https://vox.com/recode/23580554/generative-ai-chatgpt-openai...
It depends what you call "serious" but on their government relations page https://www.adobe.com/about-adobe/government-relations.html they say "Adobe discloses information on our federal and state lobbying activities. Our most recent federal lobbying disclosures are listed on the U.S. Senate lobbying disclosure database and U.S. House lobbying disclosure database."
If you look at the forms on those databases you can see that Adobe has a "Policy, Government, and Ethical Innovation team in Washington, D.C." which I guess is what they call their lobbying crew.
> It's Disney you've got to watch out for
Probably most lobbying efforts would be considered 'not serious' if the standard is Disney!
If you used a really cleverly and exhaustively and iteratively prompted midjourney image to recreate a copyrighted painting, that WOULD be duplication, just like a physical artist can paint a copy of a copyrighted painting by hand.
Duplication is the end result, not the intermediary. That's why transformative collage is fine.
if you turn the temperature value down to zero it will produce the same thing every time given the same input (in the same way zlib or jpeg will)
it's a lossy compression function that is implemented using a neural net
copyright isn't removed if I take someone's existing image and re-save it as a jpg
if the courts agree and this is ruled to be infringement then we'll left with products with level of quality we see here
meaning the technology will be an economic dead end
here's to hoping
I don't think, given that we don't even have a particularly full understanding of how human conceptual logic functions, that we can claim that even AIs are using it as well. It's only "abstracting" in the sense that it has labeled one million objects in its training set with the word "tree," and fuses many of those images together to form a general picture dependent on specific parameters made to limit its set (oak tree, winter tree, etc.)
But that is different from me or you using the word tree, which is just a signifier among signifiers, it stands for nothing but a negation of the very thing it points to in a certain set of symbolic relations. Humans communicate in the order of symbolic structures, our minds function much more like LLMs, creating multitudinous pattern relations. What you call "abstract concepts" are of a secondary order imposed to create rigorous exactitude overtop the riddled mess that we call the human psyche.
What the model does is turn the picture into numbers in a clever way (embedding).
Then, the model compares the relationships between all the embedded tokens (self-attention).
Then the model does the fancy linear algebra stuff we've come to know and love (feed forward neural network).
What you're basically getting at with your temperature comment is "rather than having it semi-randomly pick one of the top n options that the feed-forward network recommends for which pixel goes where, we can always have the model simply choose the top answer each time."
That is not "we can make it reproduce training images"
The point of embedding is to see multiple examples of similar images and generalize from them. The process in and of itself breaks the ability to re-create a photo, unless the training set is very skewed.
If that's the argument of legal cases, they are likely to fail or win on ignorance.
didn't say that, all I said is it's a deterministic lossy compression function of the inputs (unless randomness is deliberately introduced)
and as such it should be treated the same way as zlib or jpeg for copyright purposes
For example, switching training order of images has a known impact on outputs, but I don't see the equivalent in traditional compression.
I agree with you that it is computer code that runs on circuits (and is therefore deterministic in a very loose sense), but it is fundamentally incorrect to compare it to traditional image compression.
This would only be true if it didn't mix or 'forget' any of the training data for each prompt, and only then for the exact same inputs of the training data. That is a lot of qualifiers that aren't practically applicable.
Slight side note: a deterministic lossy compression function should not always be treated the same as jpeg/zlib, as hash functions are technically also in that category.
I wouldn't want to be the defense's lawyer when that image gets shown to a jury.
https://www.theverge.com/2023/2/6/23587393/ai-art-copyright-...
Making absurd arguments is fun!
(The reason this is absurd is the number of parameters in the model is incredibly small compared to the size of the training set. You wouldn't even have enough bits per training image to keep a unique identifier for each training image, let alone a compressed representation. And to the degree there's redundancy in the training set, this is undesirable, actually counter-productive to the functioning of the model.)
(In a way, the human brain is actually more capable of copyright infringement since the number of parameters in the human brain is about 4-5 times that of a typical image generative AI neural net, and we're trained on a smaller and less generalizable data set. Humans can and do make copies of things from memory, which generative AI massively struggles with and can only do for corner cases.)
https://en.wikipedia.org/wiki/Copyright_protection_for_ficti...
Arguably, however, (a) not all fictional characters are copyrightable in the first place, and (b) fair use still exists.
Adobe Illustrator should not prevent an artist from drawing Mickey Mouse using the software. Nor should MidJourney.
If Disney has a problem, they can go after the human using the tool.