, regardless of the form in which it is described, explained, illustrated, or embodied in such work." - when a work combines an idea and its expression, copyright protects only the expression but not the idea itself, no matter how clever or valuable it is. Copyright does not prohibit others to freely copy the method/process/system/etc expressed in the copyrighted work.
Also, there is a general rule of thumb (established in law and precedent) in copyrightability that functionally required aspects can not be copyrighted - in essence, if you create a new, optimized routine that's superior to everything else, then only the "arbitrary" free, creative parts of that code are copyrightable, but you absolutely can't get an exclusive right to that algorithm or technique. If the way you wrote it is the only way to write it, that can't be protected; and if not, others must be able to write stuff that's functionally the same (i.e. gets all the same performance boosts) and varies only in the functionally irrelevant parts. That's one of the reasons, for example, for recipe sites having all that fluff - because you can't get copyright protection on the functional part of the recipe, or some technique in another domain like architecture or computer science. Perhaps you can get a patent on that, but copyright is not applicable for that goal.
So, going back to your examples:
People reimplementing that "smarter-than-it-probably-should state management in just a handful of LOCs" is absolutely permitted by copyright law. If the same state management can be written in many different ways, then copying your code would be a violation and they would have to reimplement the same idea in different words, but copyright law definitely allows them to copy and reuse your idea without your permission, it doesn't protect the idea, only its specific expression.
Hand-optimized graphics routines may fall into the area where there is only one possible expression for that idea which implements the same method with the same efficiency. If that happens to be the case, the routine is not eligible for copyright protection at all - you can't get a monopoly on a particular effective technique or method using copyright law; patent law covers the cases in which that can or can't be done.
For APL implementations of algorithms - again, the key principle is that copyright definitely allows others to implement the same algorithm. If an obvious reimplementation of the same algorithm results in the same terse APL code, then that's simply evidence that this particular APL code is solely the "idea" (unprotectable), not "creative expression" which would be eligible for copyright protection.
There's something quantitative in code that you don't get in drawings, drawings the unique quality is purely qualitative, so it is hard to demonstrate what exactly it was that was ripped off. When you find your exact words being returned by a code helper AI it's hard to pretend that it's not directly and plainly just copy pasting code snippets.
If you want to debate expanding IP law - that's a different discussion and one I would be rather sceptical about. I'd prefer the that IP law in general was rolled back - not forward.
It would imply that you cannot offend open source licenses by doing something as simple as recreating a codebase in another language, thus eliminating the exact matches.
Not quite - there is a reason why https://wikipedia.org/wiki/Clean_room_design exists as a concept to workaround copyright, and the same concept could hold for ML models.
So my current perspective is this: if a picture is drawn in a style similar to yours for example, that's not infringing, but if it's just a scrapbook collage of cutouts of your art it is, except where it's fair use. Would that be right?
So the same applies, if an AI actually can help people write code by learning from existing code, that's fine, but if an AI just copy pastes code blocks that's not.
Where am I going wrong here?
From that view let the data be free.
Indeed it seems like code will be vastly more prone to this problem compared to art because changing a single pixel is merely a question of aesthetics whereas code is constrained tightly by the syntax of the language. With a much smaller space of correct results duplication is likely inevitable.
For art, train on contributed images whose authors agree to use them for that purpose. There could be some organization, perhaps a nonprofit, that would own the images and users of the model could credit that organization, and perhaps contribute back their own generated work. That way a legally clean commons could be built and could grow.
That said, the application of copyright to text, vs, code, vs images, has salient differences. The concept of plagiarism in visual artwork exists by analogy, but it's hard to call it coherent.
Music is somewhere in the middle, people have sued successfully over melody and hooks.
There are things like characters in animation, it's not unheard of, but the balance is more on the side of "great artists steal".
Disrupt onward I say. Humans learn and remix from prior copyrighted work all the time using their brains (consciously chosen or not). So long as the new work is distinguishable enough to be unique there's nothing wrong with these new AI creations.
As mentioned before "style" is not something subject to copyright and the model creates a model of that style. The process of finetuning a model generally means that one would not want to recreate the original images as that would overfit it and render it, essentially useless.
When it comes to code, there is a higher chance of getting a one-to-one clone of the input as the options used in creating an algorithm, or even a simple function are dramatically reduced imo.
I have written projects where I'd consider a handful of lines of code to be the core central tenant of the entire project that everything else is built up around. Copy those lines and everything else is scaffolding that falls out naturally from the development process.
I'm not so sure. There's a generated image in the article that I think looks enough like Wonder Woman to cause a lawsuit.
That's just one of a handful of images in the article, and doesn't seem to have been chosen for its similarity to Wonder Woman.
