人工智能创新对美国职业的潜在影响
Ali Akbar Septiandri1, Marios Constantinides1, Daniele Quercia1,2
1Nokia Bell Labs, Cambridge CB3 0FA, United Kingdom.
PNAS nexus
|September 25, 2024
概括
人工智能 (AI) 影响的是特定的工作任务,而不是整个职业. 新的深度学习方法揭示了人工智能对例行和非例行任务都有影响,劳动力短缺可能会减轻工作替代.
科学领域:
- 计算机科学 计算机科学
- 劳动经济学 劳动经济学
- 知识产权知识产权知识产权
背景情况:
- 职业影响评估传统上使用手工方法或粗略匹配.
- 机器学习的进步为更精确的影响评估提供了机会.
- 了解人工智能对劳动力的影响对于未来的规划至关重要.
研究的目的:
- 开发和应用一种新的方法来量化人工智能 (AI) 对职业任务的影响.
- 利用深度学习和自然语言处理 (NLP) 来进行大规模分析AI专利及其与工作任务的关系.
- 识别特定的技能和任务容易受到人工智能的影响.
主要方法:
- 利用深度学习自然语言处理 (DLNLP) 来分析24,758项AI专利 (2015-2022年).
- 通过将专利与17,879个职业任务描述的数据集进行匹配,开发了AI影响度量.
- 根据专利内容和任务特征量化潜在的AI影响.
主要成果:
- 人工智能的影响超越了例行任务,扩展到非例行任务,如医疗诊断和飞行路径跟踪.
- 职业影响与任务所需的特定技能密切相关.
- 确定了面临人工智能影响和劳动力短缺的关键部门 (IT,医疗保健,运输).
结论:
- 人工智能影响特定的职业任务,需要超越职业层面分析的细微理解.
- 人工智能对劳动力的影响可能会通过增强而不是替代来缓解,特别是在现有短缺的行业.
- 未来的劳动力战略应该考虑人工智能的特定任务效应和部门劳动力动力.
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