- cross-posted to:
- hackernews@derp.foo
- cross-posted to:
- hackernews@derp.foo
Data poisoning: how artists are sabotaging AI to take revenge on image generators::As AI developers indiscriminately suck up online content to train their models, artists are seeking ways to fight back.
Yeah, no. There’s a difference between posting your work for someone to enjoy, and posting it to be used in a commercial enterprise with no recompense to you.
How are you going to stop that lol it’s ridiculous. Would you stop a corporate suit from viewing your painting because they might learn how to make a similar one? It’s makes absolutely zero sense and I can’t believe delulus online are failing to comprehend such simple concept of “computers being able to learn”.
Ah yes, just because lockpickers can enter a house suddenly everyone’s allowed to break and enter. 🙄
What a terrible analogy for learning 🙄
It’s not learning
It is. You should try it sometimes.
You’re not learning, that much is obvious.
Computers can’t learn. I’m really tired of seeing this idea paraded around.
You’re clearly showing your ignorance here. Computers do not learn, they create statistical models based on input data.
A human seeing a piece of art and being inspired isn’t comparable to a machine reducing that to 1’s and 0’s and then adjusting weights in a table somewhere. It does not “understand” the concept, nor did it “learn” about a new piece of art.
Enforcement is simple. Any output from a model trained on material that they don’t have copyright for is a violation of copyright against every artist who’s art was used illegally to train the model. If the copyright holders of all the training data are compensated and have opt-in agreed to be used for training then, and only then would the output of the model be able to be used.
It’s literally in the name. Machine learning. Ignorance is not an excuse.
That’s just one of the dumbest things I’ve heard.
Naming has nothing to do with how the tech actually works. Ignorance isn’t an excuse. Neither is stupidity
And yet you wield both!
There’s no copyright violation, you said it yourself, any output is just the result of a statistical model and the original art would be under fair use derivative work (If it falls under copyright at all)
Considering most models can spit out training data, that’s not a true statement. Training data may not be explicitly saved, but it can be retrieved from these models.
Existing copyright law can’t be applied here because it doesn’t cover something like this.
It 100% should be a copyright infringement for every image generated using the stolen work of others.
You can get it to spit out something very close, maybe even exact depending on how much of your art was used in the training (Because that would make your style influence the weights and model more)
But that’s no different than me tracing your art or taking samples of your art to someone else and paying them to make an exact copy, in that case that specific output is a copyright violation. Just because it can do that, doesn’t mean every output is suddenly a copyright violation.
However since it’s required to use all of the illegally obtained and in-licensed work to create it, it is a copyright violation, just as tracing over something would be. Again, existing copyright law cannot be applied here because this technology works in a vastly different way than a human artist.
A hard line has to be made that will protect artists. I’d prefer it go even farther in protecting individual copyright while weakening overall copyright for corporate owners.
It what jurisdiction is it illegal?
And is “obtained” even the right word?..
There’s currently multiple lawsuits in the courts to decide just that.
If they’re scraping the internet to add to a database of training data, I’d consider that obtaining and storing the work.
Wait until you find out how human artists learn.
And you don’t see how those two things are different?
And you don’t see how those two things are similar?
They learn completely different from an AI model, considering an AI model cannot learn
Prove it.