On 12/26/2024 8:35 PM, PGC wrote:
In short, the reason LLM outputs feel like “type 1 reasoning on steroids” is that these models have memorized so many examples that their combined intuition extends across nearly all known textual domains. But when a problem truly demands formal reasoning steps absent from their training data, LLMs lack a real “type 2” counterpart—no robust self-critique, no internal program writing, and no persistent memory to refine their logic. We can therefore liken them to formidable intuition machines without the same embedded capacity for system 2, top-down reasoning or architectural self-modification that we see in real human skill acquisition.
Of course, like humans, an AI based on an LLM could have a formal reasoning program, like Prolog, and a math program, like Maxima, appended. But then the trick will be knowing when its type 1 reasoning isn't accurate enough and switching to type 2, or knowing that type 2 isn't fast enough and switching back to type 1.
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