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Comment by brucethemoose2 | Hacker News Reader

This is intentional, as I think OpenAI is scared of legal blowback.

Cutting edge AI models and datasets have largely been "for testing" and "for research" ever since they existed. Usage rights were extremely low on the researchers' list of concerns.

But all of the sudden OpenAI and such are trying to commercialize models that (unlike a classifier or whatever) are very capable of stepping on rightholders’ toes...

Hence I suspect all the talk of trade secrets is just a front.

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Keep in mind that the architecture of LLMs is very simple, so there's not a lot of "secret sauce". What little there is, is jealously guarded.

This is why OpenAI is keeping the parameter count of GPT 4 secret. It could be something stupid huge like 4 trillion parameters, which is the "secret" that makes it work so well. Or maybe it's just a few hundred billion, and they've done something else to make it smart.

Just knowing the numbers might be sufficient for a competitor to create a GPT 4 clone. Without the numbers, they might have to go through a process of trial and error. At these scales, even a few extra training runs could cost $ millions in training, and delay competitors by months or even years.

GPT-4 was doing more than just predicting next words

Like what?

Lots of plugins and manual fine tuning by an army of people on tons of subjects

Well yes. This is precisely the type of info that OpenAI is keeping secret, because it could turn out that it's smart because its huge OR smart because it is RLHF-ed to death. Knowing which is the secret sauce might be sufficient for a large org to reproduce it.

E.g.: if the secret is that it has been trained on tons of "books", then Google could just throw Google Books at Bard 3.

Or books3. :)

(I put together a dataset of 190,000 books that I called books3, which llama eventually trained on. Usage rights are a big interest of mine, primarily because there’s a weird disconnect of people trying to claim copyright over models when the underlying data obviously wasn’t copyrightable.)

A model (if trained right) will not include the training texts verbatim, and will arguably be a big enough transformation over the source material to be copyrightable in its own right.

IANAL, and I’m not saying how this is how it’ll play out in the courts, but I don’t see why this is a “weird disconnect”

I suspect the challenge is avoiding cases where answers are overfit. Can you get it to give you the full text of a chapter in a book that it has read, for example?

It'd be easy to imagine models that accidentally made this possible.

Thanks for your work! I had already downloaded the entire dataset. But I wonder what was the criteria behind book selection? I found it difficult to understand at places*.

* Sometimes a book has several very slightly different versions. A lot of Fantasy. Sometimes many parts of a series are included but not all of it (I can imagine an argument for a sample, or for the series in its entirity). Commentaries about important philosophical arguments like Rawls' 'A theory of justice', but not the work itself.

It was bibliotik. Embarrassingly I didn’t even think that duplicate books could be a problem. The llama folks had to dedupe the books themselves. Someday I’d like to do that too and release a cleaned version.

Basically, the-eye.eu was at one point hosting all of bibliotik, so I downloaded all the epubs and converted them to text. I still have those epubs (incidentally thanks to Carmack, who through a convoluted process managed to save the them and send them to me via snail mail) and I’ve been considering releasing them so that you can filter the books yourself.

We know for a fact that GPT-4's RLHF makes the model worse by incurring an "alignment tax." Microsoft Research and others had access to GPT-4 pre-RLHF and all reported that it was far more capable then and that it lost capability as RLHF/Alignment training checkpoints came in.

Is the GPT-0314 model (via API) the one before RLHF?

Ha ha ha ha. No. OpenAI will never make pre-alignment models available.

I think sama said this week they might release GPT3.0 DaVinci. That's a pre-alignment model.

I would bet a boatload of money that all the 3-letter folks do, in fact, have access to the pre-alignment models, maybe with whatever minimal RLHF is needed to make them usable in various interfaces. The public offerings are intended to make the unaligned models more powerful by gathering training data.

code-davinci-002, the gpt-3.5 base model, was available for a while until it was removed recently. Access is still available for researchers. Various researchers and other entities have access to the GPT-4 base model as well.

With the end result of the output being: it's just predicting next words

Nope

Agreed. If LLMs did predict only a single word ahead, they would repeat themselves, if not in the next sentence then at least in the next paragraph. LLMs certainly retain memory of what they’ve already said. They are most certainly NOT Markov-1 models, despite the popularization of that oversimplification.

They really do compute just a single token at a time, but the input to that decision is the number of tokens in the context window - so that's usually around 8,000 tokens (approximately 6000 words).

You can experiment with smaller LLMs on your own devices to get a better feeling for how that works.

