Abstract
This study created the “Challenges from the Industry X Solutions from Talents” mechanism, which emphasizes learning from doing instead of traditional talent cultivation modes by aligning artificial intelligence (AI) talents with critical problems of enterprises. The problem-solving process begins with industry AI demands. This paper design a platform, AIGO, aiming to cultivate AI talents, and enabling them to solve real-world industrial problems. The platform is composed of competition, learning and community.
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References
Gagne, J.F., Kiser, G., Mantha, Y.: Global AI talent report (2019). https://jfgagne.ai/talent-2019/. Accessed 26 June 2019
Ng, A.: AI Transformation Playbook (2018). https://landing.ai/ai-transformation-playbook/. Accessed 26 June 2019
Tsai, I.C., Liao, C.H., Wu, H.J., Yeh, C.H.: An innovative hybrid model for developing cross domain ICT talent in digital economy. In: IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 745–750 (2018)
Larson, J.S., Farnsworth, K., Folkestad, L.S., Tirkolaei, H.K., Glazewski, K., Savenye, W.: Using problem-based learning to enable application of foundation engineering knowledge in a real-world problem. In: IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 500–506 (2018)
Mustaffa, N., Said, M.N.H.M., Ismail, Z., Tasir, Z.: Framework of integrating algebraic thinking in problem-based learning via online environment for school students. In: IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 372–378 (2018)
Dewey, J.: How We Think. Health & Co., Boston (1910)
Gorghiu, G., Drăghicescu, L., Cristeda, S., Petrescu, A.-M., Gorghiu, L.: Problem-based learning - an efficient learning strategy in the science lessons contex. Procedia Soc. Behav. Sci. 191, 1865–1870 (2015)
Akrawi, N.K.: Bridging the gap between learning and teaching by using knowledge-based systems. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, vol. 72 (2011)
Acknowledgment
This work is supported through the AI Talent Training Program Project of the Institute for Information Industry, subsidized by the Industrial Development Bureau, Ministry of Economic Affairs of the Republic of China.
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Tseng, HC., Chiang, TH., Chung, HJ., Yeh, CH., Tsai, IC. (2019). A Case Study of Taiwan - AI Talent Cultivation Strategies. In: Rønningsbakk, L., Wu, TT., Sandnes, F., Huang, YM. (eds) Innovative Technologies and Learning. ICITL 2019. Lecture Notes in Computer Science(), vol 11937. Springer, Cham. https://doi.org/10.1007/978-3-030-35343-8_42
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DOI: https://doi.org/10.1007/978-3-030-35343-8_42
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