This gets very murky very fast when you start to think how humans learn and process, we’re just meaty pattern matching machines.
Isn’t it a probabilistic extrapolation? Isn’t that what a guess is?
vii@lemmy.ml 3 days ago
HugeNerd@lemmy.ca 2 days ago
In people, even animals. In a pile of disorganized bits and bytes in a piece of crap? No.
Iconoclast@feddit.uk 3 days ago
It’s a Large Language Model. It doesn’t “know” anything, doesn’t think, and has zero metacognition. It generates language based on patterns and probabilities. Its only goal is to produce linguistically coherent output - not factually correct one.
It gets things right sometimes purely because it was trained on a massive pile of correct information - not because it understands a anything it’s saying.
So no, it doesn’t “guess.” It doesn’t even know it’s answering a question. It just talks.
vii@lemmy.ml 3 days ago
I know some humans that applies to
SuspciousCarrot78@lemmy.world 2 days ago
A fair point but often over stated, I feel. And it overlooks something -
Language itself encodes meaning. If you can statistically predict the next word, then you are implicitly modeling the structure of ideas, relationships, and concepts carried by that language.
You don’t get coherence, useful reasoning, or consistently relevant answers from pure noise. The patterns reflect real regularities in the world, distilled through human communication.
Yes, that doesn’t mean an LLM “understands” in the human sense, or that it’s infallible.
But reducing it to “just autocomplete” misses the fact that sufficiently rich pattern modeling can approximate aspects of reasoning, abstraction, and knowledge use in ways that are practically meaningful, even if the underlying mechanism is different from human thought.
TL;DR: it’s a bit more than just a fancy spell check. ICBW and YMMV
Iconoclast@feddit.uk 2 days ago
No, I completely agree. My personal view is that these systems are more intelligent than the haters give them credit for, but I think this simplistic “it’s just autocomplete” take is a solid heuristic for most people - keeps them from losing sight of what they’re actually dealing with.
I’d say LLMs are more intelligent than they have any right to be, but not nearly as intelligent as they can sometimes appear.
The comparison I keep coming back to: an LLM is like cruise control that’s turned out to be a surprisingly decent driver too. Steering and following traffic rules was never the goal of its developers, yet here we are. There’s nothing inherently wrong with letting it take the wheel for a bit, but it needs constant supervision - and people have to remember it’s still just cruise control, not autopilot.
The second we forget that is when we end up in the ditch. You can’t then climb out shaking your fist at the sky, yelling that the autopilot failed, when you never had autopilot to begin with.
SuspciousCarrot78@lemmy.world 2 days ago
I think were probably on the same page, tbh. OTOH, I think the “fancy auto complete” meme is a disingenuous thought stopper, so I speak against it when I see it.
I like your cruise control+ analogy. Its not quite self driving… but, it’s not quite just cruise control, either. Something half way.
LLMs don’t have human understanding or metacognition, I’m almost certain.
But next-token prediction suggests a rich semantic model, that can functionally approximate reasoning. That’s weird to think about. It’s something half way.
With external scaffolding memory, retrieval, provenance, and fail-closed policies, I think you can turn that into even more reliable behavior.
And then… I don’t know what happens after that. There’s going to come a point where we cross that uncanny valley once and for all. May we live in interesting times, as the old curse goes.
KeenFlame@feddit.nu 3 days ago
Yes it guesstimates what is wrong with you to argue like that about semantics?