学习职业任务-为未来工作分享动力
Learning Occupational Task-Shares Dynamics for the Future of Work
摘要：
最近的人工智能和自动化浪潮被认为与以前的通用技术（GPT）不同，因为它可能导致职业的基本任务要求和持续的技术失业的快速变化。在本文中，我们将一种新的动态任务共享方法应用于一个大型的在线职位发布数据集，以探索在过去十年的人工智能创新中，职业任务需求究竟发生了怎样的变化，特别是在高、中、低工资职业中。值得注意的是，自2012年和2016年以来，大数据和人工智能分别在高薪职业中大幅上升。我们建立了一个ARIMA模型来预测未来的职业任务需求，并展示了医疗保健，管理和IT领域的几个相关案例。这种跨职业的任务需求预测将在未来的劳动力再培训中发挥关键作用。
英文摘要：
The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
