Future Convergence Theory and Artificial Intelligence: A Unified Framework for Decision-Making and Prediction

This study explores the integration of Future Convergence Theory (FCT) with artificial intelligence (AI), providing a new perspective on decision-making and prediction. FCT proposes that the certainty of future states influences present dynamics, and this principle is extended to AI models. By incorporating future convergence into machine learning algorithms, AI can enhance its predictive accuracy and adaptivity. This paper develops a mathematical framework linking FCT with reinforcement learning, neural networks, and Bayesian inference, demonstrating how AI systems can optimize decisions by considering the probability distribution of future states. Furthermore, the implications for AGI (Artificial General Intelligence) and AI ethics are discussed, including how future convergence may affect AI’s long-term decision-making strategies. This work provides a foundational step toward integrating FCT into AI, opening new directions in AI research, optimization, and self-improving systems.