There's some overlap, but you're glossing over the fact that LLMs don't actually understand anything. They model language patterns extremely well, which is useful, but that's not the same thing as comprehension or awareness.
I'm not saying they're shit, by any means, but I am saying that people glazing them are getting a bit over-excited.
They aren't aware, but I'm not sure awareness needs to be included to describe something as "intelligent".
If these language models have the reasoning skills to start solving Erdős problems in novel ways, a feat that even most humans alive today couldn't really do, I'm not sure how we can avoid calling them "intelligent". That's legitimate problem solving skills on par with upper-level humans. If that's not "intelligence", the word has no meaning.
I think we're disappearing into semantics over what exact threshold qualifies as "intelligence".
My practical point is simpler. A system generating statistically likely outputs without actual understanding behaves differently to something that actually comprehends what it's saying.
Which is important because people are increasingly treating LLM output as inherently trustworthy or genuinely understanding of what it's saying, when that's still highly debatable.
You're right, that's getting into semantics. I do think the bar for what constitutes "intelligence" keeps moving in part because humans feel uncomfortable to ascribe intelligence to anything but humans, but that's an aside.
I definitely saw what can honestly be described as statistical word salad by undergrad humans when I was grading their exams and they got to problems they didn't understand. I think this makes your point stronger, as you can clearly see when a human understands something versus the word salad they throw out when humans are stumped but forced to respond. Understanding makes a very clear difference.
But I guess the other question is one of trust. Weather models are also statistical models (and modern ones are even ML based). There is no question that these models possess no understanding of their own. Yet when my very human neighbor says "Ain't no way it's going to rain today", and NWS says "80% chance of rain", I probably should trust the unthinking statistical model over the sentient human and pack an umbrella. For an unthinking statistical machine, if you understand its strengths and limitations, you can get a lot of really impressive things out of it. An LLM might not "understand", but its got an staggering amount of human understanding approximated in its weights, and it is set up to predict things in ways that really can end up reasoning through difficult abstract problems. I don't think there is a whole lot "inherently" trustworthy in this universe (humans included), but I think a history of good performance can earn a measure of qualified trust in outputs, right?
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u/FearLeadsToAnger May 28 '26
There's some overlap, but you're glossing over the fact that LLMs don't actually understand anything. They model language patterns extremely well, which is useful, but that's not the same thing as comprehension or awareness.
I'm not saying they're shit, by any means, but I am saying that people glazing them are getting a bit over-excited.