this post was submitted on 18 Oct 2023
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Your claim was about current LLMs.
But it's a fundamental limitation of what LLMs are. They are not AI. They do not have anything in common with intelligence, and they don't have a particularly compelling path forward.
They also, even if they weren't actually terrible for almost every purpose, are obscenely heavy and what we're calling "current" isn't something capable of being executed on consumer hardware, dedicated card or not.
Finally, the idea that they can't get worse is just as flawed. They're heavily poisoning the well of future training data, and ridiculous copyright nonsense has the very real possibility of killing training further even though training on copyrighted material doesn't in any way constitute copyright infringement.
Maybe open source LLMs aren't up to the task, but proprietary ones certainly are.
Also, you wouldn't really need a LLM, just a FM that you fine tune for your specific purpose.
What's this thing you call FM?
It's a foundation model. Basically it's the base algorithm that you train with data. LLMs are FMs that have been trained with an enormous amount of data, but they aren't necessary for every application, especially if you only need the AI/ML to perform a specific task.
Fine tuning an FM is just feeding it your own data.