To push back on this, a somewhat recent Linus Torvalds ~quote:
"I don't think that 'just predicting the next word' is the insult that people think it is, it's mostly what we all do."
If we break our lives down into the different types of reasoning, and what we mostly do day-to-day, this rings very true to me.
I currently believe that our brains generally operate as very efficient inference machines. Sometimes we slow down to think things through, but for example, when in the ideal "flow state" it's some kind of distilled efficient inference. Isn't it? This is very hard for me to deny at this time.
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edit:
4o appears to agree with both of you, more than it does with me.
I may be wrong, but it seems to me this also is a case of improper use of words.
Those LLMs neither agree nor disagree. They do not understand. They produce output, and we read that output and we ourselves consider the output to be something, or something else.
All an LLM does is produce output. There's no conceptual understanding behind it, and so there is no agreement, or disagreement.
> All an LLM does is produce output. There's no conceptual understanding behind it, and so there is no agreement, or disagreement.
I think that I agree. However, even on HN, what percentage of human comments are simply some really basic inference, aka output/"reddit"/etc... and those are humans.
I am not trying to elevate LLMs to some form of higher intelligence, my only point is that most of the time, we are not all that much better. Even the 0.000001% best of us fall into these habits sometimes. [0]
I currently believe that modern LLM architecture will likely not lead to AGI/ASI. However, even without that, they could do a lot.
LLMs learn high-dimensional representations that capture conceptual relationships in their training data. They manipulate those representations in ways that approximate human reasoning.
System 1 is rapid, uses heuristics to make quick judgements. Not rigorous. System 1 is the default mode.
System 2 is slow deliberate reasoning, energy intensive, and even humans get that wrong.
LLMs often use something like System 1 pattern matching, get the answer wrong initially, then can be prodded into trying again with a System 2 approach (chain of thought).
LLM cannot explain their reasoning, and that is because there is no reasoning.