Humans learn to produce good art largely by learning from existing art. Copying, mimicking, or just generally taking inspiration. We're going to need to get over this hang up. Status quo bias is dumb. Especially among people who are otherwise fond of piracy or anti-ip legislation.
Sounds to me like you've decided that AI veganism is not for you!
I've thought about this comparison to human artists taking inspiration from each other a bit. The problem becomes the scale. A human artist can't look at 5bn images and use them to be able to instantly mimic the style of another artist, hundreds of times an hour.
Consider facial recognition technology. At an individual level, empowering people to quickly see all of the photos they have taken of their spouse through running facial recognition against their photos is useful and harmless. Allowing governments to run the exact same technology against every photo uploaded to Facebook is a massively harmful expansion of the surveillance state.
I don't think training models against 5bn unlicensed images is in the same scale of harm as running facial recognition against an entire country's worth of people. But this example does show that things that are fine on a small scale can be harmful at a big scale.
> Sounds to me like you've decided that AI veganism is not for you!
I feel it is insulting to veganism to try and coin this term. The moral arguments are not similar, and this one is frankly much weaker.
> I've thought about this comparison to human artists taking inspiration from each other a bit. The problem becomes the scale. A human artist can't look at 5bn images and use them to be able to instantly mimic the style of another artist, hundreds of times an hour.
A human can mimic something with a single reference. It might not be good, but that can hardly matter for a discussion of ethics. The morality cannot depend on whether or not humans can do something poorly or not.
> Consider facial recognition technology. At an individual level, empowering people to quickly see all of the photos they have taken of their spouse through running facial recognition against their photos is useful and harmless. Allowing governments to run the exact same technology against every photo uploaded to Facebook is a massively harmful expansion of the surveillance state.
Entirely different issue that happens to utilize a similar tech.
> But this example does show that things that are fine on a small scale can be harmful at a big scale.
> The problem becomes the scale. A human artist can't look at 5bn images and use them to be able to instantly mimic the style of another artist, hundreds of times an hour.
Can you say more about the scale argument? It reads as if you're saying that an entity that has an advantage shouldn't use that advantage.
If you reduce the comparison to human scale, do you still have a problem with the competition? I.e., if one artist has a working memory of 5 inspirational artworks (unlicensed) and another artist has a working memory of 3, is that wrong if they both compete to produce new artwork based on what they know of existing art?
If the answer to that question is no, then I don't see how the scale question is applicable.
> I've thought about this comparison to human artists taking inspiration from each other a bit. The problem becomes the scale. A human artist can't look at 5bn images and use them to be able to instantly mimic the style of another artist, hundreds of times an hour.
To me this seems to similar to old fashioned luddites. The machines back then that improved the productivity of labourers mostly displaced the societal role of skilled craftsmen. Of course using the machine is different to doing it by hand, but we did have some benefits from improving our productivity using machines, as well as some downsides. The interesting thing is that in this case we are moving from using machines to improve productivity of material objects, into sociocultural objects (art), and it's not clear what that will "do" exactly.
> But this example does show that things that are fine on a small scale can be harmful at a big scale.
Right but of course, the words "can be" are there. I don't think anyone is claiming that scaling a given thing up is always harmless, but generally with these kinds of technological developments you really need a super strong, clear, and concrete (for this specific case) argument to stop them. I haven't seen any such arguments, except the traditional luddite one I posted above.
Simonw, we are about to have much worse problems from bot swarms. Sleeper bots trained on “get more upvotes” will amass karma points and then be unlesshed as implacable crowds of “people” pushing public opinion around. They will amass capital and social capital to the point where regular humans represent a vashingly small sliver of online capital.
This idea of CAPTCHAs and “human only, no bots” networks is only for a decade or two, until bots just become more desirable members of every community, to humans and other bots.
What people don’t get is that their individual decisions in their own self interest doom the group. Dad works 10 hours a day to provide for family. Then mom does too. Now they both neglected their kids and each other.
Similarly, you choose a bot for its wittier comebacks and impeccably romantic style of talking. Your husband chooses a bot for its better sexual techniques and never complaining. Ultimately, neither of you needs the other and you’re one step away drom NO ONE IN SOCIETY needing you.
You would become the Borg. Except why have the drone bodies at all?
> A human artist can't look at 5bn images and use them to be able to instantly mimic the style of another artist, hundreds of times an hour.
