Human-Machine Hybrid Augmented Intelligence:Human-Machine Relationship, Collaboration and Mutual Enhancement

Abstract

The aim of human-machine hybrid augmented intelligence is to integrate human intelligence with the computational advantages of machines to effectively accomplish complex tasks through collaborative and complementary efforts. In designing systems for human-machine hybrid augmented intelligence, there are challenges in determining how humans and machines should collaborate and mutually enhance each other. To address these challenges, this paper builds upon existing research and consolidates the concept of human-machine hybrid augmented intelligence. It discusses two types of human-machine relationships: one where humans take the lead and machines provide support, and another where humans and machines have an equal and mutually beneficial coexistence. Drawing from the theory of division of labor, it proposes principles for task allocation between humans and machines. Considering the characteristics of cognitive processes and collaborative task types, it introduces a model for human-machine collaboration and mutual enhancement based on cognitive equivalence layers. The goal is to further enrich the theoretical framework of human-machine hybrid augmented intelligence and provide guidance for the design and implementation of such systems.

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