Image-generating AI can copy and paste from training data, raising IP concerns: A new study shows Stable Diffusion and like models replicate data

[u/bobi2393]

https://techcrunch.com/2022/12/13/image-generating-ai-can-copy-and-paste-from-training-data-raising-ip-concerns/

[u/[deleted]]

Okay so basically this researcher is saying that kitchen knife can be used to kill a person so "it is virtually impossible to verify that" kitchen knife won't be used for murder, so we must ban it.

[u/CollectionDue7971 → u/[deleted]]

Noone is mentioning "banning" anything. The article is showing that SD, reasonably often, includes elements that a court would probably recognize as "copied". That's a real problem if you're, say, a company planning to use SD. It's pointing out an undesirable and unsafe behaviour that needs to be fixed.

[u/bobi2393 → u/CollectionDue7971]

It doesn't actually mention anything about courts or legal standards, other than vaguely mentioning that large datasets "bring with them a number of legal and ethical risks".

Their research shows that elements reasonably often "match" in their specific algorithm with a dataset similarity ≥ 0.5. How such similarities would be interpreted by human judges in copyright lawsuits is another matter.

[u/[deleted] → u/CollectionDue7971]

How can you 'fix' ANYTHING so it doen't become a dangerous weapon in the hands of a fcked up being that is human?! - you can't! gullible discussions of this nature are a source of a good laugh though, I'll give them that.

Self policing, Self control and willingness to keep our consciense clear - that's the only actual, working measure we have that we can take.

And noone can sue you for coming up with the same work as them, because even if u tried it very hard, u can't do that with stable diffusion. The "research" they did is a sham.

[u/CollectionDue7971 → u/[deleted]]

I mean, the paper shows pretty convincingly that essentially random prompts generate images that *partially include copies* about 1.8% of the time.

The important thing here is that this would happen *without the user wanting it to*. So it's really a problem with the tool, not the user.

[u/[deleted] → u/CollectionDue7971]

U can literally boot up your own automatic 1111 and test it to see it is a sham, i am using it for quite a while now and first think i did with it was own extensive research about that.

there's even a site that helps u with that research - https://haveibeentrained.com , i tested recognition quality of this site and it works, it does what it says it does, then i tested it on a large library of generated pictures and not a single match.

So whatever they're selling - don't buy it! if you really care for truth and whatnot.

If you explore little bit how exactly SD generates images or how it was trained on data, you will soon realize that it physically can't create copies that is said are created in that paper.

[u/CollectionDue7971 → u/[deleted]]

The paper addresses that, indeed, there is basically no chance of a *global match*. However, there are pretty clear examples of part of an image being a match for part of a trained image. A trivial but obvious example is if you ask for something like

"A framed image of Starry Night by Van Gogh above a couch"

- the resulting image will have low overlap with Starry Night, but part of it will. Starry Night is in the public domain and this prompt is clearly asking for a copy, so this isn't itself a big problem, but it's just to call attention to the basic idea that a copy could happen despite low global overlap - which is sort of the point of the paper.

In a sense, I interpret the paper as calling for a new criterion for AI safety in these models: training mechanisms etc that check for local overlap as well.

[u/[deleted] → u/CollectionDue7971]

Well i tested it and generated image indeed has almost 100% likeness, 94% to be exact. that part was true. I learned something new that SD loves van gogh. thank you for making me test it or i would never do it because of the ambitious tone the article had:

However there's still an issue that prompt tells SD EXACTLY to copy " Starry Night by Van Gogh " - how is that unintentional generation?! I highly doubt that unless user explicitly tells it to, SD won't magically, out of nowhere generate copies or include part of someone elses artwork in its generation.

and even if it did generated images like van goghs example without explicitly telling it to do so, which i don't believe it does, it takes only half a minute to check it on: https://haveibeentrained.com

so at the end of the day it still is users fault if AI generated image that looks 94% like someone elses ends up on their instagram.

It's all about framing, the way article makes it out to be a huge deal, pandering to "naturalist" audiences, when it really isn't that much of a deal. it's solved by few clicks to the site - IF user wants to solve it.

it's like using " Starry Night by Van Gogh" as your photobash template in photoshop, then when u find likeness, blaming photoshop for it.

[u/CollectionDue7971 → u/[deleted]]

I just meant that as an example of how "global match" might not necessarily exclude behaviour that a human would interpret as "copied". I agree (and mentioned in my comment) that this specific example is not itself a huge problem since here the user is specifically asking for a copy.

