EVOLUTION AND DISTRIBUTION ANALYSIS OF MULTIMODAL ARTIFICIAL INTELLIGENCE SYSTEMS

The article considers the main aspects of evolution and performs a thorough analysis of the stages of formation of multimodal artificial intelligence systems (AIS). It was determined that in modern realities, artificial intelligence has undergone a transformational shift towards embracing multimodality in large language models. Modern approaches and ways of improving large language models by means of processing and generating a large amount of data are analyzed. The stages of transformation of artificial intelligence in the direction of multimodality of innovative development in large language models have been studied. The issue of verification and interaction of information systems with the surrounding world is considered. It was determined that they are inherently multimodal, multicomponent. Ways of improving large language models with the help of the ability to process and generate different data modalities are analyzed. It has been investigated that modern multimodal artificial intelligence systems are effectively used in various fields of science, education, and economics and require further development and improvement. It was determined that due to the rapid development of information technologies and systems in various spectrums of life, AI is experiencing a rapid modification, where generative models, which are becoming more and more perfect, deserve special attention. An overview of the architecture of the AnyGPT model is performed, where modalities are tokenized into discrete tokens, on the basis of which LLM performs multimodal perception and generation in autoregression. The methodology underlying AnyGPT was found to be multi-component, with the model demonstrating capabilities on par with specialized models in all assessment modalities tested. It has been established that tools designed to detect objects generated by artificial intelligence are in a state of development and are constantly being modified.