论文标题
学习工作未来的职业任务展示动态
Learning Occupational Task-Shares Dynamics for the Future of Work
论文作者
论文摘要
AI和自动化的最近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性技术失业的迅速变化。在本文中，我们将动态任务共享的新颖方法应用于大量的在线职位发布数据集，以探讨在过去十年中，在AI创新的过去十年中，尤其是在高，中和低工资职业中，职业任务需求的确切变化。值得注意的是，自2012年和2016年以来，大数据和AI分别在高工资职业中显着增加。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中的几个相关示例。这种任务要求跨职业的预测将在培训未来的劳动力方面发挥关键作用。
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.
