The question asks to identify the concept that distinguishes AI systems from traditional software systems. Let's evaluate each option: AI systems are always more efficient than traditional systems. This is not universally true. The efficiency of a system depends on the specific task and implementation. Traditional algorithms can be highly optimized and more efficient for certain problems than AI solutions. AI systems do not rely on algorithms and models. This is incorrect. AI systems, especially those based on machine learning, are built upon sophisticated algorithms and mathematical models (e.g., neural networks, decision trees). AI systems learn from data to improve performance over time. This is a fundamental characteristic of many AI systems, particularly those involving machine learning. Unlike traditional software that follows explicitly programmed rules, AI systems can analyze data, identify patterns, and adapt their behavior to improve their accuracy or effectiveness without being explicitly reprogrammed for every new situation. AI systems require continuous manual updates to function. This is generally not the distinguishing factor. While AI models may require retraining or fine-tuning, the goal is often to reduce the need for continuous manual* updates to their core logic, as they are designed to adapt. Traditional software often requires manual updates for new features or bug fixes. The most accurate distinguishing concept is the ability of AI systems to learn and improve from data. The correct option is: AI systