Architecture of Self-Learning Artificial Intelligence

        The architecture of Self-Learning Artificial Intelligence (SLAI) represents a revolutionary approach to the design of autonomous systems. SLAI integrates cutting-edge technologies such as message-passing neural networks (MPNN), knowledge graphs, and large language models (LLM), creating a unified platform for data analysis, process optimization, and self-directed learning. This system is capable not only of adapting to new conditions but also of efficiently accumulating knowledge, identifying complex dependencies in data, and independently improving its performance. SLAI demonstrates a unique ability to decompose complex tasks, autonomously generate and optimize program code, and uncover hidden patterns. These capabilities enable it to surpass traditional approaches to artificial intelligence, significantly accelerating task execution, reducing errors, and minimizing reliance on human oversight. SLAI lays the foundation for the development of scalable and autonomous systems capable of solving highly complex tasks. Through its ability to integrate, learn, and act, SLAI establishes a cornerstone for the realization of Artificial General Intelligence (AGI). However, its development also raises critical questions about the future of human-machine interaction, inviting us to consider a world where systems are not only capable of executing commands but also of making decisions based on their own insights.