You're comparing the physical item with the reasoning capabilities of that item.
A popular LLM joke at the moment is "How many Rs in strawberry?"
You, a human being, understand this question. The purpose of the activity is to count the letter R in that word and give the one correct answer.
LLMs don't understand any of that. They don't know what words are, what letters are, what numbers are, what counting is, that a question can have one definite correct answer or many answers, what a question is, nor what an answer is.
They break your input into tokens and then look at the most likely set of output tokens given your input. That's all.
If you train a model on enough sensible input tokens and sensible responses then the output will mostly seem human and sensible. But it's never reasoning.
A popular LLM joke at the moment is "How many Rs in strawberry?"
You, a human being, understand this question. The purpose of the activity is to count the letter R in that word and give the one correct answer.
LLMs don't understand any of that. They don't know what words are, what letters are, what numbers are, what counting is, that a question can have one definite correct answer or many answers, what a question is, nor what an answer is.
They break your input into tokens and then look at the most likely set of output tokens given your input. That's all.
If you train a model on enough sensible input tokens and sensible responses then the output will mostly seem human and sensible. But it's never reasoning.