The AI jobs crisis is here, now: It's not coming, it has already arrived.
The AI jobs crisis is here, now
cross-posted from: https://futurology.today/post/4613643
"We had been working with their AI tool for a while, and it was absolutely not at the point of being capable of writing lessons without humans.”
Lol, LMAO this is gonna be a disaster once the bubble crashes and current models undergo recursive degradation.
I do think LLMs are going to start getting worse once more data is fed into them. And then instead of admitting this. These companies will have so much capital they will just tune the AI to say exactly what they want it to every time. We already see some of this but it will get worse.
Reject LLMs branded as AI, retvrn to 9999999 nested if statements.
LLMs are going to start getting worse once more data is fed into them
I've been hearing this for a while, has it started happening yet?
And if it did, is there any reason why people couldn't switch back to an older version?
I knew the answer was "Yes" but it took me a fuckin while to find the actual sources again
https://arxiv.org/pdf/2307.01850 https://www.nature.com/articles/s41586-024-07566-y
the term is "Model collapse" or "model autophagy disorder" and any generative model is susceptible to it
as to why it has not happened too much yet: Curated datasets of human generated content with minimal AI content If it does: You could switch to an older version, yes, but to train new models with any new information past a certain point you would need to update the dataset while (ideally) introducing as little AI content as possible, which I think is becoming intractable with the widespread deployment of generative models.
The witting or unwitting use of synthetic data to train generative models departs from standard AI training practice in one important respect: repeating this process for generation after generation of models forms an autophagous (“self-consuming”) loop. As Figure 3 details, different autophagous loop variations arise depending on how existing real and synthetic data are combined into future training sets. Additional variations arise depending on how the synthetic data is generated. For instance, practitioners or algorithms will often introduce a sampling bias by manually “cherry picking” synthesized data to trade off perceptual quality (i.e., the images/texts “look/sound good”) vs. diversity (i.e., many different “types” of images/texts are generated). The informal concepts of quality and diversity are closely related to the statistical metrics of precision and recall, respectively [39 ]. If synthetic data, biased or not, is already in our training datasets today, then autophagous loops are all but inevitable in the future.
Sometimes yeah you can see it. Not only with updates but within a conversation, Models degrade in effectiveness long before context window is reached. Things like image generation tend to get worse after >2 edits and even if the image seed is given .
