Post #3387473

@orange_lux@eldritch.cafe @saxnot@chaos.social @dibi58@this.mouse.rocks @karl@infosec.exchange llms are useful for NLP. Inference is actually relatively cheap, it's the training that's really expensive and resource intensive. We've probably already maxed out LLM capabilities, so most of this training is not useful. Companies keep training because they need to convince investors that infinite growth is possible. What actual gains are being made are coming from architectural changes, not from training. Basically, "AI data centers" should not exist. Local models can do everything that's needed. If we need to train new models, those need to be balanced against climate goals (basically, don't fucking do it). And LLMs should be removed from basically everything they've been shoved into recently. If you don't know why LLMs are useful, you shouldn't have to interact with LLMs. Even some of the places where they are useful, they can be used to construct cheaper models. There are a few things, like correlation across huge data sets, that they're useful for. But even then, simple encoding can give you semantic search, where inference is not necessary or only provides minimal additional benefit. Yeah, basically, 95-99% reduction in cars and AI. It's basically the same thing.