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Generative AI tweaks the original material such that it's modified beyond recognition, and non verbatim. A bit like how artists tweak other artist's work, put t

Generative AI tweaks the original material such that it's modified beyond recognition, and non verbatim. A bit like how artists tweak other artist's work, put their own spin on it, and then pass it off as 'original'.

redcobra762 3y ago
If you ask it to sure, but there are examples of ChatGPT reproducing whole NYT articles, with not nearly enough alterations to constitute a new work.
- jondwillis 3y ago
  What were the prompts? A whole article would barely fit in the output tokens, typically, yeah?
  - redcobra762 3y ago
    https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec2023.pdf https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec20... Exhibit J (phone won’t cooperate with paste).
Xelynega 3y ago
An alternative viewpoint: Generative AI algorithmically processes the inputs such that they can be roughly recreated after decoding. A bit like how jpeg encodes images "beyond recognition"(aka lossy).
pclmulqdq 3y ago
Except when it doesn't. See the NYT's lawsuit here - long runs of the training data are spit out verbatim.
rvz 3y ago
Or produces the watermark from the copyright holder which even Getty can see it is theirs [0], implying that Stability AI trained on Getty's images without their permission and tried to commercialize it via DreamStudio. [0] https://www.technollama.co.uk/high-court-rules-that-getty-v-stability-ai-case-can-proceed https://www.technollama.co.uk/high-court-rules-that-getty-v-... [1] https://www.theverge.com/2023/1/17/23558516/ai-art-copyright-stable-diffusion-getty-images-lawsuit https://www.theverge.com/2023/1/17/23558516/ai-art-copyright...
HarHarVeryFunny 3y ago
GenAI CAN do that, but it also can regenerate training sources as-is. The way LLMs work makes it quite likely that once it has started copying a training sample it will continue to do so (after copying/generating N consecutive words of a training sample, the most statistically likely next word will be often be the N+1th word of that same sample). The same thing happens for images - the more an output looks like an input, the more likely (as diffusion-based generation proceeds) it is to look more like it. There are recent examples of Dune posters being recreated essentially as-is.

redcobra762 3y ago

If you ask it to sure, but there are examples of ChatGPT reproducing whole NYT articles, with not nearly enough alterations to constitute a new work.

jondwillis 3y ago
What were the prompts? A whole article would barely fit in the output tokens, typically, yeah?
- redcobra762 3y ago
  https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec2023.pdf https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec20... Exhibit J (phone won’t cooperate with paste).

Xelynega 3y ago

An alternative viewpoint: Generative AI algorithmically processes the inputs such that they can be roughly recreated after decoding. A bit like how jpeg encodes images "beyond recognition"(aka lossy).

pclmulqdq 3y ago

Except when it doesn't. See the NYT's lawsuit here - long runs of the training data are spit out verbatim.

rvz 3y ago

Or produces the watermark from the copyright holder which even Getty can see it is theirs [0], implying that Stability AI trained on Getty's images without their permission and tried to commercialize it via DreamStudio. [0] https://www.technollama.co.uk/high-court-rules-that-getty-v-stability-ai-case-can-proceed https://www.technollama.co.uk/high-court-rules-that-getty-v-... [1] https://www.theverge.com/2023/1/17/23558516/ai-art-copyright-stable-diffusion-getty-images-lawsuit https://www.theverge.com/2023/1/17/23558516/ai-art-copyright...

HarHarVeryFunny 3y ago

GenAI CAN do that, but it also can regenerate training sources as-is. The way LLMs work makes it quite likely that once it has started copying a training sample it will continue to do so (after copying/generating N consecutive words of a training sample, the most statistically likely next word will be often be the N+1th word of that same sample). The same thing happens for images - the more an output looks like an input, the more likely (as diffusion-based generation proceeds) it is to look more like it. There are recent examples of Dune posters being recreated essentially as-is.