this post was submitted on 29 Nov 2023
435 points (97.4% liked)

Technology

59217 readers
2773 users here now

This is a most excellent place for technology news and articles.


Our Rules


  1. Follow the lemmy.world rules.
  2. Only tech related content.
  3. Be excellent to each another!
  4. Mod approved content bots can post up to 10 articles per day.
  5. Threads asking for personal tech support may be deleted.
  6. Politics threads may be removed.
  7. No memes allowed as posts, OK to post as comments.
  8. Only approved bots from the list below, to ask if your bot can be added please contact us.
  9. Check for duplicates before posting, duplicates may be removed

Approved Bots


founded 1 year ago
MODERATORS
 

ChatGPT is full of sensitive private information and spits out verbatim text from CNN, Goodreads, WordPress blogs, fandom wikis, Terms of Service agreements, Stack Overflow source code, Wikipedia pages, news blogs, random internet comments, and much more.

you are viewing a single comment's thread
view the rest of the comments
[–] [email protected] 7 points 11 months ago (2 children)

I wonder if this kind of cut/paste happens with image generators. Do they sometimes output an entire image from their training data? Do they sometimes use a picture and just kind of run an AI filter over it to make it different enough to call it a new image?

[–] [email protected] 9 points 11 months ago (1 children)

Diffusion AI (most image AI) works differently than an LLM. They actually start with noise, and adjust it iteratively to satisfy the prompt. So they don't tend to reproduce entire images unless they are overtrained (i.e. the same image was trained a thousand times instead of once) or the prompt is overly specific. (i.e you ask for "The Mona Lisa by Leonardo")

But words don't work well with diffusion, since dog and God are very different meanings despite using the same letters. So an LLM spits out a specific sequence of word tokens.

[–] [email protected] 2 points 11 months ago

You could use diffusion to generate text. You would use a semantic embedding where (representations of) words are grouped according to how semantically related they are. Rather than dog/God, you would more likely switch dog for canine. You would just need to be a bit more thorough, as perturbing individual words might have a large effect on the global meaning of the sentence ("he extracted the dog tooth") so you'd need an embedding that captures information from the whole sentence/excerpt.