AI for Technoscientific Discovery: A Human-Inspired Architecture

Journal of Creativity Pub Date : 2024-02-08 DOI:10.1016/j.yjoc.2024.100077

J.Y. Tsao , R.G. Abbott , D.C. Crowder , S. Desai , R.P.M. Dingreville , J.E. Fowler , A. Garland , P.P. Iyer , J. Murdock , S.T. Steinmetz , K.A. Yarritu , C.M. Johnson , D.J. Stracuzzi

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引用次数: 0

Abstract

We present a high-level architecture for how artificial intelligences might advance and accumulate scientific and technological knowledge, inspired by emerging perspectives on how human intelligences advance and accumulate such knowledge. Agents advance knowledge by exercising a technoscientific method—an interacting combination of scientific and engineering methods. The technoscientific method maximizes a quantity we call “useful learning” via more-creative implausible utility (including the “aha!” moments of discovery), as well as via less-creative plausible utility. Society accumulates the knowledge advanced by agents so that other agents can incorporate and build on to make further advances. The proposed architecture is challenging but potentially complete: its execution might in principle enable artificial intelligences to advance and accumulate an equivalent of the full range of human scientific and technological knowledge.

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人工智能促进技术科学发现：人类启发式架构

受人类智能体如何推进和积累科技知识这一新兴观点的启发，我们提出了人工智能体如何推进和积累科技知识的高级架构。智能体通过运用技术科学方法--一种科学与工程方法的互动组合--来增进知识。技术科学方法通过更具创造性的似是而非的效用（包括 "啊哈！"的发现时刻），以及更具创造性的似是而非的效用，最大限度地提高我们称之为 "有用的学习 "的数量。社会会积累代理所获得的知识，以便其他代理能够吸收并在此基础上取得进一步的进步。所提议的架构极具挑战性，但有可能是完整的：它的执行原则上可使人工智能推进并积累相当于人类全部科技知识的知识。

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来源期刊

Journal of Creativity

CiteScore

2.10

自引率

0.00%

发文量