I wasn’t attempting to attack what you said, merely pointing out that once you cross the line into philosophy things get really murky really fast.

You assert that LLMs aren’t taught the rules, but every word is not just a word. The tokenization process includes part of speech tagging, predicate tagging, etc. The ‘rules’ that you are talking about are actually encapsulated in the tokenization process. The way the tokenization process for LLMs, at least as of a few years ago when I read a textbook on building LLMs, is predicated on the rules of the language. Parts of speech, syntax information, word commonality, etc. are all major parts of the ingestion process before training is done. They may not have had a teacher giving them the ‘rules’, but that does not mean it was not included in the training.

And circling back to the philosophical question of what it means to “learn” or “know” something, you actually exhibited what I was talking about in your response on the math question. Putting to piles of apples on a table and counting them to find the total is a naïve application of the principals of addition to a situation, but it is not describing why addition operates the way it does. That answer does not get discussed until Number Theory in upper division math courses in college. If you have never taken that course or studied Number Theory independently, you do not know ‘why’ adding two numbers together gives you the total, you know ‘that’ adding two numbers together gives you the total, and that is enough for your life.

Learning, and by extension knowledge, have many forms and processes that certainly do not look the same by comparison. Learning as a child is unrecognizable when compared directly to learning as an adult, especially in our society. Non-sapient animals all learn and have knowledge, but the processes for it are unintelligible to most people, save those who study animal intelligence. So to say the LLM does or does not “know” anything is to assert that their “knowing” or “learning” will be recognizable and intelligible to the lay man. Yes, I know that it is based on statistical mechanics, I studied those in my BS for Applied Mathematics. I know it is selecting the most likely word to follow what has been generated. The thing is, I recognize that I am doing exactly the same process right now, typing this message. I am deciding what sequence of words and tones of language will be approachable and relatable while still conveying the argument I wish to levy. Did I fail? Most certainly. I’m a pedantic neurodivergent piece of shit having a spirited discussion online, I am bound to fail because I know nothing about my audience aside from the prompt to which you gave me to respond. So I pose the question, when behaviors are symmetric, and outcomes are similar, how can an attribute be applied to one but not the other?

The Cult of Pythagoras would like to have a word with you.

That doesn’t stop them from being obsolete, it just means that people who have the “if it ain’t broke, don’t fix it” mentality can get fucked.

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See, I agree with everything up to the end. There you are getting into the philosophy of cognition. How do humans answer a question? I would argue, for many, the answer for most topics would be "I am repeating what I was taught/learned/read. An argument could be made that your description of responding with “What would a realistic answer to this question look like?” is fundamentally symmetric with “This is what I was taught.” Both are regurgitating information fed to them by someone who presumably (hopefully) actually had a firm understanding of the material themselves. As an example: we are all taught that 2+2=4, but most people are not taught WHY 2+2=4. Even fewer are taught that 2+2=11 in base 3 or how to convert bases at all. So do people “know” that 2+2=4 or are they just repeating the answer that they were told was correct?

I am not saying that LLMs understand or know anything, I am saying that most humans don’t either for most topics.

I have never used an AI to code and don’t care about being able to do it to the point that I have disabled the buttons that Microsoft crammed into VS Code.

That said, I do think a better use of AI might be to prepare PRs in logical and reasonable sizes for submission that have coherent contextualization and scope. That way when some dingbat vibe codes their way into a circle jerk that simultaneously crashes from dual memory access and doxxes the entire user base, finding issues is easier to spread out and easier to educate them on why vibe coding is boneheaded.

I developed for the VFX industry and I see the whole vibe coding thing as akin to storyboards or previs. Those are fast and (often) sloppy representations of the final production which can be used to quickly communicate a concept without massive investment. I see the similarities in this, a vibe code job is sloppy, sometimes incomprehensible, but the finished product could give someone who knew what the fuck they are doing a springboard to write it correctly. So do what the film industry does: keep your previs guys in the basement, feed them occasionally, and tell them to go home when the real work starts. (No shade to previs/SB artists, it is a real craft and vital for the film industry as a whole. I am being flippant about you for commedic effect. Love you guys.)