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StevenVincentOne t1_je7uj5q wrote

Reply to comment by Prestigious-Ad-761 in The argument that a computer can't really "understand" things is stupid and completely irrelevant. by hey__bert

They are confusing how an LLM is engineered and trained with how it actually operates and performs. We know how they are engineered and trained. The actual operation and performance is a black box. It's emergent behavior. Even people like Stephan Wolfram are making this basic mistake.

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Prestigious-Ad-761 t1_je88gnt wrote

Right? Emergent behaviours, that's how I see it. But I'm not very knowledgeable about AI engineering, so we're probably wrong, right?

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StevenVincentOne t1_je8icbo wrote

No, we are not. It's a definite "forest for the trees" perceptual issue. Many of the people so far inside the forest of AI cannot see beyond the engineering into the results of their own engineering work. AI are not machines. They are complex, and to some degree self-organizing, systems of dynamic emergent behaviors. Mechanistic interpretations are not going to cut it.

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Andriyo t1_je8qj9c wrote

I wouldn't call it a blackbox how it operates - it's just tensor operations some linear algebra, nothing magic.

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Franimall t1_je905k3 wrote

We know how neurons work, but that doesn't mean we understand consciousness. It's the immense complexity and scale of the structure that makes up the black box, not the mechanism.

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Prestigious-Ad-761 t1_jeb639j wrote

Did I say anywhere a blackbox was magic? I'm referring to the fact that with our current understanding, we can only with great difficulty infer why a neural network works well within a given task with the "shape" that it acquired from its training. And inferring it for each task/subtask/microsubtask it now has the capacity to achieve seems completely impossible, from what I understand.

But truly I'm an amateur, so I may well be talking out of my arse. Let me know if I am.

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Andriyo t1_jedfs83 wrote

I'm not a specialist myself either but I gather what's difficult to understand the LLMs for humans is due to the fact that models are large, with many dimensions (features) and inference is probabilistic in some aspects (that's how they implement creativity). All that combined makes it hard to understand what's going on. But that's true for any large software system. It's not unique to LLMs.
I use word "understand" here in the meaning that one is capable to predict how software system would behave for a given input.

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