Show HN: This Food Does Not Exist
nyx-ai.github.io
nyx-ai.github.io
Why are these kinds of things impressive?
I think part of my issue is that I don't really "get" these ML projects ("This X does not exist" or perhaps ML in general).
My understanding is that, in layman's terms, workers are shown many, many examples of X and then are asked to "draw"/create X, which they then do. The corollary I can think of is if I were to draw over and over for a billion, billion years and each time a drawing "failed" to capture the essence of a prompt (as deemed by some outside entity), both my drawing, and my memory of it was erased. At the end of that time, my skill in drawing X would be amazing.
_If_ that understanding is correct, it would seem unimpressive? It's not as though I can pass a prompt of "cookie" to an untrained generator and, it pops out a drawing of one. And likewise, any cookie "drawing" generated by a trained model is simply an amalgam of every example cookie.
What am I missing?
I guess it is true that more than a couple demos like this have been shown, so some of the awe might have worn off, but it is still pretty shocking to lots of us that you can describe the general idea of something to a computer and it can figure out and produce "what you mean," fuzzy as that is.
To be clear, I'm not trying to devalue this at all; In fact, as I noted above, I am certain I'm missing something and that was what my comment was aimed at. In any case, thank you for taking the time to reply (seriously).
It's not creativity though. It's a program that arranges pixels in a way that is statistically similar to some training data set. It doesn't "draw" anything, it doesn't "figure out" anything. There is no thought or idea behind it.
The output is mildly interesting but there is no creative act at work, and there's certainly no revolution in the artistic world.
> For the longest time it was assumed that creativity was an almost magically human trait.
Maybe by some people. This is a human-centric perspective, a form of speciesm if I can call it that. Various primates have shown creativity, and various animals have shown the ability to solve problems creatively. Heck, even my cat figured out how to open doors by pulling down on the handle. Humans are likely not more creative than other animals with similar brain size, it's just that there's other factors at play that make it seem like that (such as opposable thumbs and the passing down of knowledge between generations using speech).
So is your perspective :) You essentially take a process we don't understand and can't quantify and say "because I can understand this, this can't be it". You don't know, claiming otherwise is disingenuous.
Yes, you are right, I could be wrong, perhaps this form of statistical imitation is at the core of greater creativity, and not another hype wave in software development. Time will tell.
that's the exact argument made against chess engines - are they "really" playing chess?
What's to say that the brain works different than people imagine creativity to be? That we think we are creative might be an illusion, because the brain is tricking you into thinking that you creatively came up with an original idea, when the reality is that the idea came from a long list of training data that one might've been exposed to all his life.
And who's to say that brute force, and statistical methods of producing content is not creative?
I think this is the crux of my thought on the subject. Thank you.
I absolutely see the kinds of uses it can have (guiding the repair of artwork that was damaged, seeing patterns in huge datasets, etc.), but I think the way it's marketed is that it's somehow coming up with new art, but it's really just "recreating" (on a very micro level) everything in the training set.
You could absolutely argue that creatives are just regurgitating things they've seen before, but I think the big separation between ML and human creativity is that it crosses "genres", if you will. For example, I could be influenced by my experience in a car crash in such a way that it causes me to create X (art, software, music, etc.) in such an abstract way, I'm not sure whether it's even possible to recreate artificially.
>> Heck, even my cat figured out how to open doors by pulling down on the handle.
... your cat can reach the doorknob? Maybe you should make sure to lock the door? O.o
Anyway that's a smart cat.
Let's say a website knows you love chocolate cake, well the website is now going to show you a chocolate cake instead and has generated a unique image of that for you.
And then there's the "in the style of Salvador Dali" aspect.
Even just an image of whatever with a very specific color would be a huge benefit for designers. Because almost all stock photography is trying to appeal to the broadest audience as possible, they all have a similar muted pallette of colors. Which makes stock photography kind of look like stock photography. ML generated images or even ML manipulated images could change that.
This allows for incredible things, like having a world-class artist create a song drawing based on your prompt (i.e. DALL-E) in one second.
The part of a GAN that people use for control is based on an encoder. You put in your 3-dimensional source image and it gets converted to that 1x1px high-dimensional noise in such a way that putting the noise into the encoder will produce your image again.
So the network architecture/structure of a GAN is the exact same structure as you'd use for a denoising autoencoder. You might train that GAN with a different intent, but it's still a stack of upscalers and convolutions that perfectly matches the architecture inside the decoder part of a denoising auto-encoder.
