I read an article the other day about managers complaining about zoomers not even knowing how type on a keyboard.

This idea that copyright and IP shouldn’t exist at all is kinda absurd.

For the majority of human existence, that was the default.

Copyright exists as an explicit tradeoff between the rights of the public to be able to do as they please with stuff introduced into the public sphere, and a legal limitation infringing on the publics liberty for a limited time for the purpose of encouraging the creation of more works for the public benefit. It was not introduced as some sort of inherent right, but as a trade between the public and creators to incentivise them.

Stripping it away from existing artists who has come to depend on it without some alternative would be grossly unfair, but there’s nothing absurd about wanting to change the bargain over time. After all, that has been done many times, and the copyright we have now is vastly different and far more expansive and lengthy than early copyright protection.

Personally, I’d be in favour of finding alternative means of supporting creators and stripping back copyright as a tradeoff. The vast majority of creators earn next to nothing from their works; only a very tiny minority makes a livable wage of art of any form at all, and of the rest the vast majority of profits take place in a very short period of initial exploitation of a work, so we could allow the vast majority to earn more from their art relatively cheaply, and affect the rest to a relatively limited degree, while benefiting from the reduced restrictions.

This idea that copyright and IP shouldn’t exist at all is kinda absurd

I don’t hold this opinion at all.

I’m just saying that there are uses for which you don’t need a license. Say, visiting an art exhibition and then going home and trying to draw similar pictures. Wether AI training falls into this category or instead requires a license is currently unclear.

Btw, two spaces before the line break
Creates the spacing you want.

And if you think that working with AI does not take effort you either did not try, or don’t have an artistic bone in your body. Randos typing “Woman with huge bazingas” into an UI and hitting generate don’t get copyright on the output, rightly so: Not just did they not do anything artistic, they also overlook all the issues with whatever gets generated because they lack the trained eye of an artist.

To me, that’s not an argument for regulating AI, though, because most regulation we can come up with will benefit those with deep enough pockets to buy themselves out of the problem, while solving nothing.

E.g. as I’ve pointed out in other debates like this, Getty Images has a market cap of <$2bn. OpenAI may have had a valuation in the $90bn range. Google, MS, Adobe all also have shares prices that would trivially allow them to purchase someone like Getty to get ownership of a large training set of photos. Adobe already has rights to a huge selection via their own stock service.

Bertelsmann owns Penguin Random-House and a range ofter publishing subsidiaries. It’s market cap is around 15 billion Euro. Also well within price for a large AI contender to buy to be able to insert clauses about AI rights. (You think authors will refuse to accept that? All but the top sellers will generally be unable to afford to turn down a publishing deal, especially if it’s sugar-coated enough, but they also sit on a shit-ton of works where the source text is out-of-copyright but they own the right to the translations outright as works-for-hire)

That’s before considering simply hiring a bunch of writers and artists to produce data for hire.

So any regulation you put in place to limit the use of copyrighted works only creates a “tax” effectively.

E.g. OpenAI might not be able to copy artist X’s images, but they’ll be able to hire artist Y on the cheap to churn out art in artist X’s style for hire, and then train on that. They might not be able to use author Z’s work, but they can hire a bunch of hungry writers (published books sells ca 200 copies on average; the average full time author in the UK earns below minimum wage from their writing) as a content farm.

The net result for most creators will be the same.

Even wonder why Sam Altmann of OpenAI has been lobbying about the dangers of AI? This is why. And its just the start. As soon as these companies have enough capital to buy themselves access for data, regulations preventing training on copyrighted data will be them pulling up the drawbridge and making it cost-prohibitive for people to build open, publicly accessible models in ways that can be legally used.

And in doing so they’ll effectively get to charge an “AI tax” on everyone else.

If we’re going to protect artists, we’d be far better off finding other ways of compensating them for the effects, not least because it will actually provide them some protection.

You can see the difference in the process in the results, for example in how some generated pictures will contain something like a signature in the corner

If you were to train human children on an endless series of pictures with signatures in the corner, do you seriously think they’d not emulate signatures in the corner?

If you think that, you haven’t seen many children’s drawings, because children also often pick up that it’s normal to put something in the corner, despite the fact that to children pictures with signatures is a tiny proportion of visual input.

Or how it is at least possible to get the model to output something extremely close to the training data

People also mimic. We often explicitly learn to mimic - e.g. I have my sons art folder right here, full of examples of him being explicitly taught to make direct copies as a means to learn technique.

