This paper explores the applications of stochastic methods in the field of artificial intelligence (AI), focusing on their contribution to optimization, learning, and decision-making. By employing stochastic models, we enable AI to effectively handle uncertainty, unpredictability, and data variation. We analyze how stochastic processes can improve the convergence of algorithms in training deep neural networks and optimizing parameters. Additionally, we investigate the applications of stochastic simulations in assessing the performance of machine learning algorithms in real-world conditions.
Through this study, we highlight the key advantages and challenges of stochastic methods in the field of artificial intelligence. Furthermore, we explore how stochastic methods allow artificial intelligence models to better adapt to changing circumstances and unpredictable situations, thereby increasing their robustness and applicability in the real world. We analyze the impact of stochastic elements on decision-making processes, examining how these properties contribute to the adaptability of systems in dynamic environments.
By reviewing relevant studies and experiments, this research provides insight into how stochastic methods contribute to the evolution of artificial intelligence, opening new perspectives for understanding and improving the performance of models in real-world scenarios. This paper contributes to a broader understanding of the role of stochastic processes in the field of artificial intelligence, emphasizing the potential for further development and applications in various domains