It's just another step on the path towards AI being better than humans at certain activities. We've already gone through this with things like math, chess, imitating voices, translating languages, and lately visual art seems to be coming close to a point where the AI will have an edge over humans. We will have to learn to cope, one way or another, because eventually, AI will be better at everything, as we are limited by our biological bodies.
Humanity will create its own synthetic god, and the consequences will be interesting.
The argument about differences in scale constituting differences in kind is compelling, and yet I'm not sure how to evaluate any particular instance of this type of argument without just deferring to my existing opinion on the topic at hand.
For example, it's easy to construct hypothetical scenarios where a difference in scale seem to clearly constitute a difference in kind and where suppression of a new technology seems clearly reasonable. An easy hypothetical (but not implausible) example is some new kind of extremely deadly weapon that would be much easier to build than existing weapons if the "recipe" was made public.
But it's also easy to make the same argument for things like, say, the printing press. And indeed there has always been intense opposition from many to the printing press and subsequent technologies that make distributing written language much easier, with opponents often presenting arguments in this same form. Yet it feels wrong to me to apply the argument this way.
Is that just because I have existing opinions that generally oppose restrictions in the ability to distribute ideas, but I don't generally oppose restrictions on very deadly weapons? Or is there some other way to distinguish between valid and invalid instances of this form of argument, perhaps by attempting to estimate the overall risk versus reward?
These problems of scale aren't unique to recent leaps in technology, or even to automation or technology at all. Government intelligent agencies can, given certain goals and management, do a great deal of harmful and disturbing things without using any secret or advanced technology. A team of 1,000 people with, say, basic PC office equipment from the early 2000s, could do a great deal if they were competently trained and managed and told to, say, investigate and attempt to undermine some group that the agency doesn't like for whatever reason. In my opinion the technology has little to do with the problem, even though the potential for harm is unique to the scale of resources dedicated to it, and even though they would be a lot less effectively if they didn't have that technology (i.e. imagine if they no longer had access to personal computers at the office).
But author of the article says he would be fine with AI trained on licensed images, which could be also on bigger scale than a single human is capable of.
Looks like some people doesn't like it because the data it was trained on but others are worried about the technology and its capabilities over humans.
This blindly steam rolls over some important distinctions. Humans aren’t allowed to sell direct copies of other people’s art (not in the US and many other countries anyway). This isn’t a hang-up, and it’s not status quo bias, it’s well covered law and economic philosophy that is already blending new digital rights ideas into hard fought legal precedents. We have explicitly decided to have the social goal to protect the rights of artistic creative people & businesses without having their work instantly ripped off.
Humans also are good at taking inspiration from ideas, where today’s AI is borrowing pixels. The AI is copying in a way that humans don’t, it’s not mimicking and taking inspiration - that is anthropomorphising the software that is trained and programmed to make automated mashups of images.
So in part it depends on what we do with AI images. There may be nothing wrong with training on copyrighted material if the resulting inferences are never distributed publicly nor used for commercial purposes. Of course that seems extremely unlikely, which is why it needs to be discussed carefully and debated in good faith, right?
Maybe in part it also depends on whether the AI software is guaranteed to produce something very different from any individual training image. If the outputs are guaranteed to be always a mashup, and never indistinguishable from any single input, that seems like it would be an easier pill to swallow. (There appears to be legal precedent along these lines for music sampling.)
> Humans aren’t allowed to sell direct copies of other people’s art
Direct copying yes, but stealing ideas? No. Actually it is a well-known practice in the art industry to use several existing works as references, and creating new artwork that is bit of mixture of all of them.
That is exactly what DALL-E/StableDiffusion are trying to do.
AI isn't borrowing pixels, either. If you read the idea of diffusion models, quite the opposite, what it directly learns is how to destroy an image from what it is into gaussian noise. The trick here is we can reverse this process 'creating' arts from noises.
There is no copying pixels, not even in the most simplistic version of how this model works.
More importantly, humans are inspectable. You can ask a human why they did something one way. You can put them on a stand and ask them whether they had knowledge of X, why they didn't do alternative Y, how they arrived at formulation Z.
All this goes out the window with AI, we have to judge the entire model as a whole, and even though we see DALL-E etc spit out watermarks we can't call it a liar or a thief. "This part overfits but the whole model is transformative, we think" say the authors.
1) Humans learn from existing art, but they mainly draw on their experience and perception of the physical world when creating new art. AI doesn't have access to the real world, so it's art is 100% based on existing work, not just inspired by it.
