Artificial Intelligence-Related Research Funding by the U.S. National Science Foundation and the National Natural Science Foundation of China

Abadi, Hamidreza Habibollahi Najaf; He, Zhou; Pecht, Michael

doi:10.1109/access.2020.3029231

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…The studied databases were consistent in finding the National Natural Science Foundation of China (NSFC) and the National Science Foundation (NSF) from the United States as the principal funding agencies (see Table 5). Both institutions have been compared accord-ing to their influence on the development of artificial intelligence-associated research in the past decade [44]. The main findings suggest that from 2010 to 2019, the NSF supported more AI research than the NSFC, injecting 1.7 billion more dollars into research.…”

Section: Most Participative Funding Agenciesmentioning

confidence: 99%

Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

et al. 2023

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Material science is a broad discipline focused on subjects such as metals, ceramics, polymers, electronics, and composite materials. Each of these fields covers areas associated with designing, synthesizing, and manufacturing, materials. These are tasks in which the use of technology may constitute paramount importance, reducing cost and time to develop new materials and substituting try-and-error standard procedures. This study aimed to analyze, quantify and map the scientific production of research on the fourth industrial revolution linked to material science studies in Scopus and Web of Science databases from 2017 to 2021. For this bibliometric analysis, the Biblioshiny software from RStudio was employed to categorize and evaluate the contribution of authors, countries, institutions, and journals. VOSviewer was used to visualize their collaboration networks. As a result, we found that artificial intelligence represents a hotspot technology used in material science, which has become usual in molecular simulations and manufacturing industries. Recent studies aim to provide possible avenues in the discovery and design of new high-entropy alloys as well as to detect and classify corrosion in the industrial sector. This bibliometric analysis releases an updated perspective on the implementations of technologies in material science as a possible guideline for future worldwide research.

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Section: Most Participative Funding Agenciesmentioning

confidence: 99%

Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

et al. 2023

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“…Artificial intelligence (AI) research has captured the attention of national research funders (Abadi et al, 2020), the private sector (Bughin et al, 2017), the news media (Chuan et al, 2019), social scientists (Frank et al, 2019), and public policy experts and decision-makers (Jobin et al, 2019). In this context, the AI research field has come to be of scholarly and national interest.…”

Section: Introductionmentioning

confidence: 99%

Investigating hybridity in artificial intelligence research

2023

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Research in the global field of artificial intelligence is increasingly hybrid in orientation. Researchers are beholden to the requirements of multiple intersecting spheres, such as scholarly, public, and commercial, each with their own language and logic. Relatedly, collaboration across disciplinary, sector and national borders is increasingly expected, or required. Using a dataset of 93,482 artificial intelligence publications, this article operationalises scholarly, public, and commercial spheres through citations, news mentions, and patent mentions, respectively. High performing publications (99th percentile) for each metric were separated into eight categories of influence. These comprised four blended categories of influence (news, patents and citations; news and patents; news and citations; patents and citations) and three single categories of influence (citations; news; patents), in addition to the ‘Other’ category of non-high performing publications. The article develops and applies two components of a new hybridity lens: evaluative hybridity and generative hybridity. Using multinomial logistic regression, selected aspects of knowledge production – research context, focus, artefacts, and collaborative configurations – were examined. The results elucidate key characteristics of knowledge production in the artificial intelligence field and demonstrate the utility of the proposed lens.

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“…The production of AI research has rapidly increased (Liu et al, 2018). The field has received significant private and public investment (Abadi et al, 2020;Bughin et al, 2017), and widespread news media coverage (Ouchchy et al, 2020). Concurrently, the field has become the subject of public policy debate (Jobin et al, 2019) and social science research (Frank et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Investigating the influence of AI research topics in the academic, public, and industry spheres

Berman

Williams

Michalska

2023

27th International Conference on Science, Technology and Innovation Indicators (STI 2023)

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The Artificial Intelligence research field sits at the intersection of several overlapping spheres (academia, industry, media), each with their own logics and commitments. The influence of research within these worlds is studied through a number of bibliometric methods, including citation metrics for measuring influence within academia, and counts of patents and news-media mentions for influence in industry and the media. Using a large-scale, publicly-available dataset of research outputs, we compare the topical content of outputs that are highly influential in each of these worlds. We identify significant differences between the content of influential research in these worlds, indicating that the academic, industry and media worlds value different aspects of the Artificial Intelligence field. These differences provide new insights on the evaluation of research produced within the Artificial Intelligence field.

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Artificial Intelligence-Related Research Funding by the U.S. National Science Foundation and the National Natural Science Foundation of China

Cited by 10 publications

References 12 publications

Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

Investigating hybridity in artificial intelligence research

Investigating the influence of AI research topics in the academic, public, and industry spheres

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