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Notes -
We have a good idea of how to train AI to solve mathematical problems, of virtually unbounded complexity. In the course of this, AI clearly learns "techniques" as shown here, if not "theories". I don't think King's prowess is theory-driven either, but in any case we don't have a good idea of how to train AI to be a good prose writer. We have some ideas, but are unlikely to act on them. There's not much money to be made in it, and plenty of highly motivated enmity – AI is already widely hated. and yes, autoregressive generation for the prompt "write like King" is not like King actually writing a novel. We have such tricks though.
My point is, it's not a general principle that AI will only rehash human techniques in some uninspired "probabilistic" way. If there is a hill to climb, such that "good" and "bad" outputs with regard to the problem statement can be distinguished, AI can bumble its way up the hill and also find new tricks. We've seen this before LLMs, with AlphaGo and move 37, we're starting to see it with LLMs.
Human mind runs entirely on probabilistic mush. Neural networks were invented as approximation of our own approximate learning. But probabilistic decision processes can have clear enough decision boundaries that they become able to operate with "abstractions", "symbols" or "theories". They also remain able to fail. For example, you are failing to update on evidence, because you haven't been trained to take input like "Terry Tao is surprised" seriously and think it's infinitely less interesting than your preconceived notions, basically some dweeb noise. Unlike an LLM, you can update at lifetime, so maybe you'll reread the above post and see how it contradicts your position.
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