I don't think anyone's saying they're Markov chains with context length 1. In all the critiques I've read no one's even come close to articulating that.

But they are Markov chains with context length N. They're approximating (implicit) Markov transition matrices through a fancier method of computation but they still are just generating one token at a time.

I thought it was common knowledge that LLMs have a context of X tokens (8192 in the case of GPT4) and use those as input to predict the next token probabilities.

Still repetition is a problem, that's why they introduce a penalty for generating the exact same token, so when picking the actual token to output from a list of probabilities, they penalize the ones already in the context so its less likely to repeat itself.

Except, literally yes

Tokens, actually.

So this is how JWT based web security dies, with thunderous applause.

I wasn't sure how you were going to use a telescope up in space for security anyway

Is the manual fine tuning driven in part by the thumbs up, thumbs down feedback on chatgpt responses?

Almost certainly

AGI: pick any two?

I find this a super interesting topic: My prediction is that we will eventually see a huge backlash against this copyright whitewashing. I think the current status quo is unacceptable but I fear the upcoming legislation will throw out the baby with the bathwater.

What is “copyright whitewashing”? I’ve never heard that phrase before.

they mean laundering IP theft

Exactly, laundering was what I meant, I mistranslated from German.

Imagine a machine that takes in copyrighted works and, with the press of a button, outputs works which are substantially similar but not copyrighted.

(I.e. pointing to your black box and claiming it's a clean room)

But to reiterate existing talking points, using copyrighted works for AI weight generation is exactly the same thing as how human artists learn skills just because some people refer to the process as “learning” or “training”, and any counterarguments to this are meaningless fearmongering outdated Luddite rages, besides such uses are fair uses and transformative uses and explicitly allowed in some jurisdictions under specific terms not allowing blanket rights to regurgitation so it’s all lawful and financially fully exploitable.

forwardslash sierra.

So, like Terry Brooks then?

> which are substantially similar but not copyrighted.

if it's considered substantially similar, then why is it not copyrighted? The difference must also be substantial. Therefore, it should be acceptable as a new works, rather than a derivative works.

Actually, I only realize now, that it is a mistranslation from my mother tongue. What I meant to say is probably better expressed by 'content laundering' analogous to 'money laundering'.

One reading of the latest US Supreme Court ruling on the Andy Warhol estate's case is that the current legal frameworks already prevent this.

The ruling focused on what was the commercial use case, not on what was the degree of transformation in Andy Warhol's artwork. The opinion appears to say that as the Prince painting was not explicitly licensed for the commercial use case of reproduction in magazine story and could conceivably have been substituted for the purpose of printing in the magazine to accompany a story by the original artist's photograph, it violated copyright. There was even an explicit distinction drawn between this work and the Campbell Soup paintings which were characterized as a social commentary and therefore fair use.

There are likely class action lawsuits brewing based on this ruling because today when commercial artists in agencies create layouts, they generally license images & artwork.

The way I read that, it was only ruled in favor because they were directed to recreate x photo with some changes. If the Scream painting were still copyrighted, it sounds like telling an AI to "create Edvard Munch's The Scream just with lighter colors" would be a copyright violation - but something like "create a new painting in the style of Edvard Munch" would still fall under "artists can't copyright a style".

The output isn't the only infringement, arguably. You could also argue (and I expect good lawyers will), that the numeric representation of the artist's works inside the model is already an infringing copy. (Just like the JPEG bytes stored on a server, even without them being blitted to a screen.)

Models don't store any artist's works. They are way too small to do that.

Getty has alleged that Stable Diffusion is sometimes returning some of their copyright images[1]. Even if the model seems too small to directly store the images, it seems at least plausible to me that the parameters can act as a compression such that the model could just output an almost direct copy of an original. I have certainly seen stable diffusion emit images which look like a getty watermark has just been blurred out.

[1] https://www.reuters.com/legal/getty-images-lawsuit-says-stab...

It doesn't store the original images, but it has learned how getty images watermark looks like and where it's located, because it has been repeated millions of times. So it sometimes can return that.

This is why it's important to clean up the training dataset. To remove duplicates, images containing watermarks, images that are too similar to each other and so on.

Models don't store any artist's works. They are way too small to do that.

I have close to none knowledge on this subject but I find it very curious and I'd like to know more on that because it seems to me they don't store it but only in the traditional sense. For example, if you could procure a quote, for instance, I asked (chatgpt):

- "In Game of Thrones what did Jon Snow say to Arya when he gave her the sword named 'needle' ?",

- and it answers: "[...] "Stick 'em with the pointy end. [...]"

