11–20 of 33 posts
No, it's capable of reasoning, but it gained its knowledge of how the world works through analyzing things that were written or drawn by (mostly) humans.
Reasoning how? LLMs are statistical models, not brains.
Earlier quoted context omitted.
Reasoning how? LLMs are statistical models, not brains.
Reasoning in behavior - you ask it to explain things and it does; it gives reasons, motivations, nuance, explains connections, clarifies points. That's reasoning.
Earlier quoted context omitted.
Reasoning in behavior - you ask it to explain things and it does; it gives reasons, motivations, nuance, explains connections, clarifies points. That's reasoning.
There's a pretty significant gap between being able to give reasons and being able to actually reason (as a verb). The latter requires complex cognitive faculties not present in a mathematical model of the English language.
Earlier quoted context omitted.
Reasoning how? LLMs are statistical models, not brains.
How are humans reasoning? Through a logical or statistical internal model. So if you make your model through slow thinking and discovery chatgpt just builds it to a billion words. The result is the same
A LLM (that hasn't been trained on any data regarding this topic) is unlikely to demonstrate the same bouba/kiki effect seen among humans. https://en.wikipedia.org/wiki/Bouba/kiki_effect
Earlier quoted context omitted.
There's a pretty significant gap between being able to give reasons and being able to actually reason (as a verb). The latter requires complex cognitive faculties not present in a mathematical model of the English language.
LLMs are tool, the important question to ask of a tool is "is this useful", not "does it work like my brain does". I don't see why you care about the internals, and not about the actual outputs.
No, it's capable of reasoning, but it gained its knowledge of how the world works through analyzing things that were written or drawn by (mostly) humans.
It's not capable of reasoning and it has no knowledge of how the world works. That's why it often spits out nonsense and falsehoods. To oversimplify a bit, it's a next-word-in-the-sentence prediction engine.
Earlier quoted context omitted.
It's not capable of reasoning and it has no knowledge of how the world works. That's why it often spits out nonsense and falsehoods. To oversimplify a bit, it's a next-word-in-the-sentence prediction engine.
While you're at it, throw out your calculator because it has no conception of numbers and doesn't know anything about mathematics.
Earlier quoted context omitted.
LLMs are tool, the important question to ask of a tool is "is this useful", not "does it work like my brain does". I don't see why you care about the internals, and not about the actual outputs.
This comment chain wasn't about outputs, it was specifically about whether or not LLMs "reason"—precisely the internals. I feel like you're just moving the goalposts now to deflect from the original question.
My point was that it should be.
Earlier quoted context omitted.
Reasoning in behavior - you ask it to explain things and it does; it gives reasons, motivations, nuance, explains connections, clarifies points. That's reasoning.
There's a pretty significant gap between being able to give reasons and being able to actually reason (as a verb). The latter requires complex cognitive faculties not present in a mathematical model of the English language.