Human-Machine Duality: What’s Next in Cognitive Aspects of Artificial Intelligence?

Abstract

The goal of the paper is to find means for the unification of human-machine duality in collective behavior of people and machines, by conciliating approaches that proceed in opposite directions. The first approach proceeds top-down from non-formalizable, cognitive, uncaused, and chaotic human consciousness towards purposeful and sustainable human-machine interaction. The second approach proceeds bottom-up from intelligent machines towards high-end computing and is based on formalizable models leveraging multi-agent architectures. The resulting work reviews the extent, the merging points, and the potential of hybrid artificial intelligence frameworks that accept the idea of strong artificial intelligence. These models concern the pairing of connectionist and cognitive architectures, conscious and unconscious actions, symbolic and conceptual realizations, emergent and brain-based computing, and automata and subjects. The special authors’ convergent methodology is considered, which is based on the integration of inverse problem-solving on topological spaces, cognitive modelling, quantum field theory, category theory methods, and holonic approaches. It aims to a more purposeful and sustainable human-machine interaction in form of algorithms or requirements, rules of strategic conversations or network brainstorming, and cognitive semantics. The paper addresses the reduction of the impact of AI development on ethics violation. The findings delivered are used to provide perspectives on the shaping of societal, ethical, and normative aspects in the symbiosis between humans and machines. Implementations in real practice are represented.

Keywords

• Cognitive semantics
• category theory
• human-machine duality
• hybrid artificial intelligence
• holonic systems
• stability in dynamical systems