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> What will save it is that, no matter how picky you are as a creator, your audience will never know what exactly was that you dreamed up, so any half-decent ap

> What will save it is that, no matter how picky you are as a creator, your audience will never know what exactly was that you dreamed up, so any half-decent approximation will work. Part of the problem is the "half decent approximations" tend towards a clichéd average, the audience won't know that the cool cyberpunk cityscape you generated isn't exactly what you had in mind, but they will know that it looks like every other AI generated cyberpunk cityscape and mentally file your creation in the slop folder. I think the pursuit of fidelity has made the models less creative over time, they make fewer glaring mistakes like giving people six fingers but their output is ever more homogenized and interchangable.

randomcatuser 2y ago
a somewhat counterintuitive argument is this: AI models will make the overall creative landscape more diverse and interesting, ie, less "average"! Imagine the space of ideas as a circle, with stuff in the middle being more easy to reach (the "cliched average"). Previously, traversing the circle was incredibly hard - we had to use tools like DeviantArt, Instragram, etc to agglomerate the diverse tastes of artists, hoping to find or create the style we're looking for. Creating the same art style is hiring the artist. As a result, on average, what you see is the result of huge amounts of human curation, effort, and branding teams. Now reduce the effort 1000x, and all of a sudden, it's incredibly easy to reach the edge of the circle (or closer to it). Sure, we might still miss some things at the very outer edge, but it's equivalent to building roads. Motorists appear, people with no time to sit down and spend 10000 hours to learn and master a particular style can simply remix art and create things wildly beyond their manual capabilities. As a result, the amount of content in the infosphere skyrockets, the tastemaking velocity accelerates, and you end up with a more interesting infosphere than you're used to.
- wongarsu 2y ago
  And as AI oversaturates the cliched average, creators will have to get further and further away from the average to differentiate themselves. If you pour a lot of work into your creation you want to make it clear that it isn't some cliched AI drivel.
  - skydhash 2y ago
    You will basically have to provide a video showcasing your workflow.
    - krainboltgreene 2y ago
      I promise you that the artists can outlive the VC money.
- TeMPOraL 2y ago
  To extend the analogy, imagine the circle as a probability distribution; for simplicity, imagine it's a bivariate normal joint distribution (aka. Gaussian in 3D) + some noise, and you're above it and looking down. When you're commissioning an artist to make you some art, you're basically sampling from the entire distribution. Stuff in the middle is, as you say, easiest to reach, so that's what you'll most likely get. Generative models let more people do art, meaning there's more sampling happening, so the stuff further from the centre will be visited more often, too. However, AI tools also make another thing easier: moving and narrowing the sampling area. Much like with a very good human artist, you can find some work that's "out there", and ask for variations of it. However, there are only so many good artists to go around. AI making this process much easier and more accessible means more exploration of the circle's edges will happen. Not just "more like this weird thing", but also combinations of 2, 3, 4, N distinct weird things. So in a way, I feel that AI tools will surface creative art disproportionally more than it'll boost the common case. Well, except for the fly in the ointment that's the advertising industry (aka. the cancer on modern society). Unfortunately, by far most of the creative output of humanity today is done for advertising purposes, and that goal favors the common, as it maximizes the audience (and is least off-putting). Deluge of AI slop is unavoidable, because slop is how the digital world makes money, and generative AI models make it cheaper than generative protein models that did it so far. Don't blame AI research for that, blame advertising.
  - etiam 2y ago
    I like the picture, but I'd be more impressed with the exploration argument if we were collectively actually doing a good job giving recognition to original and substantial works that already exist. It'd be of greater service in that regard to create a high-quality artificial stand-in for that limited-quantity "attention" and "engagement" all the bloodsuckers seem so keen on harvesting. (And I do blame the advertisers, but frankly anyone handing them new amplifiers, with entirely predictable consequences, is also not blameless.)
  - js8 2y ago
    I read this argument/analogy and the "AI slop will win" idea reminds me of the idea that "fake news will win". That is based on perception that it is easier than ever to create fake content, but fails to account for the fact that creating real content (for example, simply taking a video) is even much easier. So while there is more fake content, there is also lot more real content, and so manipulation of reality (for example, denying a genocide) is much harder today than ever. Anyway, "the AI slop will win" is based on a similar misconception, that total creative output will not increase. But like with fake news, it probably will not be the case, and so the actual amount of good art will increase, too. I think we are OK as long as normal humans prefer to create real news rather than fake news, and create innovative art rather than cliched art.
- robertlagrant 2y ago
  It's just like when Bootstrap came out. Terrible-looking websites stopped appearing, but so did beautiful websites.
