论文标题
人工智能研究中AI的解放：深度学习和计算鸿沟
The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research
论文作者
论文摘要
现代人工智能（AI）研究越来越多地在计算中变得越来越密集。但是，越来越多的问题是，由于无法获得计算能力，只有某些公司和精英大学在现代AI研究中具有优势。我们使用了来自57个著名计算机科学会议的171394篇论文的新型数据集，我们记录说，尤其是大型技术公司和精英大学的公司增加了参加大型AI会议的参与，因为深度学习在2012年的意外崛起。该效应集中在精英大学中，这些大学在QS世界大学排名中排名1-50。此外，我们发现了两种策略，通过这些策略，企业在AI研究中增加了其存在：首先，他们增加了仅公司的出版物；其次，公司主要与精英大学合作。因此，在人工智能研究中，公司和精英大学的存在增加了中期（QS排名201-300）和下层大学（QS排名301-500）。为了提供因果证据表明，深度学习的意外崛起导致了这种差异，我们利用了广义的合成控制方法，这是一种数据驱动的反事实估计器。使用基于机器学习的文本分析方法，我们提供了其他证据，表明这两组之间的差异（大型公司和非精英大学）是由对计算能力或计算的访问驱动的，我们将其称为“计算鸿沟”。大型公司和非精英大学之间的这种计算鸿沟增加了人们对AI技术中偏见和公平性的关注，并给人以“民主化” AI的障碍。这些结果表明，缺乏访问专业设备（例如计算）可以使知识生产取消民主化。
Increasingly, modern Artificial Intelligence (AI) research has become more computationally intensive. However, a growing concern is that due to unequal access to computing power, only certain firms and elite universities have advantages in modern AI research. Using a novel dataset of 171394 papers from 57 prestigious computer science conferences, we document that firms, in particular, large technology firms and elite universities have increased participation in major AI conferences since deep learning's unanticipated rise in 2012. The effect is concentrated among elite universities, which are ranked 1-50 in the QS World University Rankings. Further, we find two strategies through which firms increased their presence in AI research: first, they have increased firm-only publications; and second, firms are collaborating primarily with elite universities. Consequently, this increased presence of firms and elite universities in AI research has crowded out mid-tier (QS ranked 201-300) and lower-tier (QS ranked 301-500) universities. To provide causal evidence that deep learning's unanticipated rise resulted in this divergence, we leverage the generalized synthetic control method, a data-driven counterfactual estimator. Using machine learning based text analysis methods, we provide additional evidence that the divergence between these two groups - large firms and non-elite universities - is driven by access to computing power or compute, which we term as the "compute divide". This compute divide between large firms and non-elite universities increases concerns around bias and fairness within AI technology, and presents an obstacle towards "democratizing" AI. These results suggest that a lack of access to specialized equipment such as compute can de-democratize knowledge production.