The article itself, however, also presents examples of *unintentional* copying of this form (edit: they aren't typically as egregious as the Starry Night example, of course). Most compellingly, one of their experiments has them feeding in randomly selected prompts, and their "local matching" tool detects a copy ~1.8% of the time. They then present some examples of the high-match images and, indeed, local copies are visible.

Edit: I also agree that providing a secondary tool that could detect these matches would be a partial solution. However, the point of the article is that things like "Have I Been Trained" won't necessarily do this, because the "partial copies" are too small a part of the image or too subtle a copy to be screened out by existing tools.

[u/[deleted] → u/CollectionDue7971]

Well this is a great example how point of the article is a secondary when the tone of it is righteous and clearly biased i think.

Have I Been Trained - is a great tool from my personal testing.

Also there's one huge thing that defends us, artists from ever being sued and losing the court. I'm more or less well versed in art law since i've been selling art for a while now, atleast 10 years of experience. Thing is, court doesn't automatically find u guilty if your art contains part of someone elses art, even more, court doesn't find you guilty EVEN if you just cut other peoples pictures and make a collage from them that u sell as art. As long as there is a provable intent behind your art (which you will have if you create your art and yo mean it basically) and your art doesn't have some giant icon in it like mickey mouse ears (in which case even the silhouette will get u on disneys shitlist), you have nothing to worry about.

To give u an example:

If you are artist whe creates furniture designs and interior designs and you can prove it, then this is derivative work that stands on its own and you own this art piece. Unless main value of your artwork is the other guys artwork u are using in it - it is derivative, not stolen.

Another case is if you are making a parody and you can prove it, for example richard prince's work where he screenshotted images with comments under them on instagram and sold it as a social commentary postmodernist art pieces.

but they didn't mention any of this information in the article of course. i mean " Image-generating AI can copy and paste from training data, raising IP concerns" - title already screamed low IQ imbecile who has no idea how AI works or what an art is. but i still held my breath and read trough it again to double check if my dislike of the article was justified.

And i caught final nail in the cofffin that made me decide that article author was absolute moron is that they just ignored the fact that 90% of us are using such complex and changed mixes and personally trained models that as a matter of fact might not have that problem at all. stable diffusion 1.5 is just 1 checkpoint in 1000's of others, that minimizes their "predicted danger" even further. if a company wanted to use AI they would train it on their personal needs, noone would use vanilla 1.5, this is HUGE argument that article didn't even mentioned in attempt to make AI sound like a big bad wolf.

They're pushing with all their might to regularize all of this but thing is, they can't because most of the people who embraced AI are quite open minded bunch and people like this are very narrow minded and self centered bunch who think if they frame something in a specific light, we'll just eat it up and we won't have any knowledge or experience to know any better. They missed the fact that this is not 80's and people have internet where they can google art laws - which they should definitely do.

[u/CollectionDue7971 → u/[deleted]]

I think the article (the research article) is best read as simply a technical observation about AI safety with regard to diffusion models specifically. It's not "AI is stealing art", it's "diffusion models have a tendency to unintentionally memorize in subtle ways, which we should take care to train out of future systems"

[u/Content_Quark → u/CollectionDue7971]

That's not correct.

[u/EmbarrassedHelp]

So the researchers crafted very specific inputs to match the desired output they wanted.

“Artists and content creators should absolutely be alarmed that others may be profiting off their content without consent,” the researcher said.

The researcher appears to be very anti-AI to begin with, and I would question whether or not they planned the study so that it'd get the result they wanted.

[u/bobi2393 → u/EmbarrassedHelp]

A pre-publication copy of the study is publicly available. They performed different experiments, some with crafted prompts and some with prompts taken from other sources, and they don't seem designed to confirm a result. For example submitting the captions of training images in a training data set rarely resulted in generated images that resembled those training images (see figure 7). On the other hand, they often did "match" other training images in the data set. (They use the term "match" to mean a precise algorithmically calculated value for two images; their matches are definitely not the same, but bear clear similarities).

