Resonance diagnostics of production space of generative systems of artificial intelligence

Abstract

The development of artificial intelligence generative systems (AIGS) in the modern world requires addressing issues related to the quality, stability, and efficiency of the generated content. In this context, resonance diagnostics become of paramount importance. The purpose of this study is to explore the possibilities of applying resonance diagnostics for detecting, analyzing, and resolving problems in artificial intelligence generative systems. To achieve the set goal, the following tasks were identified: analysis of the theoretical foundations of resonance diagnostics; investigation of the potential of using resonance signals to adjust AIGS learning parameters; studying the impact of resonance diagnostics on the stability and adaptation of AIGS to changing operating conditions. The study conducted an analysis of resonance diagnostics in the context of AIGS and revealed its powerful influence on addressing issues related to system quality and productivity. The research demonstrated that resonance diagnostics can be used to achieve realism, diversity, and quality of generated content. Additionally, it was determined that it can contribute to enhancing the stability and adaptation of systems to varying operational conditions