Getty Images bans AI-generated content over fears of copyright claims

It's possible for generation models to perfectly memorize and reproduce training data, at which point I view it as a sort of indexed slightly-lossy compression, but it's almost never happening with image generation because the models are too small to memorize billions of pictures, it can't produce copies.

Stable diffusion 1.4 has been shrunk to around 4.3GB, and has around 900 million parameters.

I don't know how big Copilot is, but a relatively recently released 20 billion parameter language model is over 40GB. ( https://huggingface.co/EleutherAI/gpt-neox-20b/tree/main ) GPT-3, according to OpenAI, is 175 billion parameters.

It's possible there are some images in there you can pull out exactly as is from the training data, if they were to appear enough times, like I suspect the Mona Lisa could be almost identically reconstructed, but it would take a lot of random generation. I'm trying it now and most of the images are cropped, colors blown out, wrong number of hands or fingers, eyes are wrong, etc.

An AI can probably do half of my day job because it's stupidly repetitive. Leadership imposes old ways, and they dismiss anything "new" (i.e. newer than 2005). For example, writing all these high-level data pipelines and even web backends in C++ despite having no special performance need for it. Even though I'm not literally copy-pasting code, I'm copy-pasting something in my mind only a little higher-level than that, then relying on some procedural and muscle memory to pump it out. It's a skill that anyone can learn, just takes time. If I didn't have side projects, I'd forget what it's like to think about my code.

Some old-school programmers complain about kids with high-level languages doing their job more efficiently, so they work on lower-level stuff instead. It's been that way for decades. Now AI is knocking on that door. But before AI, C was the high-level thing, and we got compiler optimizations obviating much of the need for asm expertise, undoubtedly pissing off some who really invested in that skillset. If I'm working on something needing the performance guarantees of C or Asm, and the computer can assist me, I'm all for it. Please take this repetitive job so I can use my brain instead.

And the copyright thing is just an excuse. Programmers usually don't give a darn about copyright other than being legally obligated to comply with it. So much of programming is copy-paste. GPL had its day, and it makes less and less sense as services take over. GPL locks small-time devs out of including the code in a for-profit project but does nothing to stop big corps from using it in a SaaS. The biggest irony is how Microsoft not only uses GPL'd code for profit but also ships WSL, all legally.

Probably their value in the advertisment production chain is going to become close to zero in a few years, and they will try to stop or at least slow it down.

But once we have open source models released to the public I cannot see how a local legislation can have any impact at all.

The other challenges are: (i) the model isn't a human that can defend themself by explaining their creative process, it's a literal mathematical transformation of the inputs including the copyrighted work. (And I'm not sure "actually the human brain is just computation" defences offered by lawyers are ever likely to prevail in court, because if they do that opens much bigger cans of worms in virtually every legal field...) (ii) the representatives of OpenAI Inc who do have to explain themselves are going to have to talk about their approach to licenses for use of the material (which in this case appears to have been to disregard them altogether). That could be a serious issue for them even if the court agrees with the general principle that diffusion models or GANs are not plagiarism.

And possibly also (iii) the AI has ridiculous failure modes like implementing the Getty watermark which makes the model look far more closely derived from its source data than it actually is