BTW sometimes people also explicitly call this out. The VQ part in VQGAN stands for VQVAE, the "Vector-quantized Variational AutoEncoder". And VQGAN+CLIP is what the first open source DALL-E clones were based on.
The denoising part of a denoising autoencoder refers to the noise applied to its input, the image, not to its latent space as would be the case in the equivalency you establish between the noise distribution used as the input of a GAN's generator and the latent space of an auto-encoder (input of the decoder).
Making a comparison between the generator and a decoder and their respective input spaces has some legs but not much so. An AE's latent space is constrained in an entirely different way with a regularization term that tries to minimize the divergence between the wanted distribution and the learned distribution, that's not a problem you have with a GAN since you simply draw from your wanted distribution every time. These differences lead to learned distributions in the latent space that are entirely different, with different failure modes wrt generalization and interpolation.
Beyond that the comparison just doesn't work, yes there are two networks but the discriminator doesn't play the role of the AE's encoder at all. In addition the learning signal is not the reconstruction of the input but the classification of the generated image. That's a completely different learning process, the learned distribution is different, the gradient path is different, the training is different (much more complex for a GAN), etc... I could go on.
The VQGAN would fall under the clause in my initial comment about GANs which specifically use an AE in their very specific architecture, like the AAE. In the AAE's case they use a discriminator to regularize the latent space of an AE(instead of classifying the generated image), in the VQGAN they use a full autoencoder as generator, in addition to a third network for the discriminator. These do not fit the comparison you're making and do not function in general like most GANs do. So the general comparison of GANs to autoencoders remains inaccurate.
Agree, it converts a noisy image to a denoised image. But the odd thing is, when you put a noisy image into a StyleGAN2 encoder, you get latents which the decoder will turn into a de-noised image. So in practical use, you can take a trained StyleGAN2 encoder/decoder pair and use it as if it was a denoiser. For example https://arxiv.org/abs/2103.04192
> These differences lead to learned distributions in the latent space that are entirely different
I also agree there. The training for a denoising auto-encoder and for a GAN network is different, leading to different distributions which are sampled for generating the images. But the architecture is still very similar, meaning the limits of what can be learned should be the same.
> Beyond that the comparison just doesn't work, yes there are two networks but the discriminator doesn't play the role of the AE's encoder at all
Yes, the discriminator in a GAN won't work like an encoder. But if you look at how StyleGAN 1/2 are used in practice, people combine it with a so-called "projection", which is effectively an encoder to convert images to latents. So people use a pipeline of "image to latent encoder" + "latent to image decoder".
That whole pipeline is very similar to an auto-encoder. For example, here's an NVIDIA paper about how they round-trip from image to latent to image with StyleGAN: https://arxiv.org/abs/1912.04958 My interpretation of what they did in that paper is that they effectively trained a StyleGAN-like model with the image L2 loss typically used for training a denoising auto-encoder.
That could effectively be a lossy compression of the image, exchanging space (the image) with time (cpu).
But the tide never goes out on AI and computing. The capabilities will only grow more and more impressive and unassailable.
When the chatbot is completely convincing is someone going to ask, "I wonder close the responses are to training text" even though no one even blinks when fathers and sons act alike. No one demands children invent new languages to prove they aren't just "randomizing samplers"
I sure as hell do. No son of mine will be comprehensible to other humans until he's at least two years old.
How am I to assess how original the generated results really are?
Also a bit of cultural bias in the training is shown I think. The "pile of cookies" prompt seems to mostly generate American cookies, while e.g. a German user might be disappointed they didn't get this: https://groceryeshop.us/image/cache/data/new_image_2019/ABSB... :)
So both "banana chocolate cookies" and "banacoochoconakieslade" should work.
Try prompting craiyon for "Ein Stapel Kekse"* :)
* Google-translated
https://colab.research.google.com/github/nyx-ai/stylegan2-fl...
While everyone is excited about DALL·E/diffusion models, training those is currently out of reach for most practitioners. Craiyon (formerly DALL·E mega) has been training for months on a huge TPU 256 machine. In comparison our models were each trained in less than 10h on a machine 32x smaller. StyleGAN models also still offer unrivaled photorealism when trained on narrow domains (eg thispersondoesnotexist.com), even though diffusion models are catching up due to massive cash investments in that direction.
