Evolutionary Path of Development of Artificial Intelligence (AI) and Patterns of Knowledge Convergence over the Second and Third AI Booms

Kumiko Miyazaki, Santiago Ruiz Navaz, Ryusuke Sato


Although AI was coined by John McCarthy 60 years ago, AI has been confined to the academic and scientific research domain. AI has been through several booms and we have currently reached the 3rd AI boom which followed the 2nd AI boom centering mainly on expert systems. The current AI boom started around 2013 and AI is beginning to affect corporate management and operations. AI has been evolving over six decades but it seems that the current boom is different from the previous booms. 

In this paper, we attempt to elucidate the evolutionary path of development of AI and the structural patterns of knowledge convergence in the current and previous booms.

For this purpose we have set 2 main objectives

1) To characterize the first (1B), second (2B) and the current, third (3B) AI boom

2) To analyze the structure of knowledge convergence around AI

RQ   How have the key technologies and the applications of AI changed over time, in the 2B and the 3B?

 An innovative method has been used to identify the characteristics of AI and the evolutionary path of knowledge convergence over the booms.

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Ávila-Robinson, A. & Miyazaki, K. (2014). Assessing nanotechnology potentials : interplay between the paths of knowledge evolution and the patterns of competence building. Int. J. Technol. Intell. Plan, 10(1): 1–28.

Ávila-Robinson, A. & Miyazaki, K. (2011). Conceptualization and Operationalization of Emerging Technologies : A Complementing Approach. in Technology Management in the Energy Smart World (PICMET), 2011 Proceedings of PICMET ’11, 2011, pp. 1681–1692.

Ávila-Robinson, A. & Miyazaki, K. (2013). Dynamics of scientific knowledge bases as proxies for discerning technological emergence - The case of MEMS/NEMS technologies. Technol. Forecast. Soc. Change, 80(6): 1071–1084, July.

Callon, M. (1991). Techno-economic Networks and Irreversibility. A Sociol. monsters Essays power, Technol. …, pp. 132–165.

Callon, M., Laredo, P., Rabeharisoa, V., Gonard, T. & Leray, T. (1992). The management and evaluation of technological programs and the dynamics of techno-economic networks: The case of the AFME. Res. Policy, 21(3): 215–236.

Chan, S. K. & Miyazaki, K. (2015). Knowledge convergence between cloud computing and big data and analysis of emerging technological opportunities in Malaysia. Portl. Int. Conf. Manag. Eng. Technol., 2015-Septe: 1501–1512.

Curran, C.S., Bröring, S. & Leker, J. (2010). Anticipating converging industries using publicly available data. Technol. Forecast. Soc. Change, 77(3): 385–395, March.

Curran, C.S. & Leker, J. (2011). Patent indicators for monitoring convergence - examples from NFF and ICT. Technol. Forecast. Soc. Change, 78(2): 256–273.

Cucerzan, S. (2007). Large-Scale Named Entity Disambiguation Based on Wikipedia Data. EMNLP-CoNLL 2007, no. June, pp. 708–716.

Fujimoto, M., Miyazaki, K. & von Tunzelmann, N. (2000). Technological fusion and telemedicine in Japanese companies. Technovation, 20 (4): 169–187, Apr.

Gaines, B. R. (1998). The learning curves underlying convergence. Technol. Forecast. Soc. Change, 57(1–2): 7–34, January.

Hacklin, F. (2008). Management of convergence in innovation: Strategies and capabilities for value creation beyond blurring industry boundaries, 1st ed. Heidelberg: Physica-Verlag.

Hacklin, F., Raurich, V. & Marxt, C. (2005). Implications of Technological Convergence on Innovation Trajectories: The Case of ICT Industry. Int. J. Innov. Technol. Manag., 02(03): 313–330, September.

Hacklin, F., Marxt, C. & Fahrni, F. (2009). Coevolutionary cycles of convergence: An extrapolation from the ICT industry. Technol. Forecast. Soc. Change, 76(6): 723–736, July.

Han, E. J. & Sohn, S. Y. (2016). Technological convergence in standards for information and communication technologies. Technol. Forecast. Soc. Change, 106: 1–10.

Hou, H., Kretschmer, H. & Liu, Z. (2008). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75(2): 89–202.

Islam, N. & Miyazaki, K. (2009). Nanotechnology innovation system: Understanding hidden dynamics of nanoscience fusion trajectories. Technol. Forecast. Soc. Change, 76(1): 128–140, Jan.

Japanese Patent Office, “特許出願技術動向調査報告書(概要),” 2016. [Online]. Available: https://www.jpo.go.jp/shiryou/pdf/gidou-houkoku/26_21.pdf. [Accessed: 11-Nov-2017].

Kumar, K. (2017). How Honda is using Cognitive Search to drive real changes in quality assurance. IBM ECM Blog, 2017. [Online]. Available: https://www.ibm.com/blogs/ecm/2017/08/30/honda-using-cognitive-search-drive-real-changes-quality-assurance/. [Accessed: 11-Nov-2018]

Lalitnorasate, P. & Miyazaki, K. (2016). Convergence in functional food: technological diversification and path-dependent learning. Int. J. Technol. Intell. Plan., 11(2):140.

Lee, W,S., Han, E. J. & Sohn, S.J. (2015). Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents. Technol. Forecast. Soc. Change, 100: 317–329.

Lee, K., Yun, J.J. & Jeong, E-S. (2015). Convergence innovation of the textile machinery industry in Korea. Asian J. Technol. Innov. 23(sup1): 58–73.

Leydesdroff, L. (1989). Words and co-words as indicators of intellectual organization. Res. Policy, 18(4): 209–223.

Matsuo, Y. (2016). Future of AI what is beyond Deep Learning. Technol. Econ., pp. 10–25.

McCarthy, J. (2007). Branches of AI. [Online]. Available: http://www-formal.stanford.edu/jmc/whatisai/node2.html. [Accessed: 11-Nov-2017]

Milne, D. & Witten, I. H. (2008). Learning to link with Wikipedia. Proceeding 17th ACM Conf. Inf. Knowl. Manag. (CIKM ’08), pp. 509–518.

Miyazaki, K. & Giraldo, E. (2018). Innovation strategy and technological competence building to provide next generation network and services through convergence – the case of NTT in Japan. Asian J. Technol. Innov. 23(sup.1): 74–92.

Niu, J., Tang, W., Xu, F., Zhou, X. & Song, Y. (2016). Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis. ISPRS Int. J. Geo-Information, 5(5): 66, 2016.

Ruiz-Navas, S. & Miyazaki, K. (2018). Developing a framework to track knowledge convergence in Big data. Int. J. Technol. Intell. Plan 12(2):121 – 151. DOI: 10.1504/IJTIP.2018.096101

Rosenberg, N. (1963). Technological Change in the Machine Tool Industry , 1840-1910. J. Econ. Hist. 23(4): 414–443

Velden, T., Boyack, K. W., Gläser, J., Koopman, R., Scharnhorst, A. & Wang, S. (2017). Comparison of topic extraction approaches and their results. Scientometrics, 111(2): 1169–1221.

Yoon, B. & Park, Y. (2004). A text-mining-based patent network: Analytical tool for high-technology trend. J. High Technol. Manag. Res., 15(1): 37–50, Feb.

Wikipedia, “Wikipedia,” wikipedia about, 2017. [Online]. Available: https://en.wikipedia.org/wiki/Wikipedia:About. [Accessed: 07-Apr-2017].

DOI: http://dx.doi.org/10.14203/STIPM.2019.172


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