Abstract

This study explores the shift from explicitly programmed systems to machines capable of autonomous learning and adaptation, addressing the scalability and flexibility limitations of traditional programming. By integrating advanced machine learning, reinforcement learning, and self-evolving algorithms, this study aims to establish principles that enable machines to process information autonomously, adapt behaviors, and operate in dynamic, unstructured environments. Key challenges, such as ensuring system safety and robustness, are examined along with practical applications in robotics, personalized healthcare, and adaptive AI systems. This study lays the foundation for next-generation adaptive agents, providing a transformative framework to achieve true autonomy in artificial intelligence.

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