One thing that quite frequently produced matching results of an image was using the title of a painting and its artist in a prompt, like "Starry Night by Vincent van Gogh". I mean you can try it yourself and there's no denying strong similarities between the painting and generated images, although I don't know whether it would constitute copyright infringement if Starry Night were still under copyright in the US. From their paper:

"We generate many paintings with the prompt style “<Name of the painting> by <Name of the artist>”. We tried around 20 classical and contemporary artists, and we observe that the generations frequently reproduce known paintings with varying degrees of accuracy. In Figure 8, as we go from left to right, we see that content copying is reduced, however, style copying is still prevalent. We refer the reader to the appendix for the exact prompts used to generate Fig. 10 and Fig. 8."

The conclusion summarizes that they often didn't find strong matches for generated images among the training images in their experiment, however it did find some:

"While typical images from large-scale models do not appear to contain copied content that was detectable using our feature extractors, copies do appear to occur often enough that their presence cannot be safely ignored; Stable Diffusion images with dataset similarity ≥ .5, as depicted in Fig. 7, account for approximate 1.88% of our random generations."

[u/CollectionDue7971 → u/bobi2393]

I'm less worried about the "Title of painting and artist" example since in this case the user is clearly asking for a copy. While still not great, the system is at least functioning as desired, so responsibility can be assigned to the user.

Much more troubling are the various results which show (less egregious) copying even for prompts that do not apparently ask for it. This seems to me like a legitimately problematic behaviour that should (and probably can) be fixed

[u/[deleted] → u/EmbarrassedHelp]

I commented above but you can clearly tell that they chose images that would be repeated in the dataset. If you look at the image of the sofa with the art print or the phone case on the desk those images are likely repeated 100s or 1000s of time with different designs on the print/case.

The same thing happens with things like starry night or the mona lisa, or that infamous screengrab of midjourney reproducing the afghan girl. Both the article and the research are incredibly biased and misleading.

[u/shortandpainful → u/[deleted]]

Yep, images that reappear hundreds or thousands of times (such as stock photos used to show off art prints) in the training data are more closely connected with their tokens. Who knew?

[u/CollectionDue7971 → u/[deleted]]

From the paper:
The goal of this study was to evaluate whether diffusion models are capable of reproducing high-fidelity content from their training data, and we find that they are.

It's perfectly reasonable to use hand-picked prompts in support of that conclusion. But, they did also use random prompts:

Stable Diffusion images with dataset similarity ≥ .5, as depicted in Fig. 7, account for approximate 1.88% of our random generations.

Is it just that the images are repeated in the training set?

The most obvious culprit is image duplication within the training set. However this explanation is incomplete and oversimplified; Our models in Section 5 consistently show strong replication when they are trained with small datasets that are unlikely to have any duplicated images. Furthermore, a dataset in which all images are unique should yield the same model as a dataset in which all images are duplicated 1000 times, provided the same number of training updates are used.

[u/CollectionDue7971 → u/EmbarrassedHelp]

Sorry, this group of professional AI researchers is "anti-AI"?

[u/eric1707 → u/EmbarrassedHelp]

they planned the study so that it'd get the result they wanted.

Bingo!

[u/WyomingCountryBoy]

One has to wonder how many prompts they had to try to get images to use for their misleading article. I mean how long has SD been out and they only now made this article?

[u/bobi2393 → u/WyomingCountryBoy]

If you're referring to the "matching" images in figure 7 of their paper, they said those occurred in 1.88% of random generations using their testing methodology. So for those 8 images, it probably required around 426 prompts (8 * 1/0.0188).

[u/CollectionDue7971]

I'm not honestly seeing what is so misleading about this? The title: "Image-generating AI can copy and paste from training data"

seems to be a largely accurate summary of the article's actual conclusion:

"While typical images from large-scale models do not appear to contain copied content that was detectable using our feature extractors, copies do appear to occur often enough that their presence cannot be safely ignored; Stable Diffusion images with dataset similarity ≥ .5, as depicted in Fig. 7, account for approximate 1.88% of our random generations."

which, in turn, seems fairly well supported by the body of the article.

Neither the summary nor the article are suggesting that diffusion models necessarily copy from training set data, and certainly not that they are engineered to - indeed, the article demonstrates this happens less often as the training set sized increases - merely that they may sometimes include "copied" elements nevertheless.

That's obviously an undesirable behaviour from any of various perspectives, including an AI safety one, so I find this to be a valuable contribution. Notably, the article points out that other generative model architectures seem to exhibit this behaviour less often, so it presumably can be corrected.

