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Searching for Lost Knowledge in the Age of Intelligent Machines

“All but one,” Marchant wrote, “are from shipwrecks.” Time erases most everything and everyone, eventually. Any effort to understand the past is based entirely on incomplete records. And because it is impossible to standardize the language used to catalogue what’s left, or to fully index what is found, humans are unable to search through our own vast repositories of knowledge. To discover hidden gems in existing stores of human knowledge, Swanson wrote in his 1986 essay, we would need a massive thesaurus—one that describes “all relationships that people know about and then determine, for each search, which among those relationships” are actually relevant. “To build such a universal thesaurus entails no less than modeling all of human knowledge,” he wrote. It would be an impossible task—not least of all because, “to use such a thesaurus, one would have to retrieve relevant information from it, so a second universal thesaurus would be needed as a retrieval aid to Paid Skiptracing tools the first, and so on ad infinitum. The builder of a thesaurus is, in principle, lost in an infinite regress.” There’s some hope yet. Artificially intelligent systems are already creating and distilling robust models of human knowledge, but they’ll still be constrained by the datasets that feed into them. So there will be some degree of luck involved if, for instance, a machine happens upon an ancient document that reveals the whereabouts of more machines like the Antikythera Mechanism, or determines who built the one found on the Mediterranean seafloor so many decades ago. At the same time, the evolution of information systems makes remarkable discoveries seem more possible now than ever before.

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