2) We still don't know exactly how much the models memorise. I'm sure you'll agree that something like Google image search retrieval doesn't qualify as original art, and copyright is still an issue. If you photoshop two images together, you probably also still have to give credit to the original images. We have to draw the line somewhere on the scale from "100% derivative to 100% original". It's not yet clear where AI image generation falls on this.
> Humans learn from existing art, but they mainly draw on their experience and perception of the physical world when creating new art. AI doesn't have access to the real world, so it's art is 100% based on existing work, not just inspired by it.
Ok so let's just feed the ai some dashcam footage and this argument is null.
> I'm sure you'll agree that something like Google image search retrieval doesn't qualify as original art,
> Humans learn to produce good art largely by learning from existing art
I think you’re wrong on this. Humans learn (anything) from teachers. Yes, you can learn much on your own, but the idea of a solo artist learning from books is exceedingly rare.
So in this case, you learn art by doing, critiquing your work and the works of others. You put forth effort.
While I am amazed at the generated images and think they truly are amazing, I can’t help but think they all feel a little cheap. Like someone took a shortcut that was never meant to be found.
I do think there are real ethical issues behind the training data for both image and code generation. Nothing that can’t be solved, but random images scraped from the web are not meant for training. First - it’s not necessarily a fair-use issue. And second, garbage in, garbage out. I don’t want my auto generated images to come with a Getty watermark.
Where I do think there is hope is for the use of these as tools for artists. Where there can still be a human behind the choices and curation, but using the algorithms as a means rather than an end.
> I think you’re wrong on this. Humans learn (anything) from teachers. Yes, you can learn much on your own, but the idea of a solo artist learning from books is exceedingly rare.
If you take an art class where a teacher instructs you and tells you to replicate 12 paintings by Van Gogh, I would argue you've learned more from Van Gogh than the teacher, even if he's showing you some physical techniques.
But that is just art. Not great art.
Great art comes from stepping away from your peer group after you mastered it, being able to incooperate "unrelated" or "impossible" other concepts into the art. Its a subconscious process, of recombination and filtering.
And only some can boldly go, to were no person has gone before.
Which makes this the ultimate training goal for AI. Not AGI, but a synthesis AI, capable to produce "breakthrough" candidates for the field it is trained upon, by allowing noise and filtering for the criteria of great break throughs- explanation power, beauty, higher consistency, that puzzle piece fitting all gaps moment. If there is ever a creature out there, doing that, silicon or otherwise, humanity will own its continued existence to its existence.
To a certain extent I agree, but another argument would be that at the end of the day, computers are deterministic (even on the incredibly large scale of these image generators, given parameters x, it will produce y).
We're still not sure whether humans are deterministic or not. So you can't really equate the human process of art creation to a computers. Humans may still be pulling from some external inspiration that will never be within the reach of computers (I like to think that's the case).
I'm not sure determinism is where you want to draw the line. Just have the computer get a webcam pointed at a lava lamp. It's non-deterministic now.
Or maybe you want to still argue that that's ultimately deterministic.
Okay, well, just have a person show up to the art computer, look deep within their soul, then type in a completely random, soulful, non-deterministic seed value into the art computer. Bam! Now the computer can create real art.
Yes.. but a human can understand what I want and anticipate various outcomes from much shorter conversations that don't involve all this ridiculous prompting. Asking this system for a simple picture like "a dog smoking a cigarette" reveals just how limited this system is.
Thinking that this system, with such a small set or data and no natural language processing, is going to replace artists anytime soon, is, I think, incredibly eager to the point of foolishness.
That's fine, until an AI regurgitates a unique page of code I wrote without modification. That's just copying. Although, if the courts want to clarify that straight-up copying a page or two of code is okay, I would be happy.
My fear is copilot will be allowed to copy code, but I won't be.
But that's just stupid. You find some code on stack overflow that solves your problem. Are you going to do the refactoring dance until it looks different enough or are you just going to use the code? You find the same code on a non open source github repo. Well what now? You've got a mental model of how to solve the problem. Can't use that knowledge? That knowledge has been locked away as illegal?
I wonder what would happen if an artist got their stuff legally removed from the sources, and then you asked the AI to produce something in the style of their main influences.
Sure but the output from the AI does not give credit. So I don't know what the inspiration is or where it came from or how to find more of the same kind.
I don't think calling it a stencil copy is the right mental model.
These models take 5bn+ images and use them to influence the weights on a giant array of floating point numbers. Each input image has a truly tiny effect on the finished product.