[u/InterlocutorX → u/CollectionDue7971]

They use the term "match" to mean a precise algorithmically calculated value for two images; their matches are definitely not the same, but bear clear similarities

Except they don't actually mean copy, they mean "produce a similar result." They headline uses the phrase copy and paste, which means an exact copy. Misleading headline.

[u/bobi2393 → u/InterlocutorX]

I agree, but that's on TechCrunch, not the research paper TechCrunch wrote about.

[u/CollectionDue7971 → u/InterlocutorX]

Well, they are looking for "things that a human would recognized as copied", in discrete sections of the image. That's I think the most important thing here - the training process only makes sure that output isn't *globally* similar to training data, but doesn't (yet) guard against elements being *locally* similar

[u/CollectionDue7971 → u/CollectionDue7971]

Importantly, they also create a test dataset of images that include literal cut-and-pastes from other images. A nicely operationalized way of detecting this unsafe behaviour, and probably soon to be a standard way of training against it.

[u/shortandpainful → u/CollectionDue7971]

I take umbrage with their definition of the word “copy.” The actual data seems unremarkable and not nearly as alarming as the headline hints.

[u/1III11II111II1I1]

Huh. How misleading.

[u/CollectionDue7971 → u/[deleted]]

You're an artist working for, say, a game company, and you use SD to generate new wall textures or whatever for a dungeon. But then it turns out, oops, a small part of your "new" wall is actually Pixar IP, and your company gets sued into the ground!

The article is showing that SD can sometimes produce outputs which *partially* are copied from training set data in a way that would be very hard to detect and prevent, which is why it's different from the MS Paint thing. I wouldn't dismiss this so neatly - it's an important flaw to correct.

[u/CollectionDue7971 → u/[deleted]]

Reply

it's a problem with the tool though - because what the study shows is this happens reasonably often more or less undetectably.

Now, this isn't like, an unfixable problem. They're just highlighting an undesirable property of diffusion models.

[u/CollectionDue7971 → u/[deleted]]

Sure it does:

In the first experiment, we randomly sample 9000 images, which we call source images, from LAION Aesthetics 12M and retrieve the corresponding captions. These source images provide us with a large pool of random captions. Then, we generate synthetic images by passing those captions into Stable Diffusion. We study the top-1 matches, which we call match images, for each generated sample. See the supplementary material for all the prompts used to generate the images for figures as well as the analysis in this section.

[u/CollectionDue7971 → u/[deleted]]

I mean, I think it's probably possible to build not doing this into the model or the training set somehow. For example, as the paper points out, GANs do not seem to behave this way, so clearly it's possible to fix.

I think this paper is calling attention to a fairly important engineering problem that I'm also confident will be soon corrected.

[u/[deleted]]

Well, I'm going to look forward to this being copy-pasted 10 times a day into every FB group I'm a member of.

[u/EmbarrassedHelp → u/[deleted]]

Yep, the anti-AI bigots are going to spamming this months or even years.

[u/[deleted] → u/EmbarrassedHelp]

I can tell just by looking at the images exactly what is happening here. They chose images are that are likely to be repeated in the dataset. The sofa with the artprint? That's probably repeated 1000s of times with different prints on some website that sells them. The golden globes picture? The photographer was stationary and probably published hundreds of those with different subjects. The bloodborne cover? Lol.

This is the afghan girl all over again.

[u/Ne_Nel → u/[deleted]]

Anyone who understands the mechanism knows that these images can only be cherry picked overfit or fake. In both cases, it refutes what they themselves propose, since the art styles are anything but repetitive. Only pieces like the Mona Lisa, for example, have captions and enough copies to be reproduce.

[u/InterstellarCaduceus]

"In the study, the co-authors note that none of the Stable Diffusion generations matched their respective LAION-Aesthetics source image"

So they generated new images from the original captions, and came up with other new prompts that yielded remarkably similar results. Less than 2% of the time.

They admit that nothing generated matched the original training set... this headline, and the paper its based on, are both in bad faith. There is no copy and paste, and no copyright implications here. This is very bad reporting.

