Control Systems and Computers

Control Systems and Computers, 2024, Issue 4 (308), pp. 

V.D. Minenko, Founder, Twigames Inc., Wilmington, Delaware, USA, 3422 Old Capitol Trail, Suite# 241, Wilmington, DE 19808, US, ORCID: https://orcid.org/0009-0003-5299-6786, valerii@twigames.net

ANALYSIS OF THE APPLICATION OF AI GENERATORS FOR SOLVING COMPLEX BUSINESS PROBLEMS

Introduction. In recent years, there has been significant growth in the use of Generative Artificial Intelligence (Generative AI) applications that create visual data based on textual descriptions. This opens up new possibilities for enhancing information systems across various applied fields. It goes beyond just vivid forms of representation or information display; it offers the potential to address complex practical tasks at a fundamentally different level. The ability to store the history of visualizations allows tracking the dynamics of changes in specific information over time, providing deeper information analysis and a higher level of decision-making and strategy formulation in complex business systems.

However, the semantic quality of generating visual content remains a challenge, influenced not only by the choice of the generation model itself but also by the accuracy of the input instructions.

This article is dedicated to analyzing the current state of Generative AI technologies, existing models, their functional capabilities, advantages, limitations, and drawbacks.

Purpose. The research aims, on one hand, to identify effective applications of these technologies in solving complex business tasks, and on the other hand, to highlight issues with existing models regarding their integration into the business process chain and possible ways to address them.

Results and conclusion. The analysis conducted allowed:

Identifying common features of existing systems and highlighting characteristics significant for choosing a model in terms of its integration into a complex system, such as the semantic accuracy of generation results, openness of code, price, multilinguality, etc.

Formulating directions for further research, primarily focused on developing methodological foundations for automatically generating input textual descriptions for Generative AI models in video content generation.

Keywords: generative artificial intelligence, natural language processing, AI generation, neural network, neural network model, text-to-text model, text-to-image model, text-to-video model, generative adversarial network, machine learning, business system, open-source models, semantic representation quality.