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- W2783006506 abstract "Innovation is often predicated on discovering useful new combinations of existing knowledge in highly complex knowledge spaces. These needle-in-a-haystack type problems are pervasive in fields like genomics, drug discovery, materials science, and particle physics. We develop a combinatorial-based knowledge production function and embed it in the classic Jones growth model (1995) to explore how breakthroughs in artificial intelligence (AI) that dramatically improve prediction accuracy about which combinations have the highest potential could enhance discovery rates and consequently economic growth. This production function is a generalization (and reinterpretation) of the Romer/Jones knowledge production function. Separate parameters control the extent of individual-researcher knowledge access, the effects of fishing out/complexity, and the ease of forming research teams." @default.
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- W2783006506 date "2018-04-01" @default.
- W2783006506 modified "2023-10-10" @default.
- W2783006506 title "Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth" @default.
- W2783006506 doi "https://doi.org/10.3386/w24541" @default.
- W2783006506 hasPublicationYear "2018" @default.
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