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

Gavriushenko, Mariia; Kaikova, Olena; Terziyan, Vagan

doi:10.1016/j.promfg.2020.02.092

Cited by 12 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [11], the specifics of systems where the processes are secured by collective (human + AI) intelligence has been studied. Because the cognitive processes work differently within human and within artificial minds, the cognitive vulnerabilities are different and appropriate attack scenarios could be hybrid to succeed with both components.…”

Section: Immune Project Puzzlesmentioning

confidence: 99%

“…It has been argued (on an abstract level) that, to be able to achieve high level of security, one has to train (using hybrid training methods) both (human and AI) components of security systems together as a collective intelligence. In the extended version [12] of the study from [11], the specific architectures have been suggested to train collective intelligence using adversarial learning. The architectures are able to generate sophisticated attacks (aka digital vaccines), which pushes collective intelligence to learn by adaptation.…”

Section: Immune Project Puzzlesmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Threats against Industry 4.0: Adversarial Training of Resilience

Kaikova

Terziyan²,

Tiihonen³

et al. 2022

E3S Web Conf.

Self Cite

View full text Add to dashboard Cite

Industry 4.0 and Smart Manufacturing are associated with the Cyber-Physical-Social Systems populated and controlled by the Collective Intelligence (human and artificial). They are an important component of Critical Infrastructure and they are essential for the functioning of a society and economy. Hybrid Threats nowadays target critical infrastructure and particularly vulnerabilities associated with both human and artificial intelligence. This article summarizes some latest studies of WARN: “Academic Response to Hybrid Threats” (the Erasmus+ project), which aim for the resilience (regarding hybrid threats) of various Industry 4.0 architectures and, especially, of the human and artificial decision-making within Industry 4.0 processes. This study discovered certain analogy between (cognitive) resilience of human and artificial intelligence against cognitive hacks (special adversarial hybrid activity) and suggested the approaches to train the resilience with the special adversarial training techniques. The study also provides the recommendations for higher education institutions on adding such training and related courses to their various programs. The specifics of related courses would be as follows: their learning objectives and related intended learning outcomes are not an update of personal knowledge, skills, beliefs or values (traditional outcomes) but the robustness and resilience of the already available ones.

show abstract

Section: Immune Project Puzzlesmentioning

confidence: 99%

Section: Immune Project Puzzlesmentioning

confidence: 99%

Hybrid Threats against Industry 4.0: Adversarial Training of Resilience

Kaikova

Terziyan²,

Tiihonen³

et al. 2022

E3S Web Conf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In previous research, we introduced briefly the concept of the collaborative intelligence and the University of the Future, as well as the concept of digital clone, which will benefit in creation of digital learning assistants [3]. The concept of 'digital twins or clones' was first introduced in 2003 [14].…”

Section: Collective Intelligence In Decisionmaking Process In Indusmentioning

confidence: 99%

“…To perform its mission in challenging and constantly changing environments, the CI cannot be hardcoded; it must be trained [3]. It would be naïve to assume that we could anticipate all the future challenges that the CI might potentially face and adapt the training process and learning content accordingly.…”

Section: Adversarial Training Of the Collective Intelligencementioning

confidence: 99%

Cloning and training collective intelligence with generative adversarial networks

Terziyan

Gavriushenko

Girka

et al. 2021

IET Collab Intel Manufact

Self Cite

View full text Add to dashboard Cite

Industry 4.0 and highly automated critical infrastructure can be seen as cyber-physicalsocial 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. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Dabei spielt auch die kollektive Intelligenz (engl. "collective intelligence") eine Rolle (Mavridis et al 2012;Gavriushenko et al 2020), unter Beteiligung natürlicher wie künstlicher Akteure und Aktanten (Latour 2007). Im Folgenden werden Beispiele für eine solche Kooperation und Kollaboration genannt, auf der Grundlage von eigenen Überlegungen, die wiederum auf den genannten Darstellungen und eigenen Beobachtungen beruhen.…”

Section: Kooperation Kollaboration Oder Generalismusunclassified

Der Einsatz von Servicerobotern bei Epidemien und Pandemien

Bendel

2020

HMD

View full text Add to dashboard Cite

Zusammenfassung Seit jeher werden Roboter eingesetzt, um gefährliche oder für uns nicht bewältigbare Aufgaben zu erledigen. Sie entschärfen Bomben, transportieren Gefahrenstoffe und arbeiten sich in für uns nicht erreichbare Gebiete vor. Die COVID-19-Pandemie hat gezeigt, dass auch Serviceroboter, die eigentlich nicht für Sonderfälle vorgesehen sind, hilfreiche Dienste bei der Versorgung von Isolierten und bei der Eindämmung von Krankheiten leisten können. Der vorliegende Beitrag stellt vier Typen von Servicerobotern vor. Dann gibt er Beispiele für die Roboternutzung während der Coronakrise im Jahre 2020. Schließlich wird der Frage nachgegangen, in welchem Umfang und in welcher Weise die Robotertypen zusammenarbeiten können und ob man manche von ihnen zu Generalisten weiterentwickeln kann. Zudem werden Geschäftsmodelle und Betriebsmöglichkeiten thematisiert. Der Beitrag zeigt, dass Kohorten von Robotern in Zukunft lebenswichtig sein könnten.

show abstract

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

Cited by 12 publications

References 10 publications

Hybrid Threats against Industry 4.0: Adversarial Training of Resilience

Hybrid Threats against Industry 4.0: Adversarial Training of Resilience

Cloning and training collective intelligence with generative adversarial networks

Der Einsatz von Servicerobotern bei Epidemien und Pandemien

Contact Info

Product

Resources

About