Cloning and training collective intelligence with generative adversarial networks. Issue 1 (23rd February 2021)
- Record Type:
- Journal Article
- Title:
- Cloning and training collective intelligence with generative adversarial networks. Issue 1 (23rd February 2021)
- Main Title:
- Cloning and training collective intelligence with generative adversarial networks
- Authors:
- Terziyan, Vagan
Gavriushenko, Mariia
Girka, Anastasiia
Gontarenko, Andrii
Kaikova, Olena - Other Names:
- Digesi Slavatore guestEditor.
- Abstract:
- Abstract: Industry 4.0 and highly automated critical infrastructure can be seen as cyber‐physical‐social systems controlled by the Collective Intelligence. Such systems are essential for the functioning of the society and economy. On one hand, they have flexible infrastructure of heterogeneous systems and assets. On the other hand, they are social systems, which include collaborating humans and artificial decision makers. Such (human plus machine) resources must be pre‐trained to perform their mission with high efficiency. Both human and machine learning approaches must be bridged to enable such training. The importance of these systems requires the anticipation of the potential and previously unknown worst‐case scenarios during training. In this paper, we provide an adversarial training framework for the collective intelligence. We show how cognitive capabilities can be copied ("cloned") from humans and trained as a (responsible) collective intelligence. We made some modifications to the Generative Adversarial Networks architectures and adapted them for the cloning and training tasks. We modified the Discriminator component to a so‐called "Turing Discriminator", which includes one or several human and artificial discriminators working together. We also discussed the concept of cellular intelligence, where a person can act and collaborate in a group together with their own cognitive clones.
- Is Part Of:
- IET collaborative intelligent manufacturing. Volume 3:Issue 1(2021)
- Journal:
- IET collaborative intelligent manufacturing
- Issue:
- Volume 3:Issue 1(2021)
- Issue Display:
- Volume 3, Issue 1 (2021)
- Year:
- 2021
- Volume:
- 3
- Issue:
- 1
- Issue Sort Value:
- 2021-0003-0001-0000
- Page Start:
- 64
- Page End:
- 74
- Publication Date:
- 2021-02-23
- Subjects:
- Production management -- Periodicals
Production engineering -- Periodicals
Production management
Production engineering
Electronic journals
Periodicals
658.5 - Journal URLs:
- https://digital-library.theiet.org/content/journals/iet-cim ↗
https://ietresearch.onlinelibrary.wiley.com/journal/25168398 ↗
https://digital-library.theiet.org/content/journals/iet-cim/ ↗
https://ieeexplore.ieee.org/servlet/opac?punumber=8425306 ↗
http://ieeexplore.ieee.org/Xplore/home.jsp ↗ - DOI:
- 10.1049/cim2.12008 ↗
- Languages:
- English
- ISSNs:
- 2516-8398
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23691.xml