Bridging human and machine learning for the needs of collective intelligence development
Gavriushenko, M., Kaikova, O., & Terziyan, V. (2020). Bridging human and machine learning for the needs of collective intelligence development. In F. Longo, F. Qiao, & A. Padovano (Eds.), ISM 2019 : 1st International Conference on Industry 4.0 and Smart Manufacturing (pp. 302-306). Elsevier. Procedia Manufacturing, 42. https://doi.org/10.1016/j.promfg.2020.02.092
Published inProcedia Manufacturing
© 2020 The Authors
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. ...
ConferenceInternational Conference on Industry 4.0 and Smart Manufacturing
Is part of publicationISM 2019 : 1st International Conference on Industry 4.0 and Smart Manufacturing
Publication in research information system
MetadataShow full item record
Additional information about fundingNo funding information.
Showing items with similar title or keywords.
Kumpulainen, Samu; Terziyan, Vagan (Elsevier, 2022)Artificial Intelligence (AI) is known to be a driving force behind the Industry 4.0. Nowadays the current hype on development and industrial adoption of the AI systems is mostly associated with the deep learning, i.e., ...
Voutilainen, Janne; Kari, Martti (Academic Conferences International, 2020)In 2019, e-criminals adopted new tactics to demand enormous ransoms from large organizations by using ransomware, a phenomenon known as “big game hunting.” Big game hunting is an excellent example of a sophisticated and ...
Wiafe, Isaac; Koranteng, Felix N.; Obeng, Emmanuel N.; Assyne, Nana; Wiafe, Abigail; Gulliver, Stephen R. (IEEE, 2020)Due to the ever-increasing complexities in cybercrimes, there is the need for cybersecurity methods to be more robust and intelligent. This will make defense mechanisms to be capable of making real-time decisions that can ...
Terziyan, Vagan; Vitko, Oleksandra (Elsevier, 2022)Artificial Intelligence is an important asset of Industry 4.0. Current discoveries within machine learning and particularly in deep learning enable qualitative change within the industrial processes, applications, systems ...
Terziyan, Vagan; Golovianko, Mariia; Gryshko, Svitlana (IOS Press, 2018)Artificial intelligence is an unavoidable asset of Industry 4.0. Artificial actors participate in real-time decision-making and problem solving in various industrial processes, including planning, production, and management. ...