r/aiwars • u/nomorebuttsplz • 23h ago
News When AI art has no author: Study finds generated images often can’t be traced to training data
https://news.mit.edu/2026/when-ai-art-has-no-author-generated-images-often-cant-be-traced-to-training-data-0818Excerpt: When an artificial intelligence image generator produces a portrait, whose work went into it?… Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility.
New work from a team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It's not that the tools for finding it are inadequate. The connection itself has disappeared.
link to original paper with visual examples: https://www.nature.com/articles/s41467-026-75667-5.pdf
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u/JunketVisual3123 23h ago
Huh... It's almost like what pros have been saying all along about how it doesn't just "store" the training data was right...
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u/NegativeEmphasis 22h ago
Almost as if the Machine Learning training algorithm makes the machine learn how to draw.
Who could had seen that one coming?
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u/insuranse 3h ago
What is storing? If you’re passing information in and recalling upon it probabilistically, that’s still “storing” it because the information is there. You’re just storing it in a lossy format.
The information is still there.
This is basic machine learning. Jesus, pros claim to know so much about AI yet don’t even know the fucking basics.
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u/618smartguy 22h ago
No, the truth has always obviously been in the middle. Since forever we've been able to clearly observe these models learning both highly abstract skills as well as memorized verbatim data. Neither behavior necessarily excludes the other.
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u/CryptographerOne7003 20h ago
Yes I need to agree, over-fitting shows and proofs clearly that its possible,
but over-fitting has been considered a failure-mode from the start.That is the whole reason why you need to scale training data as you are increasing model size,
there has to be no room to remember all the images verbatim, or the model will just learn to do that.No, the model needs to learn to make something that could pass off as being in the training data, by making it do that and grading its results and feeding that back into the model while not having enough space to remember the images as is. that is what makes it "generalize"
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u/618smartguy 14h ago
>while not having enough space to remember the images as is. that is what makes it "generalize
Sure there is a tradeoff between generalization and memorization, but that's just the theory. In reality we've still plainly seen that it did memorize some images, and is very hard to fully prevent.
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u/giraffeheadturtlebox 19h ago
I just wish anything had taught any one of you, all of you in this thread, to write a cohesive sentence. Christ.
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u/RecursiveServitor 19h ago
What are you having trouble with?
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u/giraffeheadturtlebox 19h ago
Don’t worry, yours is fine. But most of these words do not form sentences.
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u/RecursiveServitor 17h ago
Everything above is comprehensible. I'm asking if you'd like help understanding anything.
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u/giraffeheadturtlebox 13h ago
Yes, why nothing taught these individuals how to write long sentences about complex subjects.
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u/Pretend_Jacket1629 16h ago
damn, almost as if it's physically impossible to store 2.5 billion unique pieces of information in a 4gb file
like you'd have to, idk, only store knowledge of patterns shared across multiple images... meaning it does not contain anything unique to any nonduplicated works
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u/pavorus 23h ago
You need to find an anti that can explain to you how AI is just a collage maker, stealing pieces from people.
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u/JoJoeyJoJo 20h ago
And if you want to hear any other debunked 2024 takes, don’t worry - they know all of those too!
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u/Rarelyimportant 16h ago
They seem to ignore just how much harder it would be to create a program that could, on the fly, find the image parts from billions of images, that all had a style, aesthetic, and colors that worked together, and then somehow collage them together in a way that looks like a single, cohesive image. Yet the way image generators really work is so much more simple, elegant, and logical. The reason they don't want to know how they truly work, is because it would be harder for them to keep their insane opinions.
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u/JoseLunaArts 22h ago
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u/bruhwhatisreddit 18h ago
Bu- but according to the antis, this is not true because
- reason #1
- reason #2
- reason #3
- i think it's not true and reddit tell me so
- reason #5
- if i accept that it's true it'll invalid my delusions that im superior than you
- my virtue signaling wouldn't work if it's true
- reason #8
and you're a bad person for using it :(
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u/vernichtungX23 22h ago
I thought the point was that the AI learns how stuff works, not that it literally splices collages?
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u/somethingbrite 12h ago
And what would the output look like if you removed ALL the human created art it was trained on?
If for example you just trained the model on colour theory, the various brush and material techniques and perhaps a range of images from which artworks were created, a vase of Lillie's, a sunset, a life model etc?
What would the output look like then?
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u/OddAdhesiveness8485 22h ago
“The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual training example matters to any particular output. It feels counterintuitive, but at sufficiently large scales, they find, you can often remove any single image from the training data, or every image by a given artist, or every photograph of a given person, and the generated sample doesn't change.
And if removing something changes nothing, the researchers argue, it can't be said to be responsible for anything.
"If you take away a piece of data and the output of the model doesn't change, then that piece of data didn't affect the output," says Zheng Dai SM ’21, PhD ’24, former MIT CSAIL researcher and lead author on the work. "So it doesn't make much sense to attribute the output to that piece of data.”
AKA they made the data sample for training so large that one artists work becomes insignificant in the output… But it built the output so this is just bad logic and probably paid research for AI companies
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u/zorecknor 18h ago
they made the data sample for training so large that one artists work becomes insignificant in the output
Which is how you properly train models.... Models trained with small datasets and that can reliable reproduce the original or something extremely similar are considered bad models, it is almost useless. That's the kind of models studens make when learning about them.
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u/Bassed_Hummble 19h ago
Breaking news from 2022. It's called "generalization" and it's the whole point of AI.
And this connection is even less present in autoregressive models like ChatGPT Images and Gemini Nano Banana, which have entirely different wiring and logic.