Then it indicates to me that the information is there. Maybe we should consider that the model actually stores the information but the information is compressed ? Could you ask midjourney to recreate the Mona Lisa of Mickey Mouse ? The right information can't just appear out of thin air. If I recall correctly, someone had some success in identifying and modifying the right neurons or weights of some LLM which changed it "opinion" on where Rome is located ?

This is not precisely true. It's been shown that image models can reproduce certain works almost exactly (up to very minor differences). It takes some effort to find such pieces but they exist.

It takes a lot of effort and the results aren't that great. (low resolution, bad hands) It's way easier to just find the original image and use that instead.

Technically, the DMCA does not limit people from storing copyrighted works, only downloading, distributing and/or reproducing.

Except this is not transformative at all - LLMs are literally trained to be able to output their training data.

That's why it produced those substantially plagiarized CNET articles.

It's more complex than that, they are trained to model their language and understanding of a concept to match the author. There is not enough memory in the network to remember the actual text.

It's like giving a talented an artist a day with a painting, and then a day later ask them to precisely copy the painting from memory. Would they come close enough for it to be considered a forgery, or will it be a transformative reinterpretation? It will probably depend on the skill of the artist, and I feel it might go either way.

Whether a human artist can do this is largely irrelevant to the question of copyright, however. Selling a close copy of another artists’ work (unless they’re long dead) would likely be infringement.

I think the distinction is that you don't arrest someone for merely having the capability of reproducing a work, but after they've reproduced it and attempted to use it in an infringing manner. I don't know how applicable the analogy is given that LLMs don't "think", of course.

you might say that, but the literal benchmark of LLMs (or any supervised learning algorithm for that matter) is loss, or how much 'distance' is between their output, and the validation set, when seeded with the training set. With a loss of well below 1%, which is typical, it means it can pretty much recreate the training data.

I've not seen that number before, what paper is that where there's a loss of less than 1% on the validation/test set?

For people who are unaware of this case, here[1] is a writeup.

TL;DR: Andy Warhol used a photograph of Prince by Lynn Goldsmith to create a series of original silk screen artworks called "Orange Prince". The supreme court ruled (as I understand it) that Warhol's use was not sufficiently transformative to negate the right of the original copyrightholder.

[1] https://www.theguardian.com/artanddesign/2023/may/18/andy-wa...

Edit to add: Here’s a side-by-side https://www.mondaq.com/images/article_images/1324682a.jpg . I mean extreme right second from the bottom he hasn’t even changed the colours.

In terms of actions, the US has gone as far as to kneecap a competitor's chip industry and restrict their GPU access.

I reckon that behind closed doors there will be a strong sense that any kind of regulation that impedes AI development would not be in the national interest.

It still seems odd to me that the standard advice for "how do I avoid copyright issues with example code" is "rewrite it in your own style", and we're happy with this, but now that we have an algorithm that can do this (using a stupendous amount of calculation, it's not like these things learn for free... yet...) this is suddenly somehow cheating.

I think the protestations about copyright aren't entirely honest. People aren't mad that LLMs are "stealing their work", they're scared that LLMs are copying their ability to produce such work.

Its some of both. Stable Diffusion will rarely blatently copy a work, as it has no distinction between creative reconstruction and plagiarism. I think this applies to LLMs too, its just much harder for human brains to detect.

At the same time, the artists and such who claim any of its output is illegal are being hypocritical. Nothing is made in a vacuum.

Do you think people should be allowed to learn from books we read, music we hear, etc?

If we’re extending copyright to be not only about reproduction of the work, but also providing part of a large knowledge base for future creators to build upon… why does it matter if it’s a neural network or human being doing the learning?

I personally hope the copyright maximalists fail, because if they succeed in making “learning from a copyrighted work” without paying illegal, we end up in a dark place indeed.

I think AI aren’t people, and that they’re not even AI: it’s machine learning, and ML is a subset of AI that we haven’t really broken out of. ML is a tool, and asking whether ML should be allowed to learn on copyrighted work is equivalent to asking whether your computer is allowed to contain a copyrighted work: “it depends.”

I’m in favor of getting rid of copyright entirely, since it’s become a far cry from what it was intended to be, but we have to acknowledge both sides of the arguments in order to proceed.

    I’m in favor of getting rid of copyright entirely

This is extreme to me. What is your proposed replacement?

https://en.wikipedia.org/wiki/Copyright_alternatives

I agree we should either get rid of, or very least overhaul copyright.