The final model is on the order of 4.5GB - the compression ratio is unreal. Nothing of the original images remains.
is this in the same vein as professional Go and Chess players having and existential crisis over AlphaGo/AlphaZero/MuZero attaining superhuman playing ability with zero supervised learning in something like < 72 hours on non-supercomputer hardware.
i suppose we'd all feel uneasy when AI eventually becomes better than humans at creative tasks which pay our bills or differentiate us within a profession.
> I know many vegans. They have access to the same information as I do about the treatment of animals, and they have made informed decisions about their lifestyle, which I fully respect.
> I myself remain a meat-eater.
It strikes me as off that one would consider themselves informed enough to have decided not to be a vegan but consider image generation AI unethical. As a former carnivore (literally sometimes going an entire day eating mostly or entirely meat), access to the information about just how horrific factory farming is and the willingness to open my eyes to it was the only thing that stopped me from being persuaded.
That's my point. Even though I understand how unethical it is to eat meat, I continue to chose to eat it. I am not proud to remain a meat eater!
I only eat meat once or twice a week, and I try to consider the sources, but despite understanding the moral implications of doing so I have not gone vegan or vegetarian.
To my mind, this is similar to a situation in which I determine that using AI trained on unlicensed images is unethical but continue to chose to use those AIs.
Yeah, I found the taking issue with copyright as the primary reason for being averse to AI to be almost breathtakingly hilarious (I at least exhaled through my nostrils once I read it). This quibble to me seems like the most anemic criticism one could muster around the ethical considerations implied by this new generation of AI.
Something as fundamental as eating -- a process you do multiple times a day to keep yourself alive, and compromising on is an incredibly large convenience/monetary/lifestyle tradeoff -- versus using AI models for generating images from text prompts are very different.
I don't think this is universally unethical, and even if one does find this unethical, it seems low on the list of unethical things to be worrying about. Even in the context of exploiting people's labor.
Furthermore, I think a lot of art generated by actual intelligence is made by those consuming tons of copyrighted material and putting a twist on things. Is it unethical to listen to The Monkees, since, to put it in the terms of the article, they were so clearly trained on the Beatles with a few tweaks here or there?
People have found inspiration in other works since we've been creating art.
In music if you use a snipet of someone else's recording you have to pay them. It wasn't always clear that would be the case, but thats where they landed. (You end up with Led Zeppelin and Beatles Samples in some early rap).
But in visual art its a little different. Borrowing is more common and remixing is kinda allowed. When does it become "transformative?" (I always think of the "Hope" poster lawsuit, where the borrower sued the photographer as a strange one. )
https://www.law.columbia.edu/news/archive/obama-hope-poster-...
But what is the AI doing? I don't think its taking the input given and being inspired... Its kinda just sampling, in a way that makes it seem like its being original. Or is the unique training set/ annotations that are is giving the AI its unique output the art, in which case its more original.
At some point some AI is going to spit something out too close to something else and the courts will probably have to decide.
>At some point some AI is going to spit something out too close to something else and the courts will probably have to decide.
People have been spitting out stuff too close to decide and letting courts decide for well over a hundred years. This is nothing new.
If people make art that is in the style of someone else, by hand or by AI, it's generally fine to do so. If it gets too close, then courts can step in as always.
Yeah, vegetarianism is on the list (along with religion, politics, and vi/emacs) of subjects that can completely derail a discussion. Another analogy might have created less distraction. I would have avoided this one personally.
> Stable Diffusion has been trained on millions of copyrighted images scraped from the web.
How is it different from how human artists train on copyrighted images?
We have no trouble to award them copyright on art which consists of elements, or is heavily inspired by elements of copyrighted works they've seen during their education?
Human imagination can't create anything really novel. Everything you create is just cutting, stitching and deforming what you already seen in semi-random ways until you get something interesting to somebody.
> Human imagination can't create anything really novel. Everything you create is just cutting, stitching and deforming what you already seen in semi-random ways until you get something interesting to somebody.
That's an awfully low opinion of art and humanity.
Company consisting of trained artists also makes $$$ on copyrighted work. Why is it ok if the creative stitching engine is made of humans but not when it's made of computers?
This is ridiculous. It's not just AIs models that built their abstract conceptions on copyrighted material, but humans too. When a human artist paints a futuristic dome, they are also subconsciously accessing millions of copyright images they've seen throughout their lives and using them "without consent". To be consistent the author would need to avert their eyes and never look at copyrighted imagery.
Also the comparison to veganism and animal suffering is off putting.
There is so much doomsay around these image generation AIs and I don't really understand it. Did photographs devalue painters? Did digital art devalue painters? Did movies devalue theater? Did YouTube videos devalue cinematographers? Did Twitch streams or TikTok videos devalue YouTubers?
