Cloning and training collective intelligence with generative adversarial networks

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.