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greenflag

What is the difference between reasoning and predicting what reasoning would look like?

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The same as the difference between a thing and a statistical model of a thing.

They don't always match up.

Human reasoning also does not match up with reality. Isn't it statistical modeling all the way down?

That statement demands an explanation.

As far as I'm aware, when reasoning does not meet reality, it is called a delusion, and I'm fairly certain that majority of humans are not delusional.

I wish that were true, but in an election year, I'm constantly reminded that it isn't.

The majority of humans is not delusional all the time. But every human is delusional some times. Same as the majority of LLMs hallucinating some times but not all the time.

So if the bar for proper reasoning is never ever being delusional then humans and LLMs sit in the same bucket.

They are delusional in ways that don't overlap, though. An LLM will not be delusional in the way Van Gogh or Howard Hughes was, but humans (sane ones anyways) will not hallucinate the exact kind of odd, unpredictable LLM output that makes you go "huh? why the heck did it do this in this way or write this." Things which are extremely obvious well within the limits of even elementary human reasoning are completely outside the purview of model capability; it's much easier for a model to "reason" when it comes to systems and ideas that are very discrete (which is why it's so useful when writing code, esp. in higher-level languages -- little uncertainty there and a round-robin verification process for workflows like mathematics. However once you ask it to intuit anything that falls apart quickly; its main utility lies in being able to compensate for these deficiencies with sheer volume and burning through tokens)

Even on the frontier models these blips still happen fairly often and it's intrusive to a lot of workflows.

Calling it delusion is too severe. Our reasoning fails us all the time. For example, if you get a problem wrong on a math test, you’ve simply made a mistake, and with a little more “prompting” (even of your own volition) you could often easily arrive at the correct answer. Now if you stubbornly persist with the wrong answer even in the face of evidence to the contrary, then you could call it delusion. But my point is, there’s a spectrum of mistakenness.

In theory, theory and practice should be the same; in practice, they are not.

Except when they are, hence why theory exists at all!

Sometimes the map IS the territory, or at least superior to it.

And there’s no oracle to tell you when those “some times” are. That’s the point of the post.

One has a human behind it and one has a matrix latent space. Usually human understanding is more "real"

Stallman vehemently states llms are bullshit generators with no true understanding.

But sometimes you can approximate your problem and unfold an llm interlocator to producing the strings you need for your task

Well, the human is just a big bag of neurons. So in what sense is its "understanding" more real?

It is perhaps sad, but with our current knowledge of neuroscience, we can't really argue that humans are somehow special. All we can say is that in practice we are still more intelligent/understanding/meaningful/conscious/whatever than LLMs. But for how long?

Bro, we do not know human exactly work now.

The same difference between getting rained on, and prediction of getting rained on.

Ask a psychopath.

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