Post #3284997
2026-05-31 09:37 UTC
The reason labs focus on LLMs is that language is a great substrate for generalization. Good luck trying to one-shot out of distribution problems using classic neutral networks. They've tried for decades to make it happen but LLMs surpassed those results in a few years.
Replies (2)
-
@placebo@lemmy.zip 2026-05-31 13:28
Idk. LLMs don't seem like a good solution because of how many resources they need to train and run compared to specialized models.
-
@Th4tGuyII@fedia.io 2026-05-31 13:49
I understand that idea, but at the same time @placebo@lemmy.zip has a point. There's a good reason why you generally don't get a CPU to do graphics and why FPGAs are usually only put on dev units. Specialist hardware is generally much more efficient cost and energy wise than generalist hardware for a given task. And I imagine that must be true for neural networks too, as that layer of language processing on top of any task naturally can't be as efficient/performatative as specialist software/networks made for the job.