<![CDATA[Coming from the breakthrough of AI-powered adaptive metamaterials (AI-AM), as reconfigurable optoelectronics, these represent a technology that allows real-time, autonomous optical and electronic control. This work presents an AI-AM framework based on machine learning, reinforcement learning, and neuromorphic computing, which aims to develop a new artificial intelligence that optimally dynamically modifies metamaterial behavior. In contrast to traditional metamaterials, the proposed system implements self-adjusting of the wavelength selectivity, polarization, and beam steering at the nanoscale using AI-driven control focused on environmental stimuli. It uses generative AI models to come up with the most optimal material configurations, reinforcement learning to adapt the tuning process, and edge AI processors for running optimised decisions in nanoseconds. For the evaluation and simulation, it is shown that active and passive integrated circuits are capable of significant improvements for response time, energy efficiency, and functional adaptability, compared to conventional approaches. Some key applications of smart lenses for augmented reality, beam steering for 5G/6G networks in AI mode, quantum-enhanced sensor and hardware configuration for neuromorphic photonic processors, etc. This work proposes a paradigm shift in the optoelectronic technology and bridges the gap between artificial intelligence and material science. Based on this study, the potential of using AI augmented metamaterials for revolutionizing photonics, communications, and quantum computing, and next-generation AI intelligent optoelectronic devices with highly reconfigurable, highly efficient, and highly multifunctional properties is demonstrated. The other two areas that future research will address will be scalability, advanced AI training models, and broader real-world applications.]]>
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