Abstract
With the fast spread and reliance on digital technologies in industry and society, collaboration between humans and machines (artificial intelligence and machine learning) becomes an important subject; however, it is not clear what kind of value can be specifically created by the collaboration between humans and machines. Roadmapping is effective as a dialogue tool for clarifying the value among stakeholders. However, the traditional roadmapping methods are insufficient, since collaboration between humans and machines can be considered as a socio-technical system, and hence evolves, while influencing each other side. This paper proposes the new co-evolutionary technology roadmapping method, and reports the results carried out for the roadmapping workshop for machine learning applications.
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References
UK AI Council, AI Roadmap, January 2021. https://www.gov.uk/government/publications/ai-roadmap. Accessed 16 Feb 2021
Yaguo, L., et al.: Applications of machine learning to machine fault diagnosis: A review and roadmap. Mech. Syst. Signal Process. 138, 106587 (2020)
Artificial Intelligence Technology Strategy Council: R&D Goals and Roadmap for Industrialization of Artificial Intelligence (in Japanese), NEDO (2017). https://www.nedo.go.jp/content/100862412.pdf. Accessed 20 Feb 2021
The Japanese Society for Artificial Intelligence, AI Map (in Japanese). https://www.ai-gakkai.or.jp/resource/aimap/. Accessed 20 Feb 2021
https://www.jst.go.jp/mirai/jp/uploads/saitaku2018/JPMJMI18BA_yoshioka.pdf. Accessed 20 Feb 2021
Daim, T.U., Oliver, T., Phaal, R. (ed.): Technology Roadmapping. World Scientific (2018)
Phaal, R., Farrukh, C., Probert, D.: T-Plan: Fast Start Technology Roadmapping: Planning Your Route to Success, Institute for Manufacturing, University of Cambridge (2001)
Uchihira, N.: Future direction and roadmap of concurrent system technology. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 90(11), 2443–2448 (2007)
Sawaragi, T.: Design of resilient socio-technical systems by human-system co-creation. Artif. Life Robot. 25(2), 219–232 (2020). https://doi.org/10.1007/s10015-020-00598-3
NEDO: Survey on the Use of Artificial Intelligence and Machine Learning in Industrial Fields and the Safety of Artificial Intelligence Technologies (in Japanese) (2019). https://www.nedo.go.jp/library/seika/shosai_201907/20190000000685.html. Accessed 26 Feb 2021
IPA: AI White Paper 2019 (in Japanese) (2018). https://www.ipa.go.jp/ikc/info/20181030.html. Accessed 26 Feb 2021
Difficulty Map for Project Management of Machine Learning Application Systems. http://www.jaist.ac.jp/ks/labs/uchihira/mlas-pm-map.html. Accessed 27 Feb 2021
Okuda, S., et al.: Exploitation pattern for machine learning systems. In: The 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) (2021)
Uchihira, N.: Innovation design method for the internet of things: requirements and perspectives. In: 2019 Portland International Conference on Management of Engineering and Technology (PICMET), pp. 1–8 (2019)
Acknowledgments
This work was supported by the JST-Mirai Program (JPMJMI20B8). The authors would like to express their gratitude to Nobukazu Yoshioka, the leader of the project “Modelling and AI for Integration of Cyber and Physical World,” and other team members who participated in the preparatory stages and on the day of the workshop. We would also like to thank Dr. Robert Phaal of the University of Cambridge and Dr. Kunio Shirahada of Japan Advanced Institute of Science and Technology for their guidance and support as experts in technology roadmapping in preparation and on the day of the workshop.
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Uchihira, N. (2021). Dialogue Tool for Value Creation in Digital Transformation: Roadmapping for Machine Learning Applications. In: Leitner, C., Ganz, W., Satterfield, D., Bassano, C. (eds) Advances in the Human Side of Service Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 266. Springer, Cham. https://doi.org/10.1007/978-3-030-80840-2_60
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DOI: https://doi.org/10.1007/978-3-030-80840-2_60
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