Technology has continuously brought us easier and more immediate ways to create art, inspiring new generations of artists who hone their skills with the new tech. Meanwhile, older forms of art continue to be valued along side the new stuff.
I'm also having a hard time seeing the ethical crisis with these AIs being trained on copyrighted material. Styles are not (or at least should not be) copyrightable. An artist can be inspired by the works of another and go on to create something new. Many forms of derivative work are even specifically granted safety under existing copyright law.
Besides, it would be practically impossible to prove that a model was trained on copyrighted works. Even if we decided it was unethical, any law against it would be theoretical and practically unenforceable. Either way, artists will have to adapt.
I don't think the situation is near as dire as so many seem to believe. An AI can only reproduce a style that has been thoroughly explored by the content which it is trained on. New styles will continue to be rewarded. Digital artists will be encouraged to push boundaries. And for the time being, the AIs still have some pretty severe limitations so artists will be able to capitalize on those.
And one more thing I never see brought up when talking about these AI image generators: there's already precedent for how this will play out. AI music composers have been around for many years now, but Dua Lipa and The Weeknd appear to be doing just fine. Even the more classical composers and orchestras seem to be going just as strong as ever. If AI artists show no sign of toppling the music industry, why should we expect the fate of digital images to be so different?
>There is so much doomsay around these image generation AIs and I don't really understand it. Did photographs devalue painters? Did digital art devalue painters? Did movies devalue theater? Did YouTube videos devalue cinematographers? Did Twitch streams or TikTok videos devalue YouTubers?
All of those are tools which did not lead to potentially the same final product produced for a miniature fraction of the labor costs.
AI composition is generally pretty shit. If (more like when) it becomes better, we will be having the exact same argument regarding composers.
> All of those are tools which did not lead to potentially the same final product produced for a miniature fraction of the labor costs.
In what practical sense are paintings distinct from photography or digital art?
It's easy to see how they're technically different, but in terms of purpose paintings should have been supplanted by photography and digital art long ago. After all, the latter two can be reproduced "for a miniature fraction of the labor costs", provide the same utility, and can be reproduced at a much larger scale. Yet painting and similar physical art forms seem to be as economically viable as ever - possibly more than ever, but I'm having trouble finding hard numbers to confidently back that up.
So soft-PSA: the following is more than a little misleading:
“The fact that it can compress such an enormous quantity of visual information into such a small space is itself a fascinating detail.”
This is not a detail: it’s the principle mechanism. The ability to compress something is conferred by the identification and exploitation of structure, conversely the scarcity or absence of structure inhibits or prohibits compression. You can eyeball check an RNG with compression techniques.
This has counter-intuitive consequences that you can test on your laptop! Even using off-the-shelf codecs it only takes a modest corpus to see that pop music compresses better than eclectic jazz, which compresses better than white noise. The same thing holds for headshots of people: a big pile of headshots drawn from a reasonably broad corpus of humans will enjoy a noticeably lower compression ratio than a subset selected by any plausible “conventional attractiveness” filter. “Conventional attractiveness” (defined any common-sense way) correlates sharply with bilateral symmetry, with obvious implications for storage space.
Information theory is the thread that ties together all this AI craze stuff!
Readit News
I've thought about this comparison to human artists taking inspiration from each other a bit. The problem becomes the scale. A human artist can't look at 5bn images and use them to be able to instantly mimic the style of another artist, hundreds of times an hour.
Consider facial recognition technology. At an individual level, empowering people to quickly see all of the photos they have taken of their spouse through running facial recognition against their photos is useful and harmless. Allowing governments to run the exact same technology against every photo uploaded to Facebook is a massively harmful expansion of the surveillance state.
I don't think training models against 5bn unlicensed images is in the same scale of harm as running facial recognition against an entire country's worth of people. But this example does show that things that are fine on a small scale can be harmful at a big scale.
I feel it is insulting to veganism to try and coin this term. The moral arguments are not similar, and this one is frankly much weaker.
> I've thought about this comparison to human artists taking inspiration from each other a bit. The problem becomes the scale. A human artist can't look at 5bn images and use them to be able to instantly mimic the style of another artist, hundreds of times an hour.
A human can mimic something with a single reference. It might not be good, but that can hardly matter for a discussion of ethics. The morality cannot depend on whether or not humans can do something poorly or not.
