Computational Intelligence From Autonomous System to Super-Smart Society and Beyond

Computational Intelligence From Autonomous System to Super-Smart Society and Beyond

Rodolfo A. Fiorini (Politecnico di Milano University, Milan, Italy)
DOI: 10.4018/IJSSCI.2020070101
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Abstract

In this article the author discusses main implications of current autonomous system (AS) through the symbiotic autonomous system (SAS) and the symbiotic system science (SSS) towards the incoming super-smart society, by bringing to light SSS fundamental considerations, according to experience and talks gained on scientific system development in the past fifty years, and formulating the proposal for a new understanding of them, at an effective scientific and operative level towards a real super-smart society. SSS is a growing scientific area which is taking a leadership role in fostering consensus on how best to bring about symbiotic relationships between current AS and incoming SAS in a mixed or hybrid society, called super-smart society. In order to achieve an antifragile behavior, next generation human-made system must have a new fundamental component able to address and to face effectively the problem of multiscale ontological uncertainty management, in an instinctively sustainable way: active, practical wisdom by design!
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Towards Real Computational Intelligence System

While we cannot predict the future, we can prepare for its potential outcome as best as we can. Knowledge and Education are our key facilitators (Fiorini, 2018; Ali, 2019; Kinsner, 2019). Therefore, we need to reframe uncertainty-as-problem in the past into the evolutive concept of uncertainty-as-resource for arbitrary multiscale system (Fiorini, 2019b; 2019c). The key change performance factors are knowledge and education, solving the major “information double-bind” (IDB) problem, just at the inner core of human knowledge extraction by experimentation in current science (Fiorini, 2015a). The new system must be based on an integrated approach; not only on the body of knowledge (BoK), but on the body of experience (BoX) too (Kinsner, 2019); not only on statistical algorithm, but on combinatorial one too, coupled together (Fiorini, 2016).

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