Artificial Intelligence (AI) is being hailed as the latest example of a General Purpose Technology that could transform productivity and help tackle important societal challenges. This outcome is however not guaranteed: a myopic focus on short-term benefits could lock AI into technologies that turn out to be sub-optimal in the longer-run. Recent controversies about the dominance of deep learning methods and private labs in AI research suggest that the field may be getting narrower, but the evidence base is lacking. We seek to address this gap with an analysis of the thematic diversity of AI research in arXiv, a widely used pre-prints site. Having identified 110,000 AI papers in this corpus, we use hierarchical topic modelling to estimate the thematic composition of AI research, and this composition to calculate various metrics of research diversity. Our analysis suggests that diversity in AI research has stagnated in recent years, and that AI research involving private sector organisations tends to be less diverse than research in academia. This appears to be driven by a small number of prolific and narrowly-focused technology companies. Diversity in academia is bolstered by smaller institutions and research groups that may have less incentives to race and lower levels of collaboration with the private sector. We also find that private sector AI researchers tend to specialise in data and computationally intensive deep learning methods at the expense of research involving other (symbolic and statistical) AI methods, and of research that considers the societal and ethical implications of AI or applies it in domains like health. Our results suggest that there may be a rationale for policy action to prevent a premature narrowing of AI research that could reduce its societal benefits, but we note the incentive, information and scale hurdles standing in the way of such interventions.

中文翻译：

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缩小人工智能研究范围？

人工智能 (AI) 被誉为通用技术的最新例子，可以改变生产力并帮助应对重要的社会挑战。然而，这一结果并不能得到保证：对短期利益的短视可能会将 AI 锁定在从长远来看并非最佳的技术中。最近关于深度学习方法和私人实验室在人工智能研究中的主导地位的争议表明，该领域可能会变得越来越狭窄，但缺乏证据基础。我们试图通过分析 arXiv（一个广泛使用的预印本网站）中 AI 研究的主题多样性来弥补这一差距。在该语料库中确定了 110,000 篇 AI 论文，我们使用分层主题建模来估计 AI 研究的主题组成，和这个组合来计算研究多样性的各种指标。我们的分析表明，近年来人工智能研究的多样性停滞不前，涉及私营部门组织的人工智能研究往往不如学术界研究多样化。这似乎是由少数多产且专注的技术公司推动的。学术界的多样性得到了较小的机构和研究团体的支持，这些机构和研究团体可能对竞赛的动机较少，与私营部门的合作水平较低。我们还发现，私营部门 AI 研究人员倾向于专注于数据和计算密集型深度学习方法，而牺牲了涉及其他（符号和统计）AI 方法的研究，以及考虑人工智能的社会和伦理影响或将其应用于健康等领域的研究。我们的研究结果表明，采取政策行动可能有理由防止人工智能研究过早缩小范围，这可能会降低其社会效益，但我们注意到阻碍此类干预的动机、信息和规模障碍。