Abstract
Artificial intelligence (AI) has been making extraordinary progress. To keep developing the AI, reciprocal feedback-communication between AI and people in many use cases are important. Hence, this study aims to effectively build an AI platform from data collection to data analysis as well as the eco-systems in the field. The platform includes multiple applications for data collection, a cloud database to store data, probabilistic latent semantic analysis and Bayesian network as the AI by which people understand why predictions and recommendations are provided as a white-box. The platform can be easily customized and comfortably deployed for each use case depending on user needs. In the test phase, as part of this study, the system has been deployed in several fields, such as museum events, vending machines, and local Child Guidance Centers that respond to child maltreatment. As part of future studies, the systems should continue to be tested and developed more openly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., Floridi, L.: Artificial intelligence and the ‘good society’: the US, EU, and UK approach. Sci. Eng. Ethics 24, 1–24 (2017)
National Science and Technology Council Networking and Information Technology. Networking and Information Technology Research and Development Subcommittee. The National Artificial Intelligence Research and Development Strategic Plan, Washington, DC, USA (2016)
Executive Office of the President National Science and Technology Council Committee on Technology: Preparing for the future of Artificial Intelligence, Washington, DC, USA (2016)
Motomura, Y.: Predictive modeling of everyday behavior from large-scale data. Synth. Engl. Ed. 2(1), 1–12 (2009)
Trifonova, N., Maxwell, D., Pinnegar, J., Kenny, A., Tucker, A.: Predicting ecosystem responses to changes in fisheries catch, temperature, and primary productivity with a dynamic Bayesian network model. ICES J. Mar. Sci. 74(5), 1334–1343 (2017)
Ide, A., Yamashita, K., Motomura, Y., Terano, T.: Analyzing regional characteristics of living activities of elderly people from large survey data with probabilistic latent spatial semantic structure modeling, Boston, MA (2018)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57 (1999)
Pearl, J.: Bayesian networks: a model of self-activated memory for evidential reasoning. In: Proceedings of the 7th Conference Cognitive Science Society, pp. 329–334 (1985)
Ishigaki, T., Takenaka, T., Motomura, Y.: Category mining by heterogeneous data fusion using PdLSI model in a retail service. In: Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 857–862 (2010)
Hirokawa, N., Murayama, K., Motomura, Y.: Probabilistic latent spatiotemporal semantic structure models based on travel history data. In: 29th Annual Conference Japanese Society for Artificial Intelligence, pp. 3–4 (2015)
Acknowledgments
This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Takaoka, K., Yamazaki, K., Sakurai, E., Yamashita, K., Motomura, Y. (2019). Development of an Integrated AI Platform and an Ecosystem for Daily Life, Business and Social Problems. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 787. Springer, Cham. https://doi.org/10.1007/978-3-319-94229-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-94229-2_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94228-5
Online ISBN: 978-3-319-94229-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)