质量(理念)
人工智能
不平等
计算机科学
心理学
机器学习
数学
认识论
数学分析
哲学
作者
Sukwoong Choi,Hyo Kang,Nam Il Kim,Junsik Kim
标识
DOI:10.48550/arxiv.2310.08704
摘要
We study how humans learn from AI, leveraging an introduction of an AI-powered Go program (APG) that unexpectedly outperformed the best professional player. We compare the move quality of professional players to APG's superior solutions around its public release. Our analysis of 749,190 moves demonstrates significant improvements in players' move quality, especially in the early stages of the game where uncertainty is highest. This improvement was accompanied by a higher alignment with AI's suggestions and a decreased number and magnitude of errors. Young players show greater improvement, suggesting potential inequality in learning from AI. Further, while players of all skill levels benefit, less skilled players gain higher marginal benefits. These findings have implications for managers seeking to adopt and utilize AI in their organizations.
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