> Consider facial recognition technology. At an individual level, empowering people to quickly see all of the photos they have taken of their spouse through running facial recognition against their photos is useful and harmless. Allowing governments to run the exact same technology against every photo uploaded to Facebook is a massively harmful expansion of the surveillance state.
Entirely different issue that happens to utilize a similar tech.
> But this example does show that things that are fine on a small scale can be harmful at a big scale.
It does not.
Can you say more about the scale argument? It reads as if you're saying that an entity that has an advantage shouldn't use that advantage.
If you reduce the comparison to human scale, do you still have a problem with the competition? I.e., if one artist has a working memory of 5 inspirational artworks (unlicensed) and another artist has a working memory of 3, is that wrong if they both compete to produce new artwork based on what they know of existing art?
If the answer to that question is no, then I don't see how the scale question is applicable.
To me this seems to similar to old fashioned luddites. The machines back then that improved the productivity of labourers mostly displaced the societal role of skilled craftsmen. Of course using the machine is different to doing it by hand, but we did have some benefits from improving our productivity using machines, as well as some downsides. The interesting thing is that in this case we are moving from using machines to improve productivity of material objects, into sociocultural objects (art), and it's not clear what that will "do" exactly.
> But this example does show that things that are fine on a small scale can be harmful at a big scale.
Right but of course, the words "can be" are there. I don't think anyone is claiming that scaling a given thing up is always harmless, but generally with these kinds of technological developments you really need a super strong, clear, and concrete (for this specific case) argument to stop them. I haven't seen any such arguments, except the traditional luddite one I posted above.
This idea of CAPTCHAs and “human only, no bots” networks is only for a decade or two, until bots just become more desirable members of every community, to humans and other bots.
What people don’t get is that their individual decisions in their own self interest doom the group. Dad works 10 hours a day to provide for family. Then mom does too. Now they both neglected their kids and each other.
Similarly, you choose a bot for its wittier comebacks and impeccably romantic style of talking. Your husband chooses a bot for its better sexual techniques and never complaining. Ultimately, neither of you needs the other and you’re one step away drom NO ONE IN SOCIETY needing you.
You would become the Borg. Except why have the drone bodies at all?
It's just another step on the path towards AI being better than humans at certain activities. We've already gone through this with things like math, chess, imitating voices, translating languages, and lately visual art seems to be coming close to a point where the AI will have an edge over humans. We will have to learn to cope, one way or another, because eventually, AI will be better at everything, as we are limited by our biological bodies.
Humanity will create its own synthetic god, and the consequences will be interesting.
For example, it's easy to construct hypothetical scenarios where a difference in scale seem to clearly constitute a difference in kind and where suppression of a new technology seems clearly reasonable. An easy hypothetical (but not implausible) example is some new kind of extremely deadly weapon that would be much easier to build than existing weapons if the "recipe" was made public.
But it's also easy to make the same argument for things like, say, the printing press. And indeed there has always been intense opposition from many to the printing press and subsequent technologies that make distributing written language much easier, with opponents often presenting arguments in this same form. Yet it feels wrong to me to apply the argument this way.
Is that just because I have existing opinions that generally oppose restrictions in the ability to distribute ideas, but I don't generally oppose restrictions on very deadly weapons? Or is there some other way to distinguish between valid and invalid instances of this form of argument, perhaps by attempting to estimate the overall risk versus reward?
"meat-based" vs "plant-based" is a spectrum we are all on; no reason to make it all-or-nothing
We should allow _everybody_ to run the exact same technology. Governments are not special. Power to the people.
Humans also are good at taking inspiration from ideas, where today’s AI is borrowing pixels. The AI is copying in a way that humans don’t, it’s not mimicking and taking inspiration - that is anthropomorphising the software that is trained and programmed to make automated mashups of images.
So in part it depends on what we do with AI images. There may be nothing wrong with training on copyrighted material if the resulting inferences are never distributed publicly nor used for commercial purposes. Of course that seems extremely unlikely, which is why it needs to be discussed carefully and debated in good faith, right?
Maybe in part it also depends on whether the AI software is guaranteed to produce something very different from any individual training image. If the outputs are guaranteed to be always a mashup, and never indistinguishable from any single input, that seems like it would be an easier pill to swallow. (There appears to be legal precedent along these lines for music sampling.)
Direct copying yes, but stealing ideas? No. Actually it is a well-known practice in the art industry to use several existing works as references, and creating new artwork that is bit of mixture of all of them.
That is exactly what DALL-E/StableDiffusion are trying to do.
