  This paper questions the feasibility of a strong (general) data-centric
artificial intelligence (AI). The disadvantages of this type of intelligence
are discussed. As an alternative, the concept of co-evolutionary hybrid
intelligence is proposed. It is based on the cognitive interoperability of man
and machine. An analysis of existing approaches to the construction of
cognitive architectures is given. An architecture seamlessly incorporates a
human into the loop of intelligent problem solving is considered. The article
is organized as follows. The first part contains a critique of data-centric
intelligent systems. The reasons why it is impossible to create a strong
artificial intelligence based on this type of intelligence are indicated. The
second part briefly presents the concept of co-evolutionary hybrid intelligence
and shows its advantages. The third part gives an overview and analysis of
existing cognitive architectures. It is concluded that many do not consider
humans part of the intelligent data processing process. The next part discusses
the cognitive architecture for co-evolutionary hybrid intelligence, providing
integration with humans. It finishes with general conclusions about the
feasibility of developing intelligent systems with humans in the
problem-solving loop.