We just don’t have very good memory. This is an argument for a difference in ability to retain and reproduce inputs, not an argument for a difference in methods.

And again, this is a strawman. It doesn’t even begin to try to answer the questions I asked, or the one raised by the person you first responded to.

That at least proves that the process is quite different to the process of human learning.

Neither of those really suggests that all (that diffusion is different to humans learn to generalize images is likely true, what you’ve described does not provide even the start of any evidence of that), but again that is a strawman.

There was no claim they work the same. The question raised was how the way they’re trained is different from how a human learns styles.

I don’t understand how you’re separating the the generated artworks from the AI that’s generating the work, but I do see your point. If a company puts out a tool for free I don’t think they should be on the hook for someone using that and creating a product. At the end of it all though, I think whoever has made any hard financial gains should should payout whoever contributed.

if the only source of input data for a network is subtly corrupted, won’t that guarantee corrupted output as well?

We have to distinguish between different kinds of “corruption”, here. What you seem to be describing is “if we only feed the model data from rule34, will it ever learn proper human anatomy” and the answer is no, it won’t. You’ll have to add data which narrows the range of body proportions from cartoonish to, well, real. That’s an external source of corruption: Feeding it bad data (for your own definition of “bad”). Garbage in, garbage out.

The corruption that these adversarial models are exploiting though is inherent in the model they’re attacking. Take… ropes and snakes and cats (or, generally, mammals). Good example: It is incredibly easy for a cat to mistake a rope for a snake – it looks exactly the same to the first layers of the visual cortex and evolution would rather have the cat jump away as soon as possible than be bitten, and it doesn’t hurt to jump away from a rope (even though the cat might end up being annoyed or ashamed (yes cats can 110% be self-conscious different story)), so when there’s an unexpected wiggly shape the first layers directly tell the motor cortex to move, short-circuiting any higher processing.

That trait has been written into the network by evolution, very similar to how we train AI models – conceptually, that is: In both cases the network gets trained for fitness for a purpose (the implementation details are indeed rather different but also irrelevant):

What those adversarial models do kinda looks like this: Take a picture of a rope. Now randomly shift pixels to make the rope subtly more snake-like until you get your cat to jump as reliably as possible, in as many different situations as possible, e.g. even if they’re expecting it and staring straight at it. Sell the product for a lot of money. People start posting pictures of ropes, rope manufacturers adjust their weaving patterns. Other cats see those pictures and ropes, some jump, and others only feel a bit, or a lot, uneasy. The ones that jump will not be able to procreate, any more, being busy jumping, while the uneasy ones will continue to evolve. After a couple of generations no cat cares about those ropes with shifted pixels any more.

Whether that trains general immunity against adversarial attacks – I wouldn’t be so sure. It very likely will make the rope/snake distinction more accurate. But even if it doesn’t build general immunity, it’s an eternal cat and mouse game and no artist will be willing to continue paying for that kind of software when it’s going to get defeated within days, anyway, because that’s just how fast we can evolve models.

Oh. Back to the definition of corruption: If all the pictures of rope that our models ever see have shifted pixels then it’s just going to assume that is the norm, and distinguish it from snakes because the tags say “rope” in one case, and “snake” in the other. The original un-shifted pictures probably won’t be an adversarial attack because they’re not a product of trying to get cats to jump.

Quick iteration is definitely the big thing. (The eye is fun because it’s so “badly designed” - we’re stuck in a local maxima that just happens to be “good enough” for us to not overcome the big glaring problems)

And yes, if all the inputs are corrupted, the output will likely be too. But 1) they won’t all be, and as long as there’s a good mix that will “teach” the network over time that the difference between a “corrupted cat” and an “uncorrupted cat” are irrelevant, because both will have most of the same labels associated with them. 2) these tools work by introducing corruption that humans aren’t meant to notice, so if the output has the same kind of corruption it doesn’t matter. It only matters to the extent the network “miscorrupts” the output in ways we do notice enough so that it becomes a cost drag on training to train it out.

But you can improve on that pretty much with feedback: Train a small network to recognize corruption, and then feed corrupted images back in as negative examples to teach it that those specific things are particularly bad.

Picking up and labelling small sample sets of types of corruption humans will notice is pretty much the worst case realistic effect these tools will end up having. But each such countermeasure will contribute to training sets that make further corruption progressively harder. Ultimately these tools are strictly limited because they can’t introduce anything that makes the images uglier to humans, and so you “just” need to teach the models more about the limits of human vision, and in the long run that will benefit the models in any case.