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
来源
INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING (ISM 2019) | 2020年 / 42卷
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
相关论文
共 50 条
  • [41] Bridging expertise with machine learning and automated machine learning in clinical medicine
    Lee, Chien-Chang
    Park, James Yeongjun
    Hsu, Wan-Ting
    ANNALS ACADEMY OF MEDICINE SINGAPORE, 2024, 53 (03) : 129 - 131
  • [42] Quantifying collective intelligence in human groups
    Riedl, Christop
    Kim, Young Ji
    Gupta, Pranav
    Malone, Thomas W.
    Woolley, Anita Williams
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2021, 118 (21)
  • [43] Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics
    Koromina, Maria
    Pandi, Maria-Theodora
    Patrinos, George P.
    OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY, 2019, 23 (11) : 539 - 548
  • [44] The Role of Artificial Intelligence and Machine Learning in Accelerating the Discovery and Development of Nanomedicine
    Agrahari, Vivek
    Choonara, Yahya E.
    Mosharraf, Mitra
    Patel, Sravan Kumar
    Zhang, Fan
    PHARMACEUTICAL RESEARCH, 2024, 41 (12) : 2289 - 2297
  • [45] Artificial intelligence and machine learning
    Kuehl, Niklas
    Schemmer, Max
    Goutier, Marc
    Satzger, Gerhard
    ELECTRONIC MARKETS, 2022, 32 (04) : 2235 - 2244
  • [46] Application of artificial intelligence and machine learning methods in drug discovery and development
    Naranjo-Castaneda, Carlos
    Coello-Coello, Carlos A.
    Juaristi, Eusebio
    ARKIVOC, 2024,
  • [47] Learning Topology: Bridging Computational Topology and Machine Learning
    Moroni, Davide
    Pascali, Maria Antonietta
    PATTERN RECOGNITION AND IMAGE ANALYSIS, 2021, 31 (03) : 443 - 453
  • [48] Artificial intelligence and machine learning
    Hahn, Peter
    HANDCHIRURGIE MIKROCHIRURGIE PLASTISCHE CHIRURGIE, 2019, 51 (01) : 62 - 67
  • [49] Learning Topology: Bridging Computational Topology and Machine Learning
    Davide Moroni
    Maria Antonietta Pascali
    Pattern Recognition and Image Analysis, 2021, 31 : 443 - 453
  • [50] Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning
    Wang, Danshi
    Zhang, Min
    FRONTIERS IN COMMUNICATIONS AND NETWORKS, 2021, 2