But could it be considered an intellectual and sociological denial-of-service attack when it's scaled to the point where a machine can crank out dozens of derivative works per minute? I'm not sure this is a situation at all comparable to human artists making derivative works. Those involve long periods of concentration, focus, and reflection by a conscious human agent to pull off, thus in some sense furthering to the intellectual development of humanity and fostering a deeper appreciation for the source work. The machine does none of that; it's sort of just a photocopier one step removed in hyperspace, copying some of the artists' abstractions instead of their brush strokes.
Since when did that become a requirement? If those are the rules now, then cutting the final credits is good enough to start torrenting movies.
> When it comes to code, there is a higher chance of getting a one-to-one clone of the input as the options used in creating an algorithm, or even a simple function are dramatically reduced imo.
If you're going to consider each function within a larger work as an individual work, that makes the 1:1 replacement claim more dubious. In order to recognizably imitate a style, one or more features of that style have to be recognizably copied, although no single area of the illustration would have to be. A function is a facet of a complete program just like recognizable features of a style are facets of each work an artist produces. If it helps, consider an artist's style as their own personal utility library.
Code is different, there isn't a style to it other than perhaps indention and variable naming conventions. Entire sections (that are protected by GPL) are copied. This by itself isn't the issue, it's contaminating your codebase that is the problem. If your work ends up with the same license as the code sources and those are properly documented per those license agreements you're fine. But if you end up violating the GPL and someone knows their code is in use, you are in a tough situation. Again, it'll end up in an expensive court room session where a judge is going to have to determine if enough code was copied to be construed as a license violation. That's the one scenario you would want to avoid in the first place because for a lot of businesses that kind of lawsuit is too expensive to fight.
Sometimes. People also borrowed them, read them in libraries, or in later years looked at free textbooks online.
> and a license granted for such use
I never heard of such a thing, and it was never seen as necessary. No student checked the licenses on their textbooks before deciding whether they could read them.
That’s one reason why it is probably better to have a derived world view than a contrived world view.
Probably worth pointing out that GitHub has a license to the code on its site (read the fine print) that is independent of other licenses that the code may available under.
Whether that license applies to training ML models is legally uncharted waters.
Whether it’s right to train those models is another matter.
Well, maybe they were all just downvoted into invisibility. But up to your post, I have seen none.
ps. I'm surprised music has not yet been yet leveled by AI, can't wait for "dimmu borgir christmas carols" prompts.
Once you have separated sources for a training data set, it's like text generation, except that instead of a single function of sequence position, you have multiple correlated functions of time. Text generation models can barely maintain self consistency from paragraph to paragraph, which is a sequence difference of maybe 200 tokens, now consider moving from token position to a time variable, and adding the requirement that multiple sequences retain coherency both with each other, and with themselves over much larger distances.
There are generative music models, but it's mostly stuff that's been trained on midi files for a specific genre or artist, and the output isn't that impressive.
I am also eagerly awaiting "hark the bloodied angel screams" with blastbeats, shrieks and blistering tremolo guitar, though.
Stealing from idealist volunteers is worse than stealing from a random person.
Code generation models need to have special handling for checking if the generated code falls under copyright.
IANAL, but to a first-order approximation: everything is "copyrighted" [1]. The copyright is owned by someone/something. The owner gets to set the terms of the licensing. The rare things not under copyright may have been put explicitly into the public domain (which actually takes some effort), or have had their copyright expire (which takes quite a while; thanks Disney).
So: this is really a question about fair use [2], and about when the terms of licensing kick in, and it should be understood and discussed as such. I don't think anyone who has really thought about this is claiming that the models can't be trained on (copyrighted) material; the consumption of the material is not the problem, is it? The problem is that the models: (1) sometimes recreate particular inputs or identifiable parts of them (like the Getty watermark), or recreate some essential characteristics of their inputs (like possibly trademark-able stylistic elements), AND ALSO, (2) have no way of attributing the output to the input.
Without being able to identify anything specific about the input, it is impossible know with certainty that the output falls within fair use (e.g. because it was sufficiently transformative), and it is impossible to know how to implement the terms of licensing for things that don't fall within fair use. There's just no getting around that with the current crop of models.
The legal minefield is not from (1) or (2), but from (1)+(2), at the moment of redistribution, monetized or not. Even if Copilot was only trained on non-reciprocal licenses (BSD, MIT), there are very likely still licensing terms of use, which may include identifying the original copyright owner. Reciprocal licenses like GPL have more involved licensing terms, but that is not the problem: the problem is failure to identify the original licensing terms. We should not use these models as an opportunity to make an issue about GPL or its authors, or about the business model of companies like Getty; both rest on copyright, and come to our attention because of licensing.
Sorry about the rant. As for your question: I think it may be as simple as: to what extent are readers here the producers of inputs to the ML models, versus consumers of outputs. It gets personal for coders when models violate licensing terms of FOSS code, but it feels fun/empowering to wield the models to make images that we'd otherwise be unable to access. From my rant above you can tell that whether its for code or images, I think the whole thing is an IP disaster.