alt Hacker News>>> How is this different from a human using an algorithm they have memorized ...
>> Humans identify which "algorithm they have memorized" to use beforehand, due to the problem to be solved being defined by other humans ...
> This doesn't make any sense at all. Was this supposed to be a gotcha?
No, it was meant to be an explanation as to the difference between "memorization" and "understanding." In this context, people pick the algorithm they determine applicable and then the question of memorization is relevant.
> An LLM is trained on problems defined by other humans, and identifies which algorithm it must use based on pattern recognition.
Funny that you make this argument here, where when I wrote elsewhere in this thread:
[LLMs] are statistical token generators whose results are
dependent upon their training data set and involve a
degree of randomness.
Nothing more.
...
It is pattern recognition, a task in which ANNs excel.
During conversation, we are statistical token generators
whose results are dependent upon our training set.
Seriously, write that definition out rigorously. It
encompasses virtually everything. It is totally
meaningless. So to say "nothing more" is effectively also a
tautology.
This argument was asinine in 2024. It is insane to be
saying these things in 2026. Where have you been?
...
It absolutely understands how to do math, by whatever
reasonable definition you want to provide to the word
"understand".
Are LLMs ANNs? Which themselves are pattern recognition algorithms (hint: they are)?
OR (setting aside the ad hominems you kindly provided)
Do LLMs possess "understanding" of concepts such as abstract mathematics (defined and interpreted by humans) and we, as simple humans, nothing more than statistical token generators as you assert?
Because it cannot be both.
It is both. I do not understand why you would assert that both cannot hold simultaneously. Pattern recognition becomes "understanding" once individually recognized concepts become sufficiently sparsified and compactified. Or, at least, that is to my knowledge the only mathematically valid definition of "understanding" one can produce at this scale (it is valid under Solomonoff induction). Philosophy is fine, but we need to have a consistent definition of what "understanding" means, or we will just talk past each other. I argue that for any proper definition you provide which humans satisfy, a strong LLM is very likely to satisfy that as well.
I also would not argue that humans are "simple token generators". That is not what I said. I said that just about everything can fall under the classification of "statistical token generators" at an abstract level, so it isn't a useful distinction. We are not talking about a Markov chain generator from the 90s, so if that is the frame of reference, I think we should all get that out of our heads.