A4 Article in conference proceedings
Bridging human and machine learning for the needs of collective intelligence development (2020)


Gavriushenko, Mariia; Kaikova, Olena; Terziyan, Vagan (2020). Bridging human and machine learning for the needs of collective intelligence development. In Longo, Francesco; Qiao, Feng; Padovano, Antonio (Eds.) ISM 2019 : 1st International Conference on Industry 4.0 and Smart Manufacturing, Procedia Manufacturing, 42. Elsevier, 302-306. DOI: 10.1016/j.promfg.2020.02.092


JYU authors or editors


Publication details

All authors or editors: Gavriushenko, Mariia; Kaikova, Olena; Terziyan, Vagan

Parent publication: ISM 2019 : 1st International Conference on Industry 4.0 and Smart Manufacturing

Parent publication editors: Longo, Francesco; Qiao, Feng; Padovano, Antonio

Conference:

International Conference on Industry 4.0 and Smart Manufacturing

Place and date of conference: Rende, Cosenza, Italy, 20.-22.11.2019

Journal or series: Procedia Manufacturing

eISSN: 2351-9789

Publication year: 2020

Number in series: 42

Pages range: 302-306

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: http://doi.org/10.1016/j.promfg.2020.02.092

Open Access: Publication published in an open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/68533


Abstract

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.


Keywords: collective intelligence; artificial intelligence; machine learning; industry

Free keywords: collective intelligence; industry 4.0; deep learning; university for everything; artificial intelligence


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 1


Last updated on 2020-18-08 at 13:46