擒数网 (随信APP) | 研究衡量了人工智能在研究中的好处,但同时也指出了潜在的不平等。



在香港科技大学,学生们在课堂上使用虚拟现实头盔-版权AFP Peter PARKS

在约7500万份出版物中,使用人工智能的作品更有可能成为“热门论文”。从医学界设计新药物候选者到社会科学中制定新的税收政策,例子不一而足。然而,许多研究人员仍然缺乏对人工智能如何有益于科学研究的系统性理解,凸显出重大的人工智能使用与培训差距。有几位研究人员缺乏对人工智能如何有益于他们的研究的系统性理解,对于人工智能能否在每个领域推动科学发展仍存在怀疑。

在获得诺贝尔物理学奖的两位以创新人工智能研究而闻名的科学家和三位获得诺贝尔化学奖的科学家之后,使用先进技术,包括人工智能,来预测蛋白质的形状得到了认可。

总体上,人工智能在各个科学领域中被广泛使用,自2015年以来大幅增加。在这一整体发现中,西北大学的研究人员发现了人工智能利益方面的人口差异。

特别是,研究还强调了在科学研究中不断增长的人工智能使用可能带来的对女性和少数研究人员的不平等影响。

数据是从7460万篇出版物、710万项专利和420万个大学课程大纲中获取的,得出结论表明人工智能显示了“引文影响溢价”。

自2015年以来,在学术研究中对人工智能的使用不断增加,表现为在出版物的标题或摘要中提到人工智能相关术语(如“人工智能”,“深度学习”和“卷积神经网络”)的数量。

主要学科为计算机科学(37%)、工程学(24%)、物理学(24%)、生物学(22%)、心理学(24%)、经济学(14%)、社会学(30%)和政治学(27%)。由于新人工智能功能的发展,每个学科都显示出明显增加的直接人工智能使用得分。

无论学科如何,标题或摘要中提到人工智能相关术语的学科论文都会获得更多引文,更有可能成为热门论文,并收到来自其他学科的更高比例引文。

首席科学家Dashun Wang和Jian Gao开发了一个测量框架,通过将自然语言处理(NLP)技术应用于这些庞大的数据集,来估计科学研究中人工智能的直接使用和潜在收益。

这项研究题为“量化人工智能在科学研究中的使用和潜在好处”,发表在《自然人类行为》杂志上。

英文版:

Students at the Hong Kong University of Science and Technology use virtual reality headsets in class - Copyright AFP Peter PARKS

Among approximately 75 million publications analysed, those employing AI are more likely to be a ‘hit paper’. Examples range fromdesigning new drug candidates in medicine to drafting new taxation policies in social sciences. Yet, many researchers still lack a systematic understanding of how AI may benefit scientific research, highlighting a substantial AI use–AI training gap. Several researchers lack a systematic understanding of how AI may benefit their research, and scepticism remains about whether AI is capable of advancing science in every field.

This comes following two scientists known for their pioneering AI research earning the Nobel Prize in Physics and a trio of scientists gaining the Nobel Prize in Chemistry, which recognized the use of advanced technology, including AI, to predict the shape of proteins.

In general, the use of AI is widespread across the sciences, dramatically increasing since 2015. Within this overall finding, the Northwestern University researchers found demographic disparities regarding the benefits of AI.

In particular, the study also highlights the unequal effects on women and minority researchers that the steadfast rise of AI use in scientific research may bring.

Data was drawn from 74.6 million publications, 7.1 million patents and 4.2 million university course syllabi to derive at the conclusion that AI exhibit a “citation impact premium.”

There has been a growing use of AI in disciplinary research since 2015, proxied by the mention of AI-related terms (such as “artificial intelligence,” “deep learning” and “convolutional neural network”) in the title or abstract of publications.

The main disciplines are computer science (37%), engineering (24%), physics (24%), biology (22%), psychology (24%), economics (14%), sociology (30%) and political science (27%).Each has shown notably sharp increases in direct AI use scores due to the development of new AI capabilities.

Regardless of discipline, disciplinary papers that mention AI-related terms in their title or abstract receive more citations, being more likely to be a hit, and receive a higher fraction of citations from other disciplines.

Lead scientists Dashun Wang and Jian Gao developed a measurement framework to estimate the direct use and potential benefits of AI in scientific research by applying natural language processing (NLP) techniques to these vast datasets.

The study, “Quantifying the Use and Potential Benefits of Artificial Intelligence in Scientific Research,” appears in the journal Nature Human Behaviour.


Study measures benefits of AI in research, yet highlights potential disparities
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