The article describes the principles of building intelligent human assistants based on the concept of co-evolutionary hybrid intelligence. The idea of the concept is the mutual development of human and artificial intelligences as a single indivisible organism. From this concept follows the necessity of continuous mutual construction and improvement of models of the state and behavior of humans and artificial intelligence. The latter is possible under the condition of personalizing a system of co-evolutionary hybrid intelligence. The principles of building such systems are illustrated by the example of intelligent doctor-patient assistants.

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

This paper questions the feasibility of a strong data-centric artificial intelligence and proposes an alternative concept of co-evolutionary hybrid intelligence. The article provides a critique of data-centric intelligent systems and presents the advantages of incorporating humans into the loop of intelligent problem-solving. An overview and analysis of existing cognitive architectures are given, concluding that many do not consider humans as part of the intelligent data processing process. The paper discusses a cognitive architecture for co-evolutionary hybrid intelligence that integrates humans and concludes with general reflections on the feasibility of developing intelligent systems with humans in the problem-solving loop.

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 modelling 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