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Towards digital cognitive clones for the decision-makers: adversarial training experiments
- Source :
- Procedia Computer Science. 180:180-189
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- There can be many reasons for anyone to make a digital copy (clone) of own decision-making behavior. This enables virtual presence of a professional decision-maker simultaneously in many places and processes of Industry 4.0. Such clone can be used as one’s responsible representative when the human is not available. Pi-Mind (“Patented Intelligence”) is a technology, which enables “cloning” cognitive skills of humans using adversarial machine learning. In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. The physical component of the environment is provided by the logistic laboratory with camera-surveillance over the conveyors. The digital component of the environment contains special modifications of Generative Adversarial Networks, which include a human-operator as a trainer, an autonomous Pi-Mind clone as a trainee (a discriminator) and a smart digital adversary as a challenger (generator of sophisticated decision situations, emergencies and attacks, which supposedly catalyzes the cloning process).
- Subjects :
- cybersecurity
Computer science
Process (engineering)
päätöksentukijärjestelmät
neuroverkot
02 engineering and technology
tekoäly
Adversarial machine learning
Adversarial system
Human–computer interaction
Component (UML)
0202 electrical engineering, electronic engineering, information engineering
esineiden internet
artificial digital immunity
kyberturvallisuus
General Environmental Science
Generative Adversarial Networks
Cloning (programming)
ohjausjärjestelmät
020206 networking & telecommunications
Adversary
Industry 4.0
koneoppiminen
älytekniikka
General Earth and Planetary Sciences
020201 artificial intelligence & image processing
Clone (computing)
Subjects
Details
- ISSN :
- 18770509
- Volume :
- 180
- Database :
- OpenAIRE
- Journal :
- Procedia Computer Science
- Accession number :
- edsair.doi.dedup.....2be9519fd4574058dcd804af5d4f0cc1
- Full Text :
- https://doi.org/10.1016/j.procs.2021.01.155