Abstract
AbstractThe article describes the author's proposal on cognitive architecture for the development of a general-level artificial intelligent agent («strong» artificial intelligence). New principles for the development of such an architecture are proposed—a hybrid approach in artificial intelligence and bionics. The architecture diagram of the proposed solution is given and descriptions of possible areas of application are described. Strong artificial intelligence is a technical solution that can solve arbitrary cognitive tasks available to humans (human-level artificial intelligence) and even surpass the capabilities of human intelligence (artificial superintelligence). The fields of application of strong artificial intelligence are limitless—from solving current problems facing the human to completely new problems that are not yet available to human civilization or are still waiting for their discoverer. The novelty of the work lies in the author's approach to the construction of cognitive architecture, which has absorbed the results of many years of research in the field of artificial intelligence and the results of the analysis of cognitive architectures of other researchers. The relevance of the work is based on the indisputable fact that current research in the field of weak artificial intelligence is starting to slow down due to the impossibility of solving general problems, and most national strategies for the development of technologies in the field of artificial intelligence declare the need to develop new artificial intelligence technologies, including Artificial General Intelligence. The work will be of interest to scientists, engineers, and researchers working in the field of artificial intelligence in general, as well as to any interested readers seeking to keep abreast of modern technologies.KeywordsArtificial intelligenceStrong artificial intelligenceCognitive architectureArtificial intelligent agentHybrid artificial intelligenceBionic approachMachine learningMultisensory integrationGoal-settingExplainability
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