AI isn't borrowing pixels, either. If you read the idea of diffusion models, quite the opposite, what it directly learns is how to destroy an image from what it is into gaussian noise. The trick here is we can reverse this process 'creating' arts from noises.
There is no copying pixels, not even in the most simplistic version of how this model works.
All this goes out the window with AI, we have to judge the entire model as a whole, and even though we see DALL-E etc spit out watermarks we can't call it a liar or a thief. "This part overfits but the whole model is transformative, we think" say the authors.
I disagree with this, we don't just hear about ideas we actually see the art. Both are shown pictures.
2) We still don't know exactly how much the models memorise. I'm sure you'll agree that something like Google image search retrieval doesn't qualify as original art, and copyright is still an issue. If you photoshop two images together, you probably also still have to give credit to the original images. We have to draw the line somewhere on the scale from "100% derivative to 100% original". It's not yet clear where AI image generation falls on this.
Ok so let's just feed the ai some dashcam footage and this argument is null.
> I'm sure you'll agree that something like Google image search retrieval doesn't qualify as original art,
Yes
> and copyright is still an issue
No
I think you’re wrong on this. Humans learn (anything) from teachers. Yes, you can learn much on your own, but the idea of a solo artist learning from books is exceedingly rare.
So in this case, you learn art by doing, critiquing your work and the works of others. You put forth effort.
While I am amazed at the generated images and think they truly are amazing, I can’t help but think they all feel a little cheap. Like someone took a shortcut that was never meant to be found.
I do think there are real ethical issues behind the training data for both image and code generation. Nothing that can’t be solved, but random images scraped from the web are not meant for training. First - it’s not necessarily a fair-use issue. And second, garbage in, garbage out. I don’t want my auto generated images to come with a Getty watermark.
Where I do think there is hope is for the use of these as tools for artists. Where there can still be a human behind the choices and curation, but using the algorithms as a means rather than an end.
If you take an art class where a teacher instructs you and tells you to replicate 12 paintings by Van Gogh, I would argue you've learned more from Van Gogh than the teacher, even if he's showing you some physical techniques.
And only some can boldly go, to were no person has gone before.
Which makes this the ultimate training goal for AI. Not AGI, but a synthesis AI, capable to produce "breakthrough" candidates for the field it is trained upon, by allowing noise and filtering for the criteria of great break throughs- explanation power, beauty, higher consistency, that puzzle piece fitting all gaps moment. If there is ever a creature out there, doing that, silicon or otherwise, humanity will own its continued existence to its existence.
We're still not sure whether humans are deterministic or not. So you can't really equate the human process of art creation to a computers. Humans may still be pulling from some external inspiration that will never be within the reach of computers (I like to think that's the case).
Or maybe you want to still argue that that's ultimately deterministic.
Okay, well, just have a person show up to the art computer, look deep within their soul, then type in a completely random, soulful, non-deterministic seed value into the art computer. Bam! Now the computer can create real art.
Thinking that this system, with such a small set or data and no natural language processing, is going to replace artists anytime soon, is, I think, incredibly eager to the point of foolishness.
My fear is copilot will be allowed to copy code, but I won't be.
This is not a reasonable model.
Art is not about form. It's about content. Meaning that can be generated and understand only by humans.
Would you say that artist did not produce something that could be called art, just because they used an AI model as part of their process?
These models take 5bn+ images and use them to influence the weights on a giant array of floating point numbers. Each input image has a truly tiny effect on the finished product.
The final model is on the order of 4.5GB - the compression ratio is unreal. Nothing of the original images remains.
So "stencil copy" doesn't work for me.
i suppose we'd all feel uneasy when AI eventually becomes better than humans at creative tasks which pay our bills or differentiate us within a profession.
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> I myself remain a meat-eater.
It strikes me as off that one would consider themselves informed enough to have decided not to be a vegan but consider image generation AI unethical. As a former carnivore (literally sometimes going an entire day eating mostly or entirely meat), access to the information about just how horrific factory farming is and the willingness to open my eyes to it was the only thing that stopped me from being persuaded.
I only eat meat once or twice a week, and I try to consider the sources, but despite understanding the moral implications of doing so I have not gone vegan or vegetarian.
To my mind, this is similar to a situation in which I determine that using AI trained on unlicensed images is unethical but continue to chose to use those AIs.
Everyone cutting meat by 50% is more impactful than 5% of people cutting meat to 0%
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Furthermore, I think a lot of art generated by actual intelligence is made by those consuming tons of copyrighted material and putting a twist on things. Is it unethical to listen to The Monkees, since, to put it in the terms of the article, they were so clearly trained on the Beatles with a few tweaks here or there?
