建筑
计算机科学
计算机体系结构
数据科学
认知科学
计算生物学
生物
地理
心理学
考古
作者
J. Y. Tsao,R.G. Abbott,Douglas C. Crowder,Saaketh Desai,Rémi Dingreville,J. Elliott Fowler,Alexis Garland,Prasad P. Iyer,J. Murdock,Scott Steinmetz,A. K. Yarritu,C. Mark Johnson,David John Stracuzzi
标识
DOI:10.1016/j.yjoc.2024.100077
摘要
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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