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It's telling that we'd have to tack on a calculator module to get ChatGPT to be able to do arithmetic reliably. There are probably a lot more less well-defined tasks, no more complicated than arithmetic, that ChatGPT can't do on its own, but the arithmetic is just the most glaring to see when it gets wrong.
My certainty is more of a gut feeling informed by Hofstadter's Gödel, Escher, Bach and the connection between strange loops and cognition. You're of course right that if you can predict the next word in a sequence well enough, you can do any intellectual task, including human cognition. But "well enough" can be a stand-in for arbitrary amounts of computation, and the transformer models don't do the necessary work. In particular, they're not reasoning about their reasoning faculties, which I believe is a key component to any general intelligence. And more parameters isn't going to get us there. We're at least one more big theoretical breakthrough away from useful machines that reason.
This is comical when you remember that humans, too, fail to do arithmetic reliably unaided. Why don't you try to add two seven-digit numbers, without any 'intermediate steps'? Also, 'chain of thought prompts' enable models to do some quantitiave problems pretty well.
How precisely is a hunter-gatherer, or an average person 'reasoning about reasoning faculties' in their day-to-day life? Hunter-gatherers were "general intelligence". Why is that necessary to ... reliably add numbers?
also: let's assume that's true. why can't a neural network just encode it's "reasoning faculties" in a sufficiently large sequence of bits and then 'reason about its reasoning faculties' within those bits, the same way you claim humans do? (And if 'reasoning about reasoning' is a rationalist (in the philosophical sense not the rat sense) on the specialness and non-materiality of the human soul, as sometimes is ... does that really work)?
this isn't to say arithmetic proves the two are the same, but these objections seem weird.
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