> all of the AI models that are being used to replace artists were trained through the mass theft of those very same artists’ work

This seems to be the underlying moral justification for creatives' opposition of AI. Job destruction is the obvious self-interested justification.

My question (probably not original) is this: which biological neural network was not trained on copyrighted material from other artists? Which guitarist never listened to classic rock bands? Which painter never looked at Monet? Nearly all successful artists publicly recognize the influence of prior art on their development and style (and the rest are lying).

A quick hypothetical:

Let's say instead of hoovering up billions of images from data dumps, the AI researchers took a still camera to all the art galleries on the planet and snapped photos of art, then trained the neural network on those photos. Is this still "theft" of the artists' work? What about if an embodied AI in a robot chassis did the same, then painted new art with oil & canvas? Is this still theft?

Perhaps this example fails because it's not enough data? But is it a difference in degree or a difference in kind?

> which biological neural network was not trained on copyrighted material from other artists?

The premise that rules should equally apply here is absurd. It’s completely natural that certain rules don’t apply to humans but not AI and vice versa. Similar to humans vs animals or humans vs inanimate objects. Even among humans, rules don’t apply equally always.

If we are going to go technical I would argue that artists generate a highly derivative work, i.e. they input the "training data" via their eyes, experience of the world, ruminating (is that the word?) about art... and then output motions of their hands, words, and so on which create new artistic work.

Diffusion models tune a series of numbers via a complicated but essentially straightforward and "pipeline-like" process from the training data, so their work is significantly less derivative, even if visually it looks different. I guess we all agree that say, a encrypted zip file of a song is not a derivative, it's the song after some data processing.

Maybe we should improve the definition of derivative work to express how many layers of indirection lie between training material and output? If I trace over a painting I guess most would agree I've barely done any art, and I think that the process is more or less "pipeline-like" (even if complicated): input data is immediately converted to "art" by my brain following the lines. An artist that studies Goya for years and slowly improves his Goya-like portraits is another kind of level of indirection between training material and output!

> My question (probably not original) is this: which biological neural network was not trained on copyrighted material from other artists? Which guitarist never listened to classic rock bands? Which painter never looked at Monet? Nearly all successful artists publicly recognize the influence of prior art on their development and style (and the rest are lying).

Those successful artists accessed their training data through legitimate means (please hold your 'well actually' replies about some artist using bittorrent here and there). They didn't all pirate, on a historic, industrial scale, every scrap of creative work that they saw in their lives. That's the key distinction.

You're focusing too much on semantic nitpicking and less on the part where people "wield it as a tool to fuck over artists."

Nobody cares if hobbyists make a "Star Wars but make it Monet" art piece with AI and put it on their wall. It's the part where bosses fire John Smith and then ask the LLM, "please write 100 articles in the style of John Smith." It's the part where an actor is hired once, their face scanned, and then their likeness is used (unpaid) in a hundred films.

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