Bridging human and machine learning for the needs of collective intelligence development

被引:9
|
作者
Gavriushenko, Mariia [1 ]
Kaikova, Olena [1 ]
Terziyan, Vagan [1 ]
机构
[1] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland
关键词
collective intelligence; Industry; 4.0; deep learning; university for everything; artificial intelligence;
D O I
10.1016/j.promfg.2020.02.092
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
There are no doubts that artificial and human intelligence enhance and complement each other. They are stronger together as a team of Collective (Collaborative) Intelligence. Both require training for personal development and high performance. However, the approaches to training (human vs. machine learning) are traditionally very different. If one needs efficient hybrid collective intelligence team, e.g. for managing processes within the Industry 4.0, then all the team members have to learn together. In this paper we point out the need for bridging the gap between the human and machine learning, so that some approaches used in machine learning will be useful for humans and vice-versa, some knowledge from human pedagogy can be useful also for training the artificial intelligence. When this happens, we all will come closer to the ultimate goal of creating a University for Everything capable of educating human and digital "workers" for the Industry 4.0. The paper also considers several thoughts on training digital assistants of the humans together in a team. (C) 2020 The Authors. Published by Elsevier B.V.
引用
收藏
页码:302 / 306
页数:5
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