Of course it learned, that's the point of training.
You claimed the model can reproduce an image from that training data. That's false, and what the judge dismissed.
“none of the Stable Diffusion output images provided in response to a
particular Text Prompt is likely to be a close match for any specific image in
the training data.”
“I am not convinced that copyright claims based a derivative theory can
survive absent ‘substantial similarity’ type allegations,” the ruling stated.
Whether using copyrighted data to train a model is fair use or not is a different discussion.
As I've read it, the first lead to the material was 99.
2018 They got funding to research it further,
2020 was a first attempt of publication at Nature that was retracted, further improvements were made until 22/23 were two patents were filled, then suddenly 10 days ago Kwon, one of the co-researchers jumped the gun publishing a paper with the details, on one hand fearing a leak of someone else publishing first as that was too simple to replicate, on the other hand excluding everyone else from the paper and only listing him and Lee/Kim (LK) as authors as a Nobel prize can only be shared by three people. 2.5hrs later LK published again listing other 5 authors but him.
First, Reddit's monetization is broken by design. It never made any sense to me why they would charge for reddit gold for an ad-free experience on their website and own mobile app but not on the API. Why would they let third party apps serve their own ads and let them charge to remove them? This would be simple to fix, both technically and in the API's ToS, just serve the same ads regardless of the client. People would be upset, but ultimately I feel it would be entirely fair. But no, it doesn't seem to be a solution considered.
Second, the LLM dataset issue is also attributed to the price hike. Again, I think it's fair if unpopular to charge premium for bulk data. Again, there are technical and ToS solutions for this. They could introduce exponential tiers for bulk data, restrictions on allowed usage, other things that make user-facing usage reasonable but bulk processing expensive, but then again, starting measuring api usage per client id and not per user goes against this point, just making the API extremely expensive for everyone anywhere to the point of being unusable.
Third, all points seem to lead to the fact that what they really want is to kill third party apps and hope a large part of those users move to their app, for what? More tracking, tighter grip, better engagement metrics? Not sure. Even the changes to the extremely hostile mobile site now forcing some users to download the app. Really, I'd figure they'd understand their userbase better than that and how a small fraction on content producers and a even smaller fraction of power users and moderators carry the site, and pissing them off is a really bad idea. But what do I know.
It's a little confusing, but as far as a understand it, Stable Diffusion was created by a collaboration between RunwayML and Compvis (aka Machine Vision and Learning research group at Ludwig Maximilian University of Munich) with Stability.AI funding the computing power for training and LAION.AI (also funded by Stability.AI) providing the dataset.
The first few releases of model and code have been done by Compvis, and this one by RunwayML. More than permitted by the license, this seems to have been released by the actual developers.
I've seen speculation that it has been released this way to provide distance between Stability.AI from eventual future litigations, but it feels more like an internal ultimatum deadline.
1. unsubscribe to all the big default subreddits, like askreddit, funny, etc (don't worry, you can still check them out if you want).
2. go to r/all and use the filter feature to block the most annoying content or popular stuff that you absolutely don't care about. I blocked politics subreddits, some memes, anime, communities for popular youtubers, some of the worse default ones. Just look at the current /r/all listing and block whatever you don't care about that appears in the first few pages, refresh and do it again a few times. I go back every once in a while to repeat the process.
3. subscribe to specific things you care about. smaller communities are better, some of the large ones are better moderated than others.
4. favorite a few (3-4) subreddits that are about things I want to check often.
My home feed is mostly tailored to my interests, even if there's some fluff. Smaller subreddits I don't check often and appear there. Then I check my favorite subreddits for specific things, and there's r/all for the popular stuff.
I find that general topics like tech, music, sports are usually bad, but more specific, not necessarily niche, are better (a sub about a specific framework, maybe, or about your hometown, favorite band, or favorite team). Moderation style helps a lot.
I've long had this notion that there are two kinds of content on a site like HN:
- temporal, like news, announcements, analysis and discussion on current events. It's usually irrelevant after a while, it gets stale fast.
- atemporal, like essays, history, theory, articles, knowledge in general that while it may get superseded, expanded or invalidated over time, it's interesting content that you learn from, from a current or historical perspective.
While the first kind tends to get more attention due to the clickbaity nature, I much prefer the second kind and wonder if a site focused solely on that would be more interesting.
Recently they added notifications saying "multiple people are typing" and "see new comments!" which appear on posts even when newest comments are old. It's absolutely infuriating. I didn't want to cure my reddit addiction but they are trying real hard to help me with that.
You claimed the model can reproduce an image from that training data. That's false, and what the judge dismissed.
Whether using copyrighted data to train a model is fair use or not is a different discussion.