However, the line in the sand I draw is giving any kind of human-based rights to ML or AI. People already are thinking ChatGPT is close to human intelligence, and we know we haven’t even seen what’s truly possible.

My fear is the typical oversimplification of complex technology will lend us to giving human rights to programs, and lead to a whole host of issues.

We need to stop anthropomorphizing technology until we really hit a philosophical quandary. Giving in too early will just give power to less-than-polished tech and either slow down research, or further drain a country’s resources

> the sand I draw is giving any kind of human-based rights to ML or AI.

Ok, but this isn't about giving rights to AI.

Instead this is about given rights to humans to use AI.

If you make a law about this stuff, at the end of the day, it all trickles down to allowing or disallow human actions.

The rules are already different for humans and machines. If I memorise a copyrighted book then that’s not copyright infringement, but if I store a copy on a hard drive then that is. You ask why distinguishing between humans and machines matters and then answer your own question: “we end up in a dark place”.

IANAL, but as I understand it, the difference is because copyright is about the transmission of the work.

If you memorize a work, you have used the work in accordance with copyright law: you were authorized to read the work. If you write out a copy of the work you memorized and give it to someone, you've broken copyright law.

If you store a copy of a work you were authorized to have, you have not broken copyright. However, if you download a book that you have no authorization for, that is a violation of copyright.

In other words, it's not about where the work is "stored" or whether or not humans were involved, it's about the transmission of that work between parties.

Actually, if you memorize that book and then type it in to your own computer, you're fine. So maybe a bit of a rework on that line of argument.

I would argue if the fair use argument holds up - that the most substantial value of these models lies in their training dataset.

It would be morally reprehensible for anyone to block training open models based on the data provided by the public at large. Such legalization would be totally unacceptable.

Why would that be morally reprehensible?

Is it not as "morally reprehensible" that I have to restrict my use of the internet now in order to avoid having my works used in that way? That reduces the amount of information available to everybody, after all.

Or maybe, maybe the people in charge will wake up, do their job, accept the challenge and make modern cc laws? Oh forget about it we will forever live in the 90'

> I find this a super interesting topic: My prediction is that we will eventually see a huge backlash against this copyright whitewashing. I think the current status quo is unacceptable but I fear the upcoming legislation will throw out the baby with the bathwater.

Worse, they have the potential to push development toward countries that do not have such laws.

They forced others out of the skunk so I am fine with it. Forcing companies like Facebook in leaking weights of their models because they are over eager to release something is no small feat. For nothing else I applaud them for that.

If a human learns to draw by studying the works of other artists, is that infringement?

If an algorithm processes a bunch of copyrighted data into a different form, is that not a derivative work?

only if the output is definitively a derivative work of an existing work. If it's the culmination of a thousand photos, it would be akin to a human spending a month studying a certain historical artists' art and painting a modern Tesla in their style.

It could be, if the human uses their skills to draw infringing outputs!

hence the entire Andy Warhol case!

>Echoing a recommendation from Joe Biden’s administration, the supreme court focused on the specific use that allegedly infringed Goldsmith’s copyright – a license of Warhol’s work to Condé Nast – and said it was not transformative because it served the same commercial purpose as Goldsmith’s photo: to depict Prince in a magazine.

https://www.theguardian.com/artanddesign/2023/may/18/andy-wa...

So why are we even arguing about this new form of copyright that restricts who can learn from a work?

If an AI model produces an infringing work and someone distributes it, sue them like you would a human doing the same thing.

This idea of training being a violation of copyright is insane.

No, is not insane at all, a computer can generate millions of derivate copies in a second, a human has limited time for learning and for making artwork, so it doesn't make sense to compare them, not in the slightest.

If suddenly there was a race of humans who could read and retain entire books in a matter of seconds (and other similar feats) the implications would be almost the same, as it would break basic expectations of time and personal resources that our society relies on, and a lot of people would be rightfully worried about such people entering the workforce.

Computers can do all sorts of things humans can't at inhuman scale and speed. Why is AI leveraging the same training data humans do inherently bad just because it can do it better and faster than humans?

Yeah, I'm a programmer, I'm aware of all that by definition. The issue here it's getting to he point where is inevitable that it will replace a huge amount of job positions in a tiny frame of time in ways society is not prepared for, if you think the amount of jobs at risk is anywhere in a similar scale we have seen before you haven't been paying attention; of course the flaw here lies on society's itself and what it means to earn a living, and not really over ML researchers or Adobe or any other company creating those AIs.