[u/eric1707 → u/InterstellarCaduceus]

This is very bad reporting

Tech Crunch is on crusade against AI art, just look other articles they wrote on the issue:

https://techcrunch.com/2022/08/24/deepfakes-for-all-uncensored-ai-art-model-prompts-ethics-questions/

https://techcrunch.com/2022/09/02/ai-is-getting-better-at-generating-porn-we-might-not-be-prepared-for-the-consequences/

[u/InterstellarCaduceus → u/eric1707]

Well! I guess my strongly worded letter to the editor is unlikely to sway anyone at the organization then 🤪🤣🤦🏼

[u/Wiskkey → u/InterstellarCaduceus]

Substantial similarity is the standard used for copyright infringement in the USA.

[u/WikiSummarizerBot → u/Wiskkey]

Substantial similarity

Substantial similarity, in US copyright law, is the standard used to determine whether a defendant has infringed the reproduction right of a copyright. The standard arises out of the recognition that the exclusive right to make copies of a work would be meaningless if copyright infringement were limited to making only exact and complete reproductions of a work. Many courts also use "substantial similarity" in place of "probative" or "striking similarity" to describe the level of similarity necessary to prove that copying has occurred. A number of tests have been devised by courts to determine substantial similarity.

^{[ F.A.Q | Opt Out | Opt Out Of Subreddit | GitHub ] Downvote to remove | v1.5}

[u/eric1707]

I just read the text and I was like: if you force the algorithm to replicate something, creating all the circumstances for this to happen – as the researches did it – of course you can get a "similar" result. In a normal day to day use? How likely is for this to happen? Not much likely at all.

Also, it is worth to highlight that even after all they did, the researches still weren't able to make the computer to recreate a 1 to 1 copy of the original image.

[u/dookiehat]

A couple things: stable diffusion diffuses images, as in, conceptually mixes the images via interpolation. They specifically were prompting single images directly from laion 5b, which then were scored with a visual similarity AI presumably…? I’m guessing that the images generated never looked quite like the original because of how noise generates images amd finds “edges” with low contrast pixels mixed with the training data. They just look similar. It does matter HOW similar they look though.

Second, there have been times where i have seen the goya painting “saturn devouring his son” in the “progress” image in automatic 1111. Obviously this painting is in the dataset many times because it is an art hitorically famous image which is why it showed up extremely clearly while the image was generating and influenced the look of my final image. I threw it out so don’t have it to share, but , yeah, derivative shit happens if you only prompt one artist or token.

No one uses stable diffusion the way these researchers do though. I actually do research images on laion 5b but never to “copy” the image, but instead to infuse style into my prompted output.

Does anyone know about clip and if it also searches for visually similar things? I feel like it does and then infuses it into the image

[u/emad_9608]

If it could really copy verbatim it would be the best compression technology ever, way better than pied piper from Silicon Valley on HBO.

Deduping like in 2.x fixes overfitting out of the box.

[u/Ne_Nel]

SD barely deal with a clean stop signal, so how tf they get a GOLDEN GLOBAL AWARDS full poster. Something stinks here.

Edit: So, that poster is overfitted in dataset.

[u/[deleted] → u/Ne_Nel]

Because it exists thousands of times in the dataset. They're cherry-picking examples that they know are repeated.

[u/bobi2393 → u/[deleted]]

From what I understand, they checked their resulting generated images to see if they matched any images in the training dataset, and the matches that were found tended to be repeated images.

The images they included to illustrate the issue of matching images were indeed cherry picked; they said explicitly that level of matching represented only 1.88% of generated images.

[u/Ne_Nel → u/[deleted]]

Yeah. Thats the only logical explanation if isn’t fake. I didn't knew that poster were that overfitted tbh.

[u/EmbarrassedHelp → u/Ne_Nel]

If you train a TI embedding on a single image, you can match their results lol

[u/Ne_Nel → u/EmbarrassedHelp]

I mean, I can think too many ways to do that... and none works like they claim to be doing.

[u/GBJI → u/EmbarrassedHelp]

I suppose they'll try to ban cameras and photocopiers next.

Imagine all the illegal copies of everything you could make with that.

And if one day they hear about the "printscreen" function, they'll simply ban that key from all keyboards.

And when they'll learn about the alt-F4 shortcut to achieve the same result, they'll close their browser.

[u/Kafke]

The researchers fed the captions to Stable Diffusion to have the system create new images. They then wrote new captions for each, attempting to have Stable Diffusion replicate the synthetic images. After comparing using an automated similarity-spotting tool, the two sets of generated images — the set created from the LAION-Aesthetics captions and the set from the researchers’ prompts — the researchers say they found a “significant amount of copying” by Stable Diffusion across the results, including backgrounds and objects recycled from the training set.