That's actually something we're investigating: can we train a single class-conditional model for multiple types of food? Or, can we finetune cheesecakes from cookies?
Is there a chance your dataset provider makes a claim that they have derived data rights over your model generated images? Would you have sufficient confidence, say, to sell your images on a stock image site?
[0] https://law.stackexchange.com/questions/19981/who-can-claim-...
("Picture is only for illustration purposes")
It is an interesting question that there may be some simple pre-processing techniques (edge detection, Fourier transform, etc) that more easily distinguish the image as a fake. Something like a shortcut from training a network to make the distinction.
Something like
from matplotlib import pyplot as plt
plt.imshow(matrix)
plt.show()Here's a PyTorch implementation by the LAION people:
https://github.com/lucidrains/imagen-pytorch
And here's 2 images I sampled after training it for some hours, like 2 hours base model + 4 hours upscaler:
* = Only the unconditional Imagen variant, meaning what they show off here. The variant with a T5 text embedding takes longer to train.
- It's hard to train to a photorealistic quality (we'd be happy to be proven wrong!)
- There is no strong pretrained model available yet
Checking the LAION Discord, the situation doesn't seem to have considerably evolved.
You can't copyright a recipe. It's information that can be freely shared. Anybody can steal it and set up their own site. You can copyright a recipe that comes with a life story. Copyright laws are weird and confusing, but it's really difficult to come up with a better solution.
Nice work.
Anyone can be an artist, musician, photographer, writer.
It's going to result in more content being created, which will change the economies of content. Rate, scale, and volume of production will increase by orders of magnitude.
Disney thinks IP is a war chest. That's an old way of thinking.
Star Wars won't be special to the new kids growing up that can generate "Space Samurai" and "Galaxy Brouhaha" in an afternoon.
We're going to hit a Cambrian explosion of content.
Is it, though? This model took over a month, on extremely fit hardware, to even create.
Lets say for a second, in some hypothetical future, that anyone can access/use/update these models (by anyone, I mean someone with both low amount of resources as well as little to no programming skill), why are they creating content?
"Rate, scale, and volume of production will increase by orders of magnitude."
If by production you mean "paid creation", I'm not so sure about that. In this world where everyone creates content from thin air 1) there is little to no monetary value to the content anymore (as monetary value inversely correlates with scarcity) 2) So there is less incentive to create anything, because there is no monetary value to doing so.
In fact, by definition we can pretty much prove that not much of anything will happen in this regard, because content is already limited by budget - the budget has not gone up, and the return has only gotten worse (in this hypothetical scenario).
What I think is more likely to happen - a few, "blessed" individuals will have out-sized content creation capabilities, without much need to innovate. The rest of us will have almost no incentive to create anything as a result.
Disney will use these systems, and they will use them to churn out more garbage, faster, on average, most kids will not be generating any movies in an afternoon.
> This model took over a month, on extremely fit hardware, to even create.
In a year kids will be training them on colab. This technology is moving so fast, it's jaw dropping. If we went back in time just ten years and showed ourselves this stuff, we'd think it was magic.
> why are they creating content?
Why does anyone post anything at all anywhere ever? Why do people edit photos and videos? Because it's human and people want to communicate.
> there is little to no monetary value to the content anymore (as monetary value inversely correlates with scarcity)
Maybe, but there's still a "search" problem in finding content that activates a response (and attracts the algorithm and views). Look at TikTok and YouTube. High rates of content creation. Immense value in both platforms. Lots of crap, but astoundingly good content mixed in. Enough to keep the whole world glued to it.
> So there is less incentive to create anything, because there is no monetary value to doing so
Nope. TikTok, YouTube, kids making Minecraft mods as a labor of love and hobby.
> content is already limited by budget
Per unit cost goes down by orders of magnitude. I don't see this resulting in less production.
This is a Henry Ford moment.
> What I think is more likely to happen - a few, "blessed" individuals will have out-sized content creation capabilities, without much need to innovate.
The market would disrupt this so fast. There's no ways these tools won't find themselves in the hands of everyone.
> Disney will use these systems, and they will use them to churn out more garbage, faster, on average, most kids will not be generating any movies in an afternoon.
Kids already make their own narratives in VRChat, Minecraft, and Roblox. The next systems will be a holy grail for them.
I'm interested in looking back on this thread in ten years. I think my predictions are correct.
I think you'll find the majority of the artwork community still favors, pen and paper, pencil (maybe oils). They do this because, at least in part, they appreciate the challenge, it makes them better as people, in some fashion.