TeMPOraL 2y ago
> I think the pursuit of fidelity has made the models less creative over time (...) their output is ever more homogenized and interchangable. Ironically, we're long past that point with human creators, at least when it comes to movies and games. Take sci-fi movies, compare modern ones to the ones from the tail end of the 20th century. Year by year, VFX gets more and more detailed (and expensive) - more and better lights, finer details on every material, more stuff moving and emitting lights, etc. But all that effort arguably killed immersion and believability, by making scenes incomprehensible. There's way too much visual noise in action scenes in particular - bullets and lighting bolts zip around, and all that detail just blurs together. Contrast the 20th century productions - textures weren't as refined, but you could at least tell who's shooting who and when. Or take video games, where all that graphics works makes everything look the same. Especially games that go for realistic style, they're all homogenous these days, and it's all cheap plastic. (Seriously, what the fuck went wrong here? All that talk, and research, and work into "physically based rendering", yet in the end, all PBR materials end up looking like painted plastic. Raytracing seems to help a bit when it comes to liquids, but it still can't seem to make metals look like metals and not Fischer-Price toys repainted to gray.) So I guess in this way, more precision just makes the audience give up entirely. > they will know that it looks like every other AI generated cyberpunk cityscape and mentally file your creation in the slop folder. The answer here is the same as with human-produced slop: don't. People are good at spotting patterns, so keep adding those low-order bits until it's no longer obvious you're doing the same thing everyone else is. EDIT: Also, obligatory reminder that generative models don't give you average of training data with some noise mixed up; they sample from learned distribution. Law of large numbers apply, but it just means that to get more creative output, you need to bias the sampling.
- wongarsu 2y ago
  Video games (the much larger industry of the two, by revenue) seems to be closer to understanding this. AAA games dominate advertising and news cycles, but on any best-seller list AAA games are on par with indie and B games (I think they call them AA now?). For every successful $60M PBR-rendered Unreal 5 title there is an equally successful game with low-fidelity graphics but exceptional art direction, story or gameplay. Western movie studios may discover the same thing soon, with the number of high-budget productions tanking lately.
samatman 2y ago
Empirically, we've passed the point where that's true, for someone not being lazy about it. https://www.astralcodexten.com/p/how-did-you-do-on-the-ai-art-turing https://www.astralcodexten.com/p/how-did-you-do-on-the-ai-ar... In other words, someone willing to tweak the prompt and press the button enough times to say "yeah, that one, that's really good" is going to have a result which cannot in fact be reliably binned as AI-generated.
- lmm 2y ago
  I mean, no? None of the AI-generated images managed to be indistinguishable. Some people were much better than others at spotting the differences. He even quotes, at length, an artist giving a detailed breakdown of what's wrong with one of the images he thought was good.
  - TeMPOraL 2y ago
    Did you read the article? Respondents performed barely better than chance. Sure, no one was actually 100% wrong[0]. Just almost always wrong, with a noticeable bias towards liking AI art more. The detailed breakdown you mention? Maybe it's accurate to that artist's thought process, maybe it's more of a rationalization; either way, it's not a general rule they, or anyone, could apply to any of the other AI images. Most of those in the article don't exhibit those "telltale signs", and the one that does - the Victorian Megaship - was actually made by human artist with no AI in the mix. EDIT: Another image that stands out to me is Riverside Cafe. Myself, like apparently a lot of other people, going by articles' comments, assumed it's a human-made one, because we vaguely remembered Vang Gogh painted something like it. He did, it's called Café Terrace at Night - and yet, despite immediately evoking the association, Riverside Cafe was made by AI, and is actually nothing like Café Terrace at Night at any level. (I find it fascinating how this work looks like a copy of Van Gogh at first glance, for no obvious reason, but nothing alike once you pause to look closer. It's like... they have similar low-frequency spectra or something?) EDIT2: Played around with the two images in https://ejectamenta.com/imaging-experiments/fourifier/ https://ejectamenta.com/imaging-experiments/fourifier/. There are some similarities in the spectra, I can't put my finger on them exactly. But it's probably not the whole answer. I'll try to do some more detailed experimentation later. -- [0] - Nor should you expect it - it would mean either a perfect calibration, or be the equivalent of flipping a coin and getting heads 30 times in a row; it's not impossible, but you shouldn't expect to see it unless you're interviewing fewer people than literally the entire population of the planet.
dragonwriter 2y ago
> I think the pursuit of fidelity has made the models less creative over time, they make fewer glaring mistakes like giving people six fingers but their output is ever more homogenized and interchangeable. That may be true of any one model (though I don’t think it really is, either, I think newer image gen models are individually capable of a much wider array of styles than earlier models), but it is pretty clearly not true of the whole range of available models, even if you look at a single model “family” like “SDXL derivatives”.

