SELF-IMPROVING COMPUTATIONAL MODELS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract

The paradigm of static computational modeling is rapidly being superseded by self-improving systems that leverage Artificial Intelligence (AI) and Machine Learning (ML). A self-improving computational model is defined as an architectural framework capable of autonomously monitoring its performance, identifying algorithmic inefficiencies, and updating its internal parameters or structural logic without human intervention. This paper investigates the mechanisms of recursive self-improvement, focusing on Meta-Learning, Reinforcement Learning (RL), and Automated Machine Learning (AutoML). We propose a unified system architecture that integrates a "Feedback Loop Controller" with a "Dynamic Optimization Engine." Through experimental simulation, this study demonstrates that self-improving models exhibit higher resilience to data drift and significantly lower long-term error rates compared to traditional fixed-parameter models. The findings suggest a shift toward "Liquid AI," where software evolves in real-time to meet the demands of fluctuating computational environments.