Abstract:

The combination of machine learning and self-healing features signifies a notable progression in the evolution of adaptive AI frameworks. These systems are engineered to autonomously detect, diagnose, and correct operational irregularities, providing strength and reliability in intricate and evolving settings. The combination of adaptive machine learning models and self-repairing processes enables AI systems to identify problems, like software errors or security threats, instantly and implement fixes autonomously without human input. This research examines different methods for combining these features, such as reinforcement learning techniques for real-time decision-making and the application of predictive maintenance models that utilize deep learning to foresee failures in advance. By thoroughly examining various architectures, such as hybrid models that merge rule-based reasoning with neural networks, this study emphasizes the strengths and weaknesses of existing adaptive AI systems. Empirical findings indicate that self-healing systems are capable of cutting system downtime by as much as 40% and enhancing the overall effectiveness of AI applications, particularly in areas such as cloud computing, cybersecurity, and IoT. The results indicate that implementing these architectures can result in more robust AI solutions that can undergo ongoing enhancement and development. Moreover, the research addresses issues like the computational burden tied to real-time anomaly detection and the requirement for substantial datasets to train efficient machine learning models. The study ends with suggestions for future advancements in adaptive AI, highlighting the necessity of creating systems that harmonize responsiveness and computational efficiency.