randomcatuser 2y ago

a somewhat counterintuitive argument is this: AI models will make the overall creative landscape more diverse and interesting, ie, less "average"! Imagine the space of ideas as a circle, with stuff in the middle being more easy to reach (the "cliched average"). Previously, traversing the circle was incredibly hard - we had to use tools like DeviantArt, Instragram, etc to agglomerate the diverse tastes of artists, hoping to find or create the style we're looking for. Creating the same art style is hiring the artist. As a result, on average, what you see is the result of huge amounts of human curation, effort, and branding teams. Now reduce the effort 1000x, and all of a sudden, it's incredibly easy to reach the edge of the circle (or closer to it). Sure, we might still miss some things at the very outer edge, but it's equivalent to building roads. Motorists appear, people with no time to sit down and spend 10000 hours to learn and master a particular style can simply remix art and create things wildly beyond their manual capabilities. As a result, the amount of content in the infosphere skyrockets, the tastemaking velocity accelerates, and you end up with a more interesting infosphere than you're used to.

wongarsu 2y ago
And as AI oversaturates the cliched average, creators will have to get further and further away from the average to differentiate themselves. If you pour a lot of work into your creation you want to make it clear that it isn't some cliched AI drivel.
- skydhash 2y ago
  You will basically have to provide a video showcasing your workflow.
  - krainboltgreene 2y ago
    I promise you that the artists can outlive the VC money.
TeMPOraL 2y ago
To extend the analogy, imagine the circle as a probability distribution; for simplicity, imagine it's a bivariate normal joint distribution (aka. Gaussian in 3D) + some noise, and you're above it and looking down. When you're commissioning an artist to make you some art, you're basically sampling from the entire distribution. Stuff in the middle is, as you say, easiest to reach, so that's what you'll most likely get. Generative models let more people do art, meaning there's more sampling happening, so the stuff further from the centre will be visited more often, too. However, AI tools also make another thing easier: moving and narrowing the sampling area. Much like with a very good human artist, you can find some work that's "out there", and ask for variations of it. However, there are only so many good artists to go around. AI making this process much easier and more accessible means more exploration of the circle's edges will happen. Not just "more like this weird thing", but also combinations of 2, 3, 4, N distinct weird things. So in a way, I feel that AI tools will surface creative art disproportionally more than it'll boost the common case. Well, except for the fly in the ointment that's the advertising industry (aka. the cancer on modern society). Unfortunately, by far most of the creative output of humanity today is done for advertising purposes, and that goal favors the common, as it maximizes the audience (and is least off-putting). Deluge of AI slop is unavoidable, because slop is how the digital world makes money, and generative AI models make it cheaper than generative protein models that did it so far. Don't blame AI research for that, blame advertising.
- etiam 2y ago
  I like the picture, but I'd be more impressed with the exploration argument if we were collectively actually doing a good job giving recognition to original and substantial works that already exist. It'd be of greater service in that regard to create a high-quality artificial stand-in for that limited-quantity "attention" and "engagement" all the bloodsuckers seem so keen on harvesting. (And I do blame the advertisers, but frankly anyone handing them new amplifiers, with entirely predictable consequences, is also not blameless.)
- js8 2y ago
  I read this argument/analogy and the "AI slop will win" idea reminds me of the idea that "fake news will win". That is based on perception that it is easier than ever to create fake content, but fails to account for the fact that creating real content (for example, simply taking a video) is even much easier. So while there is more fake content, there is also lot more real content, and so manipulation of reality (for example, denying a genocide) is much harder today than ever. Anyway, "the AI slop will win" is based on a similar misconception, that total creative output will not increase. But like with fake news, it probably will not be the case, and so the actual amount of good art will increase, too. I think we are OK as long as normal humans prefer to create real news rather than fake news, and create innovative art rather than cliched art.
robertlagrant 2y ago
It's just like when Bootstrap came out. Terrible-looking websites stopped appearing, but so did beautiful websites.

