Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021

• URL: http://arxiv.org/abs/2306.09145v1
• Date: Thu, 15 Jun 2023 14:08:07 GMT
• Title: Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021
• Authors: Stefan Hajkowicz, Conrad Sanderson, Sarvnaz Karimi, Alexandra Bratanova, Claire Naughtin
• Abstract summary: In 1960 14% of 333 research fields were related to AI, but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.
• Score: 73.06361680847708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across various fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960-2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically: the AI "springs" and "winters". We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.

Related papers

• Oil & Water? Diffusion of AI Within and Across Scientific Fields [1.4576074392895075]
  We investigate claims of the increasing ubiquity of artificial intelligence within roughly 80 million research publications across 20 diverse scientific fields. We observe exponential growth, with AI-engaged publications increasing approximately thirteenfold (13x) across all fields. While this broadening engagement suggests a move toward greater disciplinary integration in every field, increased ubiquity is associated with a semantic tension between AI-engaged research and more traditional disciplinary research.
  arXiv  Detail & Related papers  (2024-05-24T00:39:32Z)
• AI for social science and social science of AI: A Survey [47.5235291525383]
  Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research.
  arXiv  Detail & Related papers  (2024-01-22T10:57:09Z)
• Artificial intelligence in social science: A study based on bibliometrics analysis [0.0]
  This article presents the results of a bibliometric analysis of AI-related publications in the social sciences over the last ten years (2013-2022). More than 19,408 articles have been published, 85% from 2008 to 2022, showing that research in this field is increasing significantly year on year. The United States is the country that publishes the most (20%), followed by China (13%)
  arXiv  Detail & Related papers  (2023-12-09T15:16:44Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• Human-Centered Responsible Artificial Intelligence: Current & Future Trends [76.94037394832931]
  In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. All of this work is aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends.
  arXiv  Detail & Related papers  (2023-02-16T08:59:42Z)
• Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence [74.2630823914258]
  The report examines eight domains of typical urban settings on which AI is likely to have impact over the coming years. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI. The charge for this report was given to the panel by the AI100 Standing Committee, chaired by Barbara Grosz of Harvard University.
  arXiv  Detail & Related papers  (2022-10-31T18:35:36Z)
• Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report [40.38252510399319]
  "Gathering Strength, Gathering Storms" is the second report in the "One Hundred Year Study on Artificial Intelligence" project. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research. The report concludes that AI has made a major leap from the lab to people's lives in recent years.
  arXiv  Detail & Related papers  (2022-10-27T21:00:36Z)
• Characterising Research Areas in the field of AI [68.8204255655161]
  We identified the main conceptual themes by performing clustering analysis on the co-occurrence network of topics. The results highlight the growing academic interest in research themes like deep learning, machine learning, and internet of things.
  arXiv  Detail & Related papers  (2022-05-26T16:30:30Z)
• A narrowing of AI research? [0.0]
  We study the evolution of the thematic diversity of AI research in academia and the private sector. We measure the influence of private companies in AI research through the citations they receive and their collaborations with other institutions.
  arXiv  Detail & Related papers  (2020-09-22T08:23:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.