STATERA Library Volume 47 Chapter 06 Explores Self-Improvement in Artificial General Intelligence

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A chapter from the STATERA Library's Volume 47 on Artificial General Intelligence examines self-improvement, defined as an AI system's ability to enhance its own performance through experience, evaluation, and adaptation. Unlike conventional machine learning systems that are optimized during a fixed training phase, self-improving systems keep identifying weaknesses and discovering better strategies after deployment. The text distinguishes parameter adaptation, which adjusts numerical values within a fixed architecture, from structural improvement, which can alter modules, workflows, or memory organization. It also describes a feedback cycle in which an agent predicts, acts, measures outcomes against expectations, and updates the mechanisms responsible for errors.

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