3 ms·

Why? Are you saying humans are incapable of accidentally reproducing previously seen work?

988747 4y ago
Most humans are not capable of exactly reproducing previously seen work even on purpose. Every human artist has unique style that shows even when they try to imitate. That's why perfect forgery is a form of art in itself :)
wahnfrieden 4y ago
Correct
haswell 4y ago
Many of the core arguments that claim Stable Diffusion training is exercising fair use do so by claiming that this computer program is similar enough to a human brain to qualify it for the protections described by copyright law, and they base that claim of similarity on the idea that the software doesn't copy, it just learns, and that all output is fundamentally new. I'm arguing that a finding like this harms such core arguments, and highlights just one of many ways these models are entirely unlike humans. > Are you saying humans are incapable of accidentally reproducing previously seen work? To conclude that would be a form of propositional and possibly equivocation fallacy, IMO. While I acknowledge that it's possible someone might "accidentally remember" someone else's work and then create a piece that is very similar to it, this hardly seems likely as a general case, and such a possibility was baked in to the current rules as written. To take this further and claim that a human could do so with precision and in a reproducible manner seems questionable. The Stable Diffusion equivalent of this kind of "remembering" and resulting duplication is again different in context and contents than a human exposed to the same images. The fact that it can be replicated systematically is the most distinctly non-human part, and is a strong hint that we're comparing very different things.
- usrbinbash 4y ago
  > I'm arguing that a finding like this harms such core arguments Why? If a really good artist studies a single piece long enough, he could be able reproduce it to a degree where it takes expert analysis to determine which is the original. It's not as if there have never been forgeries of expensive artworks. The difference between a human studying a certain piece intensely, and a model overfitting to it, is that to the model, it happens by accident. Overfitting to the training set is not a desired outcome, it's something ML techniques are trying to actively avoid.
  - haswell 4y ago
    > Why? If a really good artist studies a single piece long enough, he could be able reproduce it Part of the answer is in your question. A really good artist is rare, and forgery is a specialized skill. The point is not that humans are capable of forgery, but that the model/software is capable of it with minimal effort, and enables anyone regardless of skill to achieve similar outcomes. Setting aside the resulting privacy issues, this has major implications. Two are: 1. The risk of forgery previously accepted by a copyright framework that assumed human actors has drastically changed. 2. The claims that Stable Diffusion only produces derivative work and not originals was at the heart of the argument that says SD model training is fair use, and regardless of why, we know this is not true. > The difference between a human studying a certain piece intensely, and a model overfitting to it, is that to the model, it happens by accident. That is one difference in the factors surrounding the initial creation of the output. The differences continue to stack up as you examine the process of learning, the algorithms that produce new output, the computational context (both hardware and software), the agency of the entity creating the output, etc. The software doesn’t do anything by accident. It does exactly what it has been instructed to do. The humans training the model are at the core of any accident. This might seem obvious, but I think it bears restating because it highlights the nature of the situation. The model is not an independent actor. Whether or not it’s a desired outcome is not relevant. The fact that it is possible - and that this possibility has further implications about the nature of the software itself - is what I argue hurts the standard arguments claiming Stable Diffusion is not infringing by virtue of its similarity to human processes of thinking and expression.

Most humans are not capable of exactly reproducing previously seen work even on purpose. Every human artist has unique style that shows even when they try to imitate. That's why perfect forgery is a form of art in itself :)

wahnfrieden 4y ago

Correct

haswell 4y ago

Many of the core arguments that claim Stable Diffusion training is exercising fair use do so by claiming that this computer program is similar enough to a human brain to qualify it for the protections described by copyright law, and they base that claim of similarity on the idea that the software doesn't copy, it just learns, and that all output is fundamentally new. I'm arguing that a finding like this harms such core arguments, and highlights just one of many ways these models are entirely unlike humans. > Are you saying humans are incapable of accidentally reproducing previously seen work? To conclude that would be a form of propositional and possibly equivocation fallacy, IMO. While I acknowledge that it's possible someone might "accidentally remember" someone else's work and then create a piece that is very similar to it, this hardly seems likely as a general case, and such a possibility was baked in to the current rules as written. To take this further and claim that a human could do so with precision and in a reproducible manner seems questionable. The Stable Diffusion equivalent of this kind of "remembering" and resulting duplication is again different in context and contents than a human exposed to the same images. The fact that it can be replicated systematically is the most distinctly non-human part, and is a strong hint that we're comparing very different things.

usrbinbash 4y ago
> I'm arguing that a finding like this harms such core arguments Why? If a really good artist studies a single piece long enough, he could be able reproduce it to a degree where it takes expert analysis to determine which is the original. It's not as if there have never been forgeries of expensive artworks. The difference between a human studying a certain piece intensely, and a model overfitting to it, is that to the model, it happens by accident. Overfitting to the training set is not a desired outcome, it's something ML techniques are trying to actively avoid.
- haswell 4y ago
  > Why? If a really good artist studies a single piece long enough, he could be able reproduce it Part of the answer is in your question. A really good artist is rare, and forgery is a specialized skill. The point is not that humans are capable of forgery, but that the model/software is capable of it with minimal effort, and enables anyone regardless of skill to achieve similar outcomes. Setting aside the resulting privacy issues, this has major implications. Two are: 1. The risk of forgery previously accepted by a copyright framework that assumed human actors has drastically changed. 2. The claims that Stable Diffusion only produces derivative work and not originals was at the heart of the argument that says SD model training is fair use, and regardless of why, we know this is not true. > The difference between a human studying a certain piece intensely, and a model overfitting to it, is that to the model, it happens by accident. That is one difference in the factors surrounding the initial creation of the output. The differences continue to stack up as you examine the process of learning, the algorithms that produce new output, the computational context (both hardware and software), the agency of the entity creating the output, etc. The software doesn’t do anything by accident. It does exactly what it has been instructed to do. The humans training the model are at the core of any accident. This might seem obvious, but I think it bears restating because it highlights the nature of the situation. The model is not an independent actor. Whether or not it’s a desired outcome is not relevant. The fact that it is possible - and that this possibility has further implications about the nature of the software itself - is what I argue hurts the standard arguments claiming Stable Diffusion is not infringing by virtue of its similarity to human processes of thinking and expression.