Self-learning AI encompasses systems capable of independently enhancing their performance over time by learning from data and experiences without explicit programming. A core enabling technology is reinforcement learning, a machine learning paradigm where an intelligent agent learns to make optimal decisions through trial and error. This process involves the agent taking actions within a dynamic environment and receiving feedback in the form of rewards or penalties, allowing it to develop strategies that maximize a cumulative reward signal. Primary applications for this technology are transforming numerous sectors, including industrial automation, autonomous navigation in vehicles, algorithmic trading in financial industries, personalized treatment strategies in healthcare, and sophisticated natural language processing.