Adobe trained their own model only on public domain images and on images that they own. Do you think that they should not be allowed to do that?

That's a different discussion and should have wide ramifications with way more nuance that "allowed" or "not allowed", if suddenly tomorrow Adobe or any other were able to create a model that is able to replace any existing job position how we should it be managed? The societal ramifications are complex, is not binary and should be discussed by politicians and intellectuals, not by corp marketing teams.

They can already replace stock image sites. Why pay getty/shutterstock an obscene amount of money for a photo if you can simply generate something similar with a prompt for free?

I checked how much some images cost on getty images - and their prices are ridiculous - 100-1000eur or more. No wonder they are trying to sue stability.ai now.

Speed isn't an existing factor in copyright law.

That is not a factor may have something to do with the fact the law was created before even sci-fi authors could have predicted machine learning and the huge difference it makes for creating artwork.

Generating synthetic copies of someone voice, or even combining voices (e.g. a CG voice that is a equal parts Kurt Cobain's voice and Laine Staley's voice) are also not currently a factor in copyright law, but it will be.

Wait, so do your same opinions about copyright for AI also apply to copyright for corporations? How is employing 1000 people to generate derivative works any different from using AI?

Because they have to hire those 1000 people and all that entails? They will have to weight if it's worth it to pay that many people, and if in the future they ever want to generate even more images they have to hire them again, instead of just re-running a program which costs them near nothing. The more I think about it the less similar are both processes.

No.

But we're not talking about humans, we're talking about software intended to put the artists whose work it was trained on - without permission or compensation - out of business, or which is at least capable of devaluing their work with its ability to copy styles (literally by name.)

Even if one could argue (as I probably have) that AI is more than simply a "stochastic parrot," a philosophical argument about whether it resembles a primitive mind (and therefore whether its output can be considered "art" in any form) doesn't answer a legal argument about copyright infringement. Humans are allowed to be inspired by other humans, software used by other humans is not.

Also you shouldn't delete your comment just because it gets downvotes and repost it. Bad form.

The bigger argument is in the form of cost of reproduction.

A human has finite time and output volume.

A machine has no such limits, only compute/memory/network/disk.

Ergo, gray areas where we could afford a human the benefit of doubt ("What? They can paint 4 in-the-style-of-Rembrandts?") produce substantially different results with machines ("Here are a million in-the-style-of-Rembrandts, produced in 30 minutes").

Whether we should permit or limit machine art is a discussion worth having -- but human copying vs machine copying is clearly a very different discussion.

Right, this is why we should burn the combines and go back to harvesting wheat by scythe, or we should dig ditches with spoons instead of using an excavator...

The problem here is we want the system we currently have to keep working when there is really no possible way that can happen in a world of AGI. IP is already a horribly broken system that can lead to absurdities. While making IP stronger will protect people like artists (maybe) it will provide far more protection to monied corporations that can afford to buy up works, and then feed them into their AI. In the pathological cases you end up with stories like 'The Right To Read'.

In a world where particular things can be created in almost infinite amounts, why will we go to such efforts to ensure they are only created in limited supply?

> we want the system we currently have to keep working

No.

We want *a* working system, that probably looks very different than the previous one.

But the "AI and let the chips fall where they may" crowd is being disingenuous with the "just like human copying" argument.

It's not. It's fundamentally different in scale.

> It's fundamentally different in scale.

yes, it is. But this increase in scale is going to be something that produces progress (ala, cheap imitations of good styles that already exist). And with unprecedented amount of "generated" works, originality is going to be highly sought after, and those who do produce original content will be capable of succeeding.

And yet, once they succeed, they cannot rest on their laurels as the AI will be very quick to reproduce that "originality" - therefore, forcing the artists/creatives to continuously come out with highly original works all the time.

> originality is going to be highly sought after

You and I are not seeing the same Walmart, McDonald's, Amazon, Netflix, etc.

The issue with comments like this is that many people are talking about the law as it is, and others are approaching it from a moral/solution-based point of view.

Legally, AI is likely free and clear since the work it outputs is vastly different from any one art piece, therefore not being a derivative work of anything else (unless you tell it to copy an artists' style AND ask it specifically to remake an artists' existing work with the intent to recreate it).

But morally, there are indeed externalities that can/should be thought of, and possible even put into law, but starting a discussion of this front is a losing game since everyone will have a different opinion on how much we should restrict, and whether it will even work (if you consider that China and Russia will forego any sort of copyright protection for AI generation, these laws could enable these countries to surpass the rest of the world in capability and quality).