I use tablet, sure, some use 3D modeling, even. People don't create pencil drawing because they are good, or sell, they do it for personal reasons. I think you're assuming this is going to solve some problem, it's really not - the people interested in content creation for content creations' sake are not likely to benefit.
> Nope. TikTok, YouTube, kids making Minecraft mods as a labor of love and hobby.
You're assuming because content platforms like TikTok exist now, that something similar must continue into the future... I see this as a flawed assumption. Regarding monetization, my point hold well here, though. For every "successful" Youtuber raking in millions, there are hundreds, thousands for whom it just didn't work out.
The content on these platforms is already often mundane, you don't see the promised plethora of content creators, because the system for the most part favors the few, not the many. Sure, everyone can upload, that doesn't mean most people do.
> If we went back in time just ten years and showed ourselves this stuff, we'd think it was magic.
I remember ten+ years ago, there was content on the web. Now, it's extremely centralized, most of it is not that good. So yeah I can time travel a bit, and things got worse. The reasons for this aren't all AI, but as trends go creating a program to do photoshop or video automatically isn't going to make things better. You're not solving the right problem here, and the people who stand to benefit are not the people you seem to think will.
A field that has existed for centuries versus something in its larval stage that is still growing legs.
Calculators reached more than slide rule users.
> They do this because, at least in part, they appreciate the challenge, it makes them better as people, in some fashion.
I would love to do art, but life is full of opportunity cost. You can't do everything.
This technology will enable so many people to create art without having to devote thousands of hours to practice. That's a good thing!
I'm sure many artists used to say the same to artists that utilized tablets when they first came about. Or C programmers joking about early Perl and Javascript programmers.
It's rhymes a bit with the Luddite argument.
We're seeing the emergence of new workflows. Few people hand wash their clothes anymore, and we're not worse for it.
> the people interested in content creation for content creations' sake are not likely to benefit.
I'm telling you that I would directly benefit. I love creating content, but I can't draw. I don't have the time to learn. These tools will empower me, and they'll only get better with time.
> For every "successful" Youtuber raking in millions, there are hundreds, thousands for whom it just didn't work out
There are more people making money creating content now than in any time in history. These tools and platforms have democratized access. People now have a path where there wasn't one before.
These platforms and tools don't, however, remove the requirement to be good at what you do and to find, understand, cater to, and grow your audience.
> Sure, everyone can upload, that doesn't mean most people do.
90% rule. There are still more creators today than in the past, and the field will only grow from here.
> Now, it's extremely centralized
That's orthogonal. I agree with you about platforms and centralization being a bit of a problem, but for what it's worth, the platforms do make it easier for most people.
This, as you put it, orthogonal. The question is not whether some small set of individuals, such as yourself stand to gain or not. That is in fact what I surmise a very few will actually gain, on the whole this is not a boon.
Other orthogonal arguments could be made regarding other orthogonal avenues you have brought up e.g. Javascript being good (terrible for the environment, in part responsible for a large amount of e-waste).
Bringing the term luddite into the equation rhymes something with sensationalist....
Being not OK with inappropriate technological uses is not synonymous with being anti technology, tools have purpose, but if you murder someone with a knife and then claim people are luddite for decrying your actions, because progress and being able to cut things...
>These tools and platforms have democratized access.
Or at least have created the illusion of such.
> I don't have the time to learn.
No pain no gain, as the saying goes. I don't think this will even be as useful to yourself as you think, you will as you put it, need to still be good, but your unwillingness to put effort into something you otherwise claim to value indicates you are unlikely to benefit at all, your work is likely to be subpar compared to those of your peers.
It's OK to not be good at everything, and rely on others, that's the foundation of a society. But claiming you will benefit from something that a tool does is like claiming that you will become rich from hammers putting nails into wood - sure we benefit from not smashing the nails into wood ourselves, maybe society benefits from better constructed houses, even, but the hammer itself does not make you a carpenter or even a construction worker. It will not benefit you in that fashion.
Which is my point, the tool in question does not democratize making movies, I bring relevant issues in the realm of current video distribution, and you want to claim it is orthogonal... I'm not sure you understand just how much it won't benefit you...
