A Long-Run Persistence Theory for AI Systems under the Redundancy-Adjusted Artificial Age Score (AAS)

Artificial intelligence is becoming increasingly crucial in systems that require interaction, adaptation, and continuous updating. This raises a fundamental question: can artificial intelligence systems persist indefinitely without suffering from unlimited structural aging? A recent study has developed a theoretical framework to analyze the long-term persistence of artificial intelligence systems using the adjusted artificial age scoring (AAS) metric. This approach enables the evaluation of artificial intelligence system structural aging over multiple operation cycles, taking into account redundancy and component consistency. The results show that structural aging can be limited and does not necessarily increase explosively. This has significant implications for the design and implementation of artificial intelligence systems that require continuous and efficient operation. The ability of artificial intelligence systems to persist without suffering from unlimited structural aging is crucial for their application in various fields, highlighting the importance of this study in the context of artificial intelligence research and its application in fields such as commerce and industry, where efficiency and durability are essential.

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