TeMPOraL 2y ago

> I think the pursuit of fidelity has made the models less creative over time (...) their output is ever more homogenized and interchangable. Ironically, we're long past that point with human creators, at least when it comes to movies and games. Take sci-fi movies, compare modern ones to the ones from the tail end of the 20th century. Year by year, VFX gets more and more detailed (and expensive) - more and better lights, finer details on every material, more stuff moving and emitting lights, etc. But all that effort arguably killed immersion and believability, by making scenes incomprehensible. There's way too much visual noise in action scenes in particular - bullets and lighting bolts zip around, and all that detail just blurs together. Contrast the 20th century productions - textures weren't as refined, but you could at least tell who's shooting who and when. Or take video games, where all that graphics works makes everything look the same. Especially games that go for realistic style, they're all homogenous these days, and it's all cheap plastic. (Seriously, what the fuck went wrong here? All that talk, and research, and work into "physically based rendering", yet in the end, all PBR materials end up looking like painted plastic. Raytracing seems to help a bit when it comes to liquids, but it still can't seem to make metals look like metals and not Fischer-Price toys repainted to gray.) So I guess in this way, more precision just makes the audience give up entirely. > they will know that it looks like every other AI generated cyberpunk cityscape and mentally file your creation in the slop folder. The answer here is the same as with human-produced slop: don't. People are good at spotting patterns, so keep adding those low-order bits until it's no longer obvious you're doing the same thing everyone else is. EDIT: Also, obligatory reminder that generative models don't give you average of training data with some noise mixed up; they sample from learned distribution. Law of large numbers apply, but it just means that to get more creative output, you need to bias the sampling.

wongarsu 2y ago
Video games (the much larger industry of the two, by revenue) seems to be closer to understanding this. AAA games dominate advertising and news cycles, but on any best-seller list AAA games are on par with indie and B games (I think they call them AA now?). For every successful $60M PBR-rendered Unreal 5 title there is an equally successful game with low-fidelity graphics but exceptional art direction, story or gameplay. Western movie studios may discover the same thing soon, with the number of high-budget productions tanking lately.

samatman 2y ago

Empirically, we've passed the point where that's true, for someone not being lazy about it. https://www.astralcodexten.com/p/how-did-you-do-on-the-ai-art-turing https://www.astralcodexten.com/p/how-did-you-do-on-the-ai-ar... In other words, someone willing to tweak the prompt and press the button enough times to say "yeah, that one, that's really good" is going to have a result which cannot in fact be reliably binned as AI-generated.

lmm 2y ago
I mean, no? None of the AI-generated images managed to be indistinguishable. Some people were much better than others at spotting the differences. He even quotes, at length, an artist giving a detailed breakdown of what's wrong with one of the images he thought was good.
- TeMPOraL 2y ago
  Did you read the article? Respondents performed barely better than chance. Sure, no one was actually 100% wrong[0]. Just almost always wrong, with a noticeable bias towards liking AI art more. The detailed breakdown you mention? Maybe it's accurate to that artist's thought process, maybe it's more of a rationalization; either way, it's not a general rule they, or anyone, could apply to any of the other AI images. Most of those in the article don't exhibit those "telltale signs", and the one that does - the Victorian Megaship - was actually made by human artist with no AI in the mix. EDIT: Another image that stands out to me is Riverside Cafe. Myself, like apparently a lot of other people, going by articles' comments, assumed it's a human-made one, because we vaguely remembered Vang Gogh painted something like it. He did, it's called Café Terrace at Night - and yet, despite immediately evoking the association, Riverside Cafe was made by AI, and is actually nothing like Café Terrace at Night at any level. (I find it fascinating how this work looks like a copy of Van Gogh at first glance, for no obvious reason, but nothing alike once you pause to look closer. It's like... they have similar low-frequency spectra or something?) EDIT2: Played around with the two images in https://ejectamenta.com/imaging-experiments/fourifier/ https://ejectamenta.com/imaging-experiments/fourifier/. There are some similarities in the spectra, I can't put my finger on them exactly. But it's probably not the whole answer. I'll try to do some more detailed experimentation later. -- [0] - Nor should you expect it - it would mean either a perfect calibration, or be the equivalent of flipping a coin and getting heads 30 times in a row; it's not impossible, but you shouldn't expect to see it unless you're interviewing fewer people than literally the entire population of the planet.

dragonwriter 2y ago

> I think the pursuit of fidelity has made the models less creative over time, they make fewer glaring mistakes like giving people six fingers but their output is ever more homogenized and interchangeable. That may be true of any one model (though I don’t think it really is, either, I think newer image gen models are individually capable of a much wider array of styles than earlier models), but it is pretty clearly not true of the whole range of available models, even if you look at a single model “family” like “SDXL derivatives”.