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.

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.

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).

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.

https://news.ycombinator.com/item?id=33425414

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 AIs 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.

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 shifting 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 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.

The difference between human an AI model is that AI focuses on seeing completely but human has to do non-art stuff as well.