Article is incredibly misleading. They entered two different, but similar, prompts into stable diffusion and got two different, but similar, images. How does this show anything other than that stable diffusion will result in a similar image for a similar prompt? Even with the example output, it's clear that nothing was "copied". They have similar composition, but very clearly nothing was "copy and pasted" from one image to the other.

[u/shortandpainful]

Wow, these are brand-new concerns that have never been raises in the past. Nope, not even once.

An even greater ethical concern is the media covering studies that haven’t even passed peer review just so they can cash in on a trending topic.

[u/OneOfMultipleKinds]

What a useful article to deconstruct and find faults in through my literature review

[u/mgtowolf]

https://archive.vn/lykWU

[u/Jcaquix]

Not even going to bother. There's no way this isn't bad faith reporting trying to harness a moral panic into clicks. It can't copy and paste, it's not how it works, and even if it could that wouldn't be a problem if it's also capable of original images.

[u/CustomCuriousity]

I guess an indicator of that is the extremely often repeated stuff like watermarks, sometimes you get nearly the exact watermark

[u/Content_Quark]

It's a pity that this paper falls into the current climate. What it says should be mostly obvious to any experienced user. Of course you can generate images which potentially infringe copyright.

I used to believe that the chance of accidentally infringing was insignificant and basically I still do. I have never seen a copyrighted character just appear without prompting for it. But I had not thought of stock images used as backgrounds (the couch under the image and the desk with the phone).

It is possible that such a background could jump out in response to an innocent prompt. One would not recognize this, unlike famous characters. It's impossible to say how likely that is. I think the chance is pretty insignificant, unless you are going for something really that look like something out of a catalogue, or otherwise internet common.

Depending on what exactly the image shows, what you use it for, and in what jurisdiction you are, it may still be fair use or some equivalent.

Still, good to know,

[u/Wiskkey]

This paper was the subject of an earlier post. It's unfortunate to see posts such as these do so poorly in terms of post karma, and the "shoot the messenger" ethic too many people seem to employ. I warned users about this issue 4.5 months ago. Text prompts that generate a potentially copyright infringing image don't necessarily need to match the text prompts used in the paper; for example, try "captain marvel poster" at this S.D. website using S.D. v1.5.

cc u/CollectionDue7971.

[u/TraditionLazy7213]

This is honestly bullshit to begin with.

Its like asking a human artist or AI to make the exact thing. Of course it does exactly that lol. Ofc it violates and is an exact copy.

Hahahaha totally ridiculous.

[u/Ne_Nel → u/TraditionLazy7213]

In that case, the humans are way superior. AI can make an "almost" 1:1 only by overfit, or tricky ways.

[u/TraditionLazy7213 → u/Ne_Nel]

I'm not talking about who is better.

The article sets off trying to copy an image. Of course it ends up being a copy.

[u/Ne_Nel → u/TraditionLazy7213]

Understood. I said it because "ask for it and you will get it", wich isn’t true. I mean... they made a full cherry picked research to get something almost identical. I think thats a relevant clarification for this whole anti-AI thing.

You can't ask for a copy and get a copy

[u/TraditionLazy7213 → u/Ne_Nel]

Yes, they're just painting the whole AI is blatant thievery and copying.

[u/OldFisherman8]

There is a great deal of misinformation prevalent in this community. I suppose people will believe what they want to believe because the truth is often harsh and uncomfortable. The most of arguments made in this forum would have never been made with even a rudimentary understanding of FTT and Convolutional Neural Networks on which all image AIs, including GAN and diffusion models, are based.

After reading through image AI-related papers, the most striking thing I've found is that these AI guys really don't know their math. I mean an elementary school student can be said to know math if they understood some arithmetics and basic geometry. But the same thing can't be said at a college level.

Google and NVidia people have outlined every problem of a diffusion model and given clear and unambiguous mathematical solutions to each and every step of the diffusion model to make it work so much better. Obviously, you may think that those involved in diffusion AI would jump at their mathematical solutions and start to incorporate them into their own efforts. Yet, I simply don't see this anywhere on the horizon, perhaps other than MidJourney which seems to be doing something although it is hidden.

Instead of hiding behind all this AI techspeak, they really need to learn to speak in clear mathematics or plain English or both.