In music if you use a snipet of someone else's recording you have to pay them. It wasn't always clear that would be the case, but thats where they landed. (You end up with Led Zeppelin and Beatles Samples in some early rap).
Even "Sound alike " singers get litigated: https://en.wikipedia.org/wiki/Midler_v._Ford_Motor_Co.
But in visual art its a little different. Borrowing is more common and remixing is kinda allowed. When does it become "transformative?" (I always think of the "Hope" poster lawsuit, where the borrower sued the photographer as a strange one. ) https://www.law.columbia.edu/news/archive/obama-hope-poster-...
But what is the AI doing? I don't think its taking the input given and being inspired... Its kinda just sampling, in a way that makes it seem like its being original. Or is the unique training set/ annotations that are is giving the AI its unique output the art, in which case its more original.
At some point some AI is going to spit something out too close to something else and the courts will probably have to decide.
People have been spitting out stuff too close to decide and letting courts decide for well over a hundred years. This is nothing new.
If people make art that is in the style of someone else, by hand or by AI, it's generally fine to do so. If it gets too close, then courts can step in as always.
How is it different from how human artists train on copyrighted images?
We have no trouble to award them copyright on art which consists of elements, or is heavily inspired by elements of copyrighted works they've seen during their education?
Human imagination can't create anything really novel. Everything you create is just cutting, stitching and deforming what you already seen in semi-random ways until you get something interesting to somebody.
Try imagining how an alien might look.
That's an awfully low opinion of art and humanity.
The part where the company training the models makes $$$ on copyrighted work can definitely be a debate point.
Also the comparison to veganism and animal suffering is off putting.
Technology has continuously brought us easier and more immediate ways to create art, inspiring new generations of artists who hone their skills with the new tech. Meanwhile, older forms of art continue to be valued along side the new stuff.
I'm also having a hard time seeing the ethical crisis with these AIs being trained on copyrighted material. Styles are not (or at least should not be) copyrightable. An artist can be inspired by the works of another and go on to create something new. Many forms of derivative work are even specifically granted safety under existing copyright law.
Besides, it would be practically impossible to prove that a model was trained on copyrighted works. Even if we decided it was unethical, any law against it would be theoretical and practically unenforceable. Either way, artists will have to adapt.
I don't think the situation is near as dire as so many seem to believe. An AI can only reproduce a style that has been thoroughly explored by the content which it is trained on. New styles will continue to be rewarded. Digital artists will be encouraged to push boundaries. And for the time being, the AIs still have some pretty severe limitations so artists will be able to capitalize on those.
And one more thing I never see brought up when talking about these AI image generators: there's already precedent for how this will play out. AI music composers have been around for many years now, but Dua Lipa and The Weeknd appear to be doing just fine. Even the more classical composers and orchestras seem to be going just as strong as ever. If AI artists show no sign of toppling the music industry, why should we expect the fate of digital images to be so different?
All of those are tools which did not lead to potentially the same final product produced for a miniature fraction of the labor costs.
AI composition is generally pretty shit. If (more like when) it becomes better, we will be having the exact same argument regarding composers.
In what practical sense are paintings distinct from photography or digital art?
It's easy to see how they're technically different, but in terms of purpose paintings should have been supplanted by photography and digital art long ago. After all, the latter two can be reproduced "for a miniature fraction of the labor costs", provide the same utility, and can be reproduced at a much larger scale. Yet painting and similar physical art forms seem to be as economically viable as ever - possibly more than ever, but I'm having trouble finding hard numbers to confidently back that up.
“The fact that it can compress such an enormous quantity of visual information into such a small space is itself a fascinating detail.”
This is not a detail: it’s the principle mechanism. The ability to compress something is conferred by the identification and exploitation of structure, conversely the scarcity or absence of structure inhibits or prohibits compression. You can eyeball check an RNG with compression techniques.
This has counter-intuitive consequences that you can test on your laptop! Even using off-the-shelf codecs it only takes a modest corpus to see that pop music compresses better than eclectic jazz, which compresses better than white noise. The same thing holds for headshots of people: a big pile of headshots drawn from a reasonably broad corpus of humans will enjoy a noticeably lower compression ratio than a subset selected by any plausible “conventional attractiveness” filter. “Conventional attractiveness” (defined any common-sense way) correlates sharply with bilateral symmetry, with obvious implications for storage space.
Information theory is the thread that ties together all this AI craze stuff!