Another way to think about it, if something is a problem, e.g. "I cannot draw", think "Maybe it's much worse than that", and you might begin to uncover the root causes of your issue. Acting prematurely, much like premature optimization, is the root of all evils, if you will. To bring it back to topic, if you create something like an image AI without properly examining what it is you are trying to accomplish, you are liable to create more harm than good. A lot of tech is like this, knee jerk scratch-an-itch, and it shows. Not an Apple fan, but they are a good example in industry, they took time to design well and the result is a more stable, more power efficient system. Android, as much as I love the platform, suffers from a million knee jerk decisions across industry, resulting in a much higher proportion of digital garbage, some virtual some literal landfill.
That's an astounding number of people making content at a level of sophistication they wouldn't have been able to pull off before.
"Once there were parking lots, now it's a peaceful oasis. This was a Pizza Hut, now it's all covered with daisies."
"Green grass grows around the backyard shit house. And that is where the sweetest flowers bloom."
"This ain't no party, this ain't no disco, this ain't no fooling around."
If you cannot distinguish reality (well), and in fact it becomes possible that most things you see do not exist, then there is nothing to stop a bad actor from producing a fake version of events in which they are elected, control everything, etc.
So, democracy would cease to exist, because democracy relies ultimately on a choice - if you have no choice then you do not have democracy, only a dictatorship.
I think it could be claimed that we're clearly in the process of learning to distrust. People distrust media more now than ever [1], because they've demonstrated, again and again, that they can't be trusted. Trust in institutions are down to new lows [2], because they've also shown that they're untrustworthy and/or incompetent. Everyone is shouting "disinformation", "misinformation", and "fake news" constantly, because nobody trusts anything anymore, and rightfully so in many cases.
1. https://news.gallup.com/poll/355526/americans-trust-media-di...
2. https://news.gallup.com/poll/394283/confidence-institutions-...
Otherwise, I fully agree with your point.
Then everyone quickly realizes it's all garbage. The bigger danger is when people think garbage isn't possible, which has been the case since Stalin erased people from photos 98 years ago.
People already have trouble telling propaganda from fact. That has been going on since forever.
At the end of the day I don't see this being a game changer. If anything, now video and photos are less evidence for/against something as the potential falseness becomes well known. Congressman X: "no, that wasn't me you saw leaving the hotel with the prostitute, my slimy opponent obviously is deep faking stuff".
And people will continue to believe what they want to believe, in spite of all evidence to the contrary, just like they do right now.
An environment in which 90% of the information is adversarial is really bad. It's a severe problem and very challenging to navigate. An environment where 99.9999% of it's adversarial and it's even harder than before to sort truth from fiction, functionally no longer has any flow of real information whatsoever.
Maybe liberal democracy is not the final outcome of human civilization. You like it and I like it (presumably we were both raised to believe this way) but perhaps it's not really true.
Just to question a base assumption here.
It seems to me, if all the things that are claimed to threaten liberal democracy actually do, liberal democracy might be much less robust and long lived then previously believed.
[EDIT] One thing I no longer think has any realistic future is the open, semi-anonymous Internet. We're either losing it because despots take over and definitely won't permit that threat to remain unfettered, or we're losing it (in perhaps a gentler-touch way) because we have to to prevent authoritarian take-over and vast civil strife. I don't think we're getting to keep that no matter what happens.
Even the "good guys" will call emergency that will never end.
Oh well, it was nice while it lasted. An intellectual Cambrian explosion. And all that porn!
On that note: I can't wait for the resulting proliferation of photorealistic tentacle hentai. Imagine the possibilities!
If I see a photo in the Guardian newspaper (or any other reputable news outfit) I'm going to presume it's real, and I expect journalists to verify that for me. If I see a random photo that doesn't look quite right on a 4chan, I'm not going to immediately assume it's news.
Scale, cost, and reach.
This was not an impediment to liberal democracy.
I am as concerned as the next guy but throwing the towel already seems a bit premature?
You can run a web server by responding to every request by hand-typing the response, too. But you couldn't realistically run one-one-millionth the modern Web that way. You can't have global-scale e-commerce that way, et c. Some things that technically could work that way, can't actually—it's too slow, too expensive. This is very much one of those "quantity has a quality all its own" things. Increase the productivity of every astroturf-poster or propaganda-front-news-site manager a few hundred times and that's a big difference.
> I am as concerned as the next guy but throwing the towel already seems a bit premature?
Where'd you get throwing in the towel? I do think we're (especially the US) really unlikely to do what we need to in time, in part because measures that are probably necessary to defend against this are themselves risky and rather unappealing. But we might.
