this post was submitted on 16 Jun 2026
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No, it's not arbitrary, the learning is done by completely different software at a completely different time on completely different computers.
I'm pointing out that the LLM is the product of the machine learning, where you feed all of Wikipedia and reddit and stack overflow in and calculate the LLM from it.
It's like if you wrote a book about the wildlife of antarctica. First you would learn about the wildlife and then you would write the book. The book isn't learning anything and it isn't intelligent. The book represents knowledge but it doesn't itself know or understand anything.
Similarly, the LLM isn't learning anything and it isn't intelligent, it's just regurgitating randomly selected words from its training data that look like they usually occur after the other words in the conversation so far.
It genuinely doesn't understand a word you said, using its guess about what sort of conversation it's supposed to be having and it's always just guessing what word it's supposed to say next.
Wait, so you do think there is a software that "does learning"?
Isn't that the whole ballgame? You're saying LLM companies have made software that learns math and coding and translation at a near-expert level, you just happen to be under the impression that this software isn't present in deployed LLMs?
Unfortunately, the training algorithm that turns stochastic noise into trained LLMs is dumb as bricks. Engineers have done some tricks, but it's basically gradient descent over the vector space of text tokens/phonemes. What allows the stochastic noise to become something that can make novel statements about wildlife (like giving a halfway coherent answer to a question that has never been asked in its dataset) is the patterns that form within the weights and biases. It's vaguely like slime mold exploring a maze; the "teacher" can just be a stupid lump of sugar, it's the "student" that does the emergently complex task of finding the best route by executing simple algorithms in every part of itself.
Effectively, this means the LLM is the software that wrote the LLM. It is the software that did the learning.
What is the difference between understanding a thing (to a certain degree) and being able to generate an accurate narrative about the thing (to the same degree)? Can you give any evidence that I've understand any word you've said? How can you tell I haven't just been very good at guessing what word I'm supposed to say next based on the many arguments I've read or participated in?
Do you mean more by 'understanding' than expressed in the Chinese Room argument?