Internationalizing AI: Evolution and Impact of Distance Factors

• URL: http://arxiv.org/abs/2112.01231v1
• Date: Wed, 10 Nov 2021 00:44:53 GMT
• Title: Internationalizing AI: Evolution and Impact of Distance Factors
• Authors: Xuli Tang, Xin Li, Feicheng Ma
• Abstract summary: International collaboration in the field of AI is not prevalent (only 15.7%) The United States and China have promoted the international collaboration in the field of AI.
• Score: 6.045548929961739
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph (MAG) dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.

Related papers

• Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
  Next several decades may well be a turning point for humanity, comparable to the industrial revolution. Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts. We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
  arXiv  Detail & Related papers  (2024-04-06T22:18:31Z)
• 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)
• Evolving landscape of US-China science collaboration: Convergence and divergence [0.0]
  The US and China have significantly fortified their collaboration across diverse scientific disciplines. Recent reports hint at a potential decline in collaboration between these two giants. This study delves into the evolving landscape of interaction between the US and China over recent decades.
  arXiv  Detail & Related papers  (2023-09-10T14:11:46Z)
• Has China caught up to the US in AI research? An exploration of mimetic isomorphism as a model for late industrializers [9.03136346887569]
  We examine China's AI development process, demonstrating that it is characterized by rapid learning and differentiation. By 2018, the time lag between China and the USA in addressing AI research topics had evaporated. This finding suggests that China has effectively bridged a significant knowledge gap and could potentially be setting out on an independent research trajectory.
  arXiv  Detail & Related papers  (2023-07-11T19:59:54Z)
• 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 [73.06361680847708]
  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.
  arXiv  Detail & Related papers  (2023-06-15T14:08:07Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• China and the U.S. produce more impactful AI research when collaborating together [5.227075179857056]
  We analyze a dataset of over 350,000 AI scientists and 5,000,000 AI papers. We find that since the year 2000, China and the U.S. have been leading the field in terms of impact, novelty, productivity, and workforce.
  arXiv  Detail & Related papers  (2023-04-21T16:47:51Z)
• Quantifying the Benefit of Artificial Intelligence for Scientific Research [2.4700789675440524]
  We estimate both the direct use of AI and the potential benefit of AI in scientific research. We find that the use of AI in research is widespread throughout the sciences, growing especially rapidly since 2015. Our analysis reveals considerable potential for AI to benefit numerous scientific fields, yet a notable disconnect exists between AI education and its research applications.
  arXiv  Detail & Related papers  (2023-04-17T08:08:50Z)
• The Road to a Successful HRI: AI, Trust and ethicS-TRAITS [65.60507052509406]
  The aim of this workshop is to give researchers from academia and industry the possibility to discuss the inter-and multi-disciplinary nature of the relationships between people and robots.
  arXiv  Detail & Related papers  (2021-03-23T16:52:12Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Understanding Team Collaboration in Artificial Intelligence from the perspective of Geographic Distance [7.097316676566972]
  The United States produced the largest number of single-country and internationally collaborated AI publications. China has played an important role in international collaborations in AI after 2010.
  arXiv  Detail & Related papers  (2020-12-25T11:06:38Z)

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.