197
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 16 Dec 2023
197 points (99.5% liked)
Technology
59080 readers
3487 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each another!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
Approved Bots
founded 1 year ago
MODERATORS
Training one AI with the output of another AI will just make an even crappier AI.
Ever since that paper about "model decay" this has been a common talking point and it's greatly misunderstood. Yes, if you just repeatedly cycle content through AI training over and over through successive generations, you get AIs that lose "fidelity." But that's not what any actual real world training regimen using synthetic data does. The helper AI is usually used to process input data. For example, if you're training an AI to respond in a chat-like format, you could take raw non-conversational text (like a book) and have the helper AI create a conversation about that content for the new AI to learn from. Or to take a real-world example, Dalle3 was trained by having a helper AI look at pictures and create detailed text descriptions of them to use as the caption to associate with the image when training.
OpenAI has put these restrictions in its TOS as a way of trying to "pull up the ladder behind them", preventing rivals from trying to build AIs as good as the ones they have already. Fortunately it's not going to work. There are already open LLMs that can be used as "helpers" without needing OpenAI at all. ByteDance was likely just being lazy here.
There is actually a whole subsection of AI focused on training one model with the output of another called knowledge distillation.
Works kinda neat with stable Diffusion tho
Depends how it's done. GAN (Generative Adversarial Network) training works with exactly that, having networks train against each other, each improving the other over time.
I've watched Multiplicity enough times to know you get a slightly less functional copy.
She touched my peppy Steve.
That wasn't a documentary, and it wasn't about machine learning.
Like photocopying a picture of a terd
deleted
Not necessarily: there have been recent works that indicate that filtering effects of fine tuned LLMs greatly improves the data efficiency (e.g phi-1). Further, if you have e.g. human selection on top of LLM generated content you can get great results as the LLM generation can be used as a soft curriculum, with the human selection biasing towards higher quality.
Sounds like what you'd get if you ordered a ChatGPT off of Wish dot com. Cheap knock-offs that blatantly steal ideas/designs and somewhat work are kinda their thing.