Comment by encyclopedism

Comment by encyclopedism a day ago

20 replies

The correct conclusion to draw and also to reiterate:

LLM's do not think, understand, reason, reflect, comprehend and they never shall.

I have commented elsewhere but this bears repeating

If you had enough paper and ink and the patience to go through it, you could take all the training data and manually step through and train the same model. Then once you have trained the model you could use even more pen and paper to step through the correct prompts to arrive at the answer. All of this would be a completely mechanical process. This really does bear thinking about. It's amazing the results that LLM's are able to acheive. But let's not kid ourselves and start throwing about terms like AGI or emergence just yet. It makes a mechanical process seem magical (as do computers in general).

I should add it also makes sense as to why it would, just look at the volume of human knowledge (the training data). It's the training data with the mass quite literally of mankind's knowledge, genius, logic, inferences, language and intellect that does the heavy lifting.

zahlman a day ago

> LLM's do not think, understand, reason, reflect, comprehend and they never shall. ... It's amazing the results that LLM's are able to acheive. ... it also makes sense as to why it would, just look at the volume of human knowledge

Not so much amazing as bewildering that certain results are possible in spite of a lack of thinking etc. I find it highly counterintuitive that simply referencing established knowledge would ever get the correct answer to novel problems, absent any understanding of that knowledge.

  • andsoitis a day ago

    > simply referencing established knowledge would ever get the correct answer to novel problems, absent any understanding of that knowledge.

    What is a concrete example of this?

    • lossolo 20 hours ago

      What problems have LLMs (so models like ChatGPT, Claude, Gemini, etc, not specific purpose algorithms like MCTS tuned by humans for certain tasks like AlphaGo or AlphaFold) solved that thousands of humans worked decades on and didn't solve (so as OP said, novel)? Can you name 1-3 of them?

      • edanm 4 hours ago

        Wait, you're redefining novel to mean something else.

        If I prove a new math theorem, it's novel - even though it's unlikely that thousands of humans have worked on that specific theorem for decades.

        LLMs have proven novel math theorems and solved novel math problems. There are more than three examples already.

senordevnyc a day ago

I’m curious what your mental model is for how human cognition works. Is it any less mechanical in your view?

  • encyclopedism a day ago

    That's a very difficult question to answer. It's an open problem in academia.

    To tease out something often it can be useful to approach problems from the opposite end. For example what is NOT the way in which human cognition works?

    We know how LLM's function, humans certainly do not function in a similar fashion. For one I can reason well enough that next year is 2026 without having most all human literary output fed to me. It's amazing how much the human mind does with so little information.

    • [removed] 16 hours ago
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  • andsoitis a day ago

    > I’m curious what your mental model is for how human cognition works. Is it any less mechanical in your view?

    human cognition is not constrained to pattern recognition and prediction of text and symbols.

    • greenpizza13 18 hours ago

      The thesis of "What is Intelligence" is based around intelligence being just that.

      > Intelligence is the ability to model, predict, and influence one’s future; it can evolve in relation to other intelligences to create a larger symbiotic intelligence.

      The book is worth a read. But I don't believe it limits the type of intelligence we have to humans, by definition. Then again, I'm only halfway through the book :).

      [https://mitpress.mit.edu/9780262049955/what-is-intelligence/]

      • andsoitis 2 hours ago

        > Intelligence is the ability to model, predict, and influence one’s future

        LLM's do pattern match and predict on textual symbols.

        Humans brains pattern match and predict beyond mere text.

        LLMs also do not learn in the moment, which I would argue is a sign of lack of intelligence.

      • dap 12 hours ago

        It seems obvious to me that "the ability to model, predict, and influence one’s future" is far more general and capable than "constrained to pattern recognition and prediction of text and symbols." How do you conclude that those are the same?

        I do like that definition because it seems to capture what's different between LLMs and people even when they come up with the same answers. If you give a person a high school physics question about projectile motion, they'll use a mental model that's a combination of explicit physical principles and algebraic equations. They might talk to themselves or use human language to work through it, but one can point to a clear underlying model (principles, laws, and formulas) that are agnostic to the human language they're using to work through them.

        I realize some people believe (and it could be) that ultimately it really is the same process. Either the LLM does have such a model encoded implicitly in all those numbers or human thought using those principles and formulas is the same kind of statistical walk that the LLM is doing. At the very least, that seems far from clear. This seems reflected in the results like the OP's.

  • ReplicantMaker a day ago

    Human cognition comes bundled with subjective experience.

    There is no mechanism known, even in principle, that explains the taste of strawberry.

    We have no justifiable reasons to believe that our cognition is in any way similar to a bunch of matrix multiplications.

  • 12_throw_away 20 hours ago

    Animal cognition is comprised of many intricate, quasi-redundant, deeply coupled systems that, among other things, can learn, form memories, interact with its environment, and grow. It is not remotely comparable to a computational neural network in any sense except that they both include "neural" in their jargon, albeit to mean vastly different things.