摘要翻译：
人工智能(AI)最近的大部分成功都是由更广泛的机器学习方法家族中令人印象深刻的成就推动的，这些方法通常被称为深度学习(DL)。本文提供了关于DL在科学中的扩散和影响的见解。通过对arxiv.org出版物语料库的自然语言处理(NLP)方法，我们描述了新兴的DL技术，并识别了相关的搜索词列表。这些搜索词允许我们从所有科学的Web of Science中检索与DL相关的出版物。基于该样本，我们记录了DL在科学系统中的扩散过程。我们发现i）在世界范围内，将DL作为一种研究工具的采用呈指数增长；ii）DL应用领域的区域差异；iii）从跨学科DL应用到应用领域内的学科研究的过渡。第二步，我们研究DL方法的采用如何影响科学发展。因此，我们实证评估DL的采用如何与健康科学中的重组新颖性和科学影响相关联。我们发现DL的采用与重组新颖性负相关，而与期望和引文绩效方差正相关。我们的发现表明DL还没有（还没有？）作为一个自动驾驶仪，在复杂的知识景观中导航，并推翻它们的结构。然而，“DL原理”符合其作为一般科学方法的核心的多功能性，这种方法以可测量的方式推进科学。
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英文标题：
《Deep Learning in Science》
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作者：
Stefano Bianchini, Moritz M\"uller and Pierre Pelletier
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最新提交年份：
2020
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computers and Society        计算机与社会
分类描述：Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Much of the recent success of Artificial Intelligence (AI) has been spurred on by impressive achievements within a broader family of machine learning methods, commonly referred to as Deep Learning (DL). This paper provides insights on the diffusion and impact of DL in science. Through a Natural Language Processing (NLP) approach on the arXiv.org publication corpus, we delineate the emerging DL technology and identify a list of relevant search terms. These search terms allow us to retrieve DL-related publications from Web of Science across all sciences. Based on that sample, we document the DL diffusion process in the scientific system. We find i) an exponential growth in the adoption of DL as a research tool across all sciences and all over the world, ii) regional differentiation in DL application domains, and iii) a transition from interdisciplinary DL applications to disciplinary research within application domains. In a second step, we investigate how the adoption of DL methods affects scientific development. Therefore, we empirically assess how DL adoption relates to re-combinatorial novelty and scientific impact in the health sciences. We find that DL adoption is negatively correlated with re-combinatorial novelty, but positively correlated with expectation as well as variance of citation performance. Our findings suggest that DL does not (yet?) work as an autopilot to navigate complex knowledge landscapes and overthrow their structure. However, the 'DL principle' qualifies for its versatility as the nucleus of a general scientific method that advances science in a measurable way.
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PDF链接：
https://arxiv.org/pdf/2009.01575