this post was submitted on 21 Oct 2024
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The person you're replying to is correct though. They do not understand, they do not analyse. They generate (roughly) the most statistically likely answer to your prompt, which may very well end up being text representing an accurate analysis. They might even be incredibly reliable at doing so. But this person is just pushing back against the idea of these models actually understanding or analysing. Its slightly pedantic, sure, but its important to distinguish in the world of machine intelligence.
I literally quoted the word for that exact reason. It just gets really tiring when you talk about AIs and someone always has to make this point. We all know they don't think or understand in the same way we do. No one gains anything by it being pointed out constantly.
You said "they literally do analyze text" when that is not, literally, what they do.
And no, we don't "all know" that. Lay persons have no way of knowing whether AI products currently in use have any capacity for genuine understanding and reasoning, other than the fact that the promotional material uses words like "understanding", "reasoning", "thought process", and people talking about it use the same words. The language we choose to use is important!
No it's not. It's pedantic and arguing semantics. It is essentially useless and a waste of everyone's time.
It applies a statistical model and returns an analysis.
I've never heard anyone argue when you say they used a computer to analyse it.
It's just the same AI bad bullshit and it's tiring in every single thread about them.
I never made any "AI bad" arguments (in fact, I said that they may be incredibly well suited to this) I just argued for the correct use of words and you hallucinated.
LLMs arent "bad" (ignoring, of course, the massive content theft necessary to train them), but they are being wildly misused.
"Analysis" is precisely one of those misuses. Grand Theft Autocomplete can't even count, ask it how many 'e's are in "elephant" and you'll get an answer anywhere from 1 to 3.
This is because they do not read or understand, they produce strings of tokens based on a statistical likelihood of what comes next. If prompted for an analysis they'll output something that looks like an analysis, but to determine whether it is accurate or not a human has to do the work.
LLMs cannot:
LLMs can
Semantics aside, they're very different skills that require different setups to accomplish. Just because counting is an easier task than analysing text for humans, doesn't mean it's the same it's the same for a LLM. You can't use that as evidence for its inability to do the "harder" tasks.
The human capacity for reason is greatly overrated. The overwhelming majority of conversation is regurgitated thought, which is exactly what LLMs are designed to do.
I don't really dispute that but at least we are able to apply formal analytical methods with repeatable outcomes. LLMs might (and do) achieve a similar result but they do so without any formal approach that can be reviewed, which has its drawbacks.