If you don't trust anything, you ... don't trust anything.
How can institutions survive? We're already living through a moment where large swaths of the population cannot get a fix on the same reality as their neighbors.
I would argue that this is happening precisely because the gullibles are being led into playing skeptics.
I believe this phenomenon was way more severe in the past. The reason you notice it now is because these meme cones are rubbing up against each other in public forums. In the past, isolated communities wallowed in their own wacky shit totally unchallenged. Pick any major religious movement of the 19th century and tell me they had a firm grasp on reality?
Take this site, even. Ok, it's less cult-y as more people learn of it, but in the past there wouldn't have been anything close to the sort of community you can run into here. Maybe I've run into one, two people in real life as "in the know" as what you typically find here...
> The reason you notice it now is because these meme cones are rubbing up against each other in public forums
Yeah, it is true the amplification effect makes crazy stuff like the church of Scientology public knowledge, but there's also just that there's too much noise to keep track of all the special little brands of cult-ish "crazy". It's really hard to gauge the effect here, unless you have a study perhaps you could link? That would be quite on topic here.
1. AI lets you generate an enormous number of lies, but what is really dangerous is one well-placed lie within a trusted stream of many truths. CNN will retain a power to mislead far in excess of Twitter bots.
2. Democracy averages over everyone's confusion, which means lies are only dangerous when large numbers of people believe them at the same time. Hordes of bots generating spurious lies won't move a democracy in any specific direction, but again, mass media will retain its power to mislead everyone at once in the same, effectual, direction.
3. People have never respected the veracity of random tweets. In the same way that trust in mainstream media outlets is reaching record lows due to their consistently biased reporting (they might not all have the same bias, but I can't think of any I'd consider free of bias), everyone will learn to adjust their incredulity to match the true quality of random tweets.
4. Companies like Twitter and Google are known to be shaping their results and algorithms according to their own "political views" (broadly construed) so at worst this would represent a partial shift of power from the old masters to new masters quite like them (social media companies). In many ways trimming the front page to reflect editorial opinion is echoed in the way Twitter trims its feeds to reflect their own editorial opinions.
All taken together, it seems like the media is afraid that equally large companies with similar business models (content, attention, advertising) might end up eclipsing them. The same old model where the TV station is afraid to upset its advertisers, thereby giving a voice to business interests, is well-recorded in the recent history of YouTube. Not so much will change, although seeing it in its old and newer forms might shed light on how it works.
The major mistake here is thinking that these two things are mutually exclusive competition. They're not.
In 2015, an internet rumor started spreading that Obama was going to invade Texas. This rumor was based on the fact that there was a routine military training exercise taking place in Bastrop called Jade Helm. It started on Facebook and was quickly picked up by the mass media. These rumors became so viral that the Texas governor activated the Texas State Guard "just in case".[1]
Later, the US government alleged that this was a planned disinformation operation carried out by the Russians and was a precursor to later operations.[0]
Twitter and Facebook started the conspiracy, but mass media laundered it and made it look real enough to get a governor to act.
0: https://www.kut.org/politics/2018-05-03/jade-helm-conspiracy...
1: https://www.texasmonthly.com/news-politics/russians-sowed-di...
Even if this is released quickly, the internet would be flooded with crazy images, and people would quickly learn to have a critical eye to the media that they see, which should already be the case, because photorealistic fiction is already possible, as everyone experiences with every blockbuster movie. Only a slight shift in perspective is required to realize that every image and video you see aren't any different than frames/clips of Thor: Love And Thunder.
But on the other hand, Adobe is pushing their CAI hard:
https://helpx.adobe.com/photoshop/using/content-credentials....
And the core benefit of "authentic" content is that it can't be generated by an AI. Only humans can own copyright.
Have we reached a point where we've bounded art within the data models are trained on?
Have we imposed a limit on ideas as a realm of "what came before" and implicitly decided that any "after" is a pointless exercise without knowing whether that's even true?
A more accurate title would be "This picture of food does not exist."
Photographers in general, certainly, but no individual photographer enough that it could ever matter, legally or ethically.
> implicitly decided that any "after" is a pointless exercise...
Seems a little pessimistic. I believe that we will still be able to recognize and appreciate new art.
In the photographers example, each photographer is fractionally damaged, with the more photographers the less the damage.
Similarly, GPT3 is ripping off the words of everyone who has ever written anything on the internet, but to such a miniscule degree that none of us could ever have cause for complaint.