人工智能能胜任这份工作吗?一个映射劳动和人工智能强度的双向模型

Fernando Martínez-Plumed, Songül Tolan, Annarosa Pesole, J. Hernández-Orallo, Enrique Fernández-Macías, Emilia Gómez
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引用次数: 13

摘要

在本文中,我们提出了一个设置,用于检查人工智能研究中研究强度的分布与当前和模拟场景中一系列工作任务(和职业)的相关性之间的关系。我们使用一组认知能力作为中间层,在劳动力和人工智能之间进行映射。这种设置有利于双向解释,以分析(1)当前或模拟的人工智能研究活动对劳动相关任务和职业产生或将产生的影响,以及(2)人工智能研究活动的哪些领域将对特定劳动任务和职业产生期望或不期望的影响。具体来说,在我们的分析中,我们将来自几个工人调查和数据库的59个通用劳动相关任务映射到认知科学文献中的14个认知能力,并将这些映射到用于评估人工智能技术进展的328个人工智能基准的综合列表中。我们提供该模型及其实现作为仿真工具。我们还通过一些示例说明了该设置的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Does AI Qualify for the Job?: A Bidirectional Model Mapping Labour and AI Intensities
In this paper we present a setting for examining the relation be-tween the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labourand AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples.
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