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I genuinely haven’t yet heard an argument that wasn’t also made about cameras at some point. There were stories of premodern tribe people afraid film recordings would steal their souls when shown the tech for the first time.
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You could do worse than think of prompt engines as complex cameras or even just funhouse mirrors. One line of thought I’ve been noodling on is expanding the digital/analog spectrum into a triangular space: digital, analog, symbolic. AI is to symbols as sampling is to signals.
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GOFAI had a symbolic/subsymbolic formal divide… modern ML is entirely descended from subsymbolic side… eg vectorizing words into frequencies rather than modeling formal grammar
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More like a tax imposed by weaker computers. The unreasonable effectiveness of data had not kicked in. But the divide is real. Simple illustration is recent results of ML doing arithmetic. It is impressively right in an impressively wrong way.
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Can’t find the paper right now, but finding was how it got the right answer a lot through just statistically learning a lot if specific arithmetic problems but in no sense had an understanding to make 90% accuracy into 100% which is trivial in ordinary non-ai compute
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