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A Monitoring Framework for Progress in Artificial Intelligence Technology: A Research Based on Scientific and Technological Indicators

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  • Fethi Aslan

Abstract

In the past century, the key technologies that shaped societal and economic transformation were mechanical, electrical, and automation technologies. In the current century, there are strong trends indicating the prominence of artificial intelligence technology. Therefore, artificial intelligence technology has become more important for all countries. The success of countries in artificial intelligence technology is only possible with well-designed artificial intelligence policy tools. It is important to measure the level of technological advancement for the formulation of policies. However, efforts to measure the scientific and technological advancement of artificial intelligence technology are insufficient. Therefore, this study aims to develop a framework to measure the scientific and technological progress of AI technology. The developed framework includes the number of publications and citations, the number of high-impact scientific journals, the number of patent applications, the number of universities ranked in the top thousand in the field of computer science, the number of international high-impact conferences, and the total number of researchers in higher education. Through these criteria, the level of scientific and technological progress of the countries has been analysed in detail. The findings clearly revealed the leading position of the USA in this field. China followed the USA. These two countries are clearly and positively differentiated from the other countries. Other countries with good performance are the UK, the Netherlands and Germany.

Suggested Citation

  • Fethi Aslan, 2024. "A Monitoring Framework for Progress in Artificial Intelligence Technology: A Research Based on Scientific and Technological Indicators," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 74(2), pages 427-459, December.
    • Handle: RePEc:ist:journl:v:74:y:2024:i:2:p:427-459
    DOI: 10.26650/ISTJECON2024-1393965
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    1. Yuxin Zhu & Dazuo Tian & Feng Yan, 2020. "Effectiveness of Entropy Weight Method in Decision-Making," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-5, March.
    2. Le, Thanh & Pham, Hanh & Mai, Sau & Vu, Ngoc, 2022. "Frontier academic research, industrial R&D and technological progress: The case of OECD countries," Technovation, Elsevier, vol. 114(C).
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