Co-evolutionary hybrid intelligence

TL;DR

This paper proposes a co-evolutionary hybrid intelligence approach that combines human and machine intelligence, addressing limitations of data-centric AI such as data scarcity, high resource consumption, and lack of explainability.

Contribution

It introduces a novel co-evolutionary framework for hybrid intelligence systems, emphasizing human-machine collaboration over traditional data-driven methods.

Findings

Highlights limitations of current data-centric AI

Proposes a hybrid human-machine co-evolution model

Suggests potential for more explainable and resource-efficient AI

Abstract

Artificial intelligence is one of the drivers of modern technological development. The current approach to the development of intelligent systems is data-centric. It has several limitations: it is fundamentally impossible to collect data for modeling complex objects and processes; training neural networks requires huge computational and energy resources; solutions are not explainable. The article discusses an alternative approach to the development of artificial intelligence systems based on human-machine hybridization and their co-evolution.