Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth
Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth
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大海捞针：人工智能与重组生长
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Alexander Oettl
中科院分区：
文献类型：
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作者：
A. Agrawal;J. McHale;Alexander Oettl
创新往往取决于在高度复杂的知识空间中发现现有知识的有用的新组合。这些大海捞针式的问题在基因组学、药物发现、材料科学和粒子物理学等领域普遍存在。我们开发了一个基于组合的知识生产函数，并将其嵌入到经典的琼斯增长模型（1995）中，以探索人工智能（AI）的突破如何显着提高预测准确性，哪些组合具有最大的潜力，从而提高发现率，从而促进经济增长。这个生产函数是罗默/琼斯知识生产函数的推广（和重新解释）。单独的参数控制的程度，个人研究人员的知识获取，捕鱼/复杂性的影响，以及组建研究团队的难易程度。
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.