> edit

What if someone trains a model on public domain works only?

Also adobe sort-of did that already - they trained their own model only on their own images. So no "IP theft" there.

This is almost precisely the argument made by the Luddite movement. “Intended to put artists out of work” is a tellingly emotional way to describe a transformer NN which self-evidently is “intended” to do no such thing. I can imagine 19th century textile workers terrified of mechanical looms using very similar language.

> Humans are allowed to be inspired by other humans, software used by other humans is not.

This absurd statement is not supported by any law you can cite. It’s nothing more than wishful thinking.

It’s hilarious that people confidently compare the anti-AI crowd to the Luddite movement, while at the same time demonstrating they know absolutely nothing about its actual history. Workers were not “terrified of mechanical looms,” but outraged that they were used to reduce pay and working conditions, instead of improve them. Of course the same will be true of AI, but I don’t expect the “can’t be bothered to even read the Wikipedia entry” crowd to understand this.

It's funny in a sickening but not unexpected way that a crowd that professes to be more learned, and interested in learning, than most others, chooses to ignore this on a consistent basis. They know they're building a world of pain, they just think if they ignore it and feign ignorance of it that they can launder their consciences.

> Workers were not “terrified of mechanical looms,” but outraged that they were used to reduce pay and working conditions, instead of improve them.

This is consistent with what I wrote. They were terrified of their jobs vanishing and sought to stop this. Ultimately it’s not realistic to prohibit useful new technology, even if it has harmful effects on some groups.

I’m sympathetic to people that are fearful of new technology that will obviously have a major impact on our society but there’s a reason Luddite is a shorthand for futile technological obstructionism.

It’s not a matter of pro vs anti AI and I wouldn’t put myself in either camp, it’s a matter of naïveté vs realism about how the world works.

The ignorance of history continues. The Luddites were not naive, but less successful in labor organizing than, say, their French counterparts, who quite successfully resisted downward pressure on wages and working conditions, allowing a more gradual transition to the new technologies. As a result, they likewise did not suffer much of the injury, cruelty, and dispossession that was inflicted on their counterparts in their English working class. The Luddite movement is more popularly know for precisely this reason: it was crushed and humiliated by the ruling class, and thus became useful for those interests to cite as an example of what happens when you oppose them vs. those who practiced solidarity, overcame them, and preserved more of their dignity and quality of life.

Letting the wealthy and powerful inflict whatever they want on you is not realism. It’s cowardice.

People are using the term "luddite" as a means to trivialize and dismiss real concerns others have. It's just an insult that deserves to be ignored, nothing more.

What if someone trains a model on public domain works only? It hasn't been done, because the law doesn't require it.

LAION-5B is a big mess and its caption quality is all over the place. Stability.ai basically chose a brute-force approach and threw a lot of data at the problem.

But now imagine if someone took all the public domain images that they could find, properly labelled them and then trained a base model on all of that.

I'm pretty sure that its output would also be very good. There are a ton of public domain photos out there so photorealism shouldn't be a problem.

So - what's your argument then?

https://huggingface.co/Mitsua/mitsua-diffusion-one is an image generation model which is "trained from scratch using only public domain/CC0 or copyright images with permission for use".

Thanks for the link. The project looks really interesting. If it's successful - it will basically kill all the arguments about ethics.

> Humans are allowed to be inspired by other humans, software used by other humans is not.

Why?

Because humans are beings capable of inspiration and software is a tool that can only transfer and transform data, and that data is subject to existing copyright laws, because we recognize the difference between ourselves and the tools we use.

And (more importantly) because the intent behind this violation is anti-human, creating a system that debases human creative endeavor, leeches off of talent and puts humans out of work for the sake of banal facsimile.

So if you're making the argument that AI is a human when it comes to the copyright, does that mean that its output is copyrightable just like a humans?

Does that mean that OpenAI/SD whoever now owns the work created in such a way?

It would logically follow from your reasoning.

For what should be pretty obvious reason, most society and laws will treat humans and corporation and their output differently.

Why are you assuming that deep learning and (human) learning are the same thing?

because he trust Human just a larger matrix.

This, and keeping the mix Proprietary.

If the progress of open-source options has shown anything - the talent wanting to work together will make progress.

Elon commented about commercialization of OpenAI: https://youtu.be/bWr-DA5Wjfw?t=3m38s

This short video perhaps should be its own submission

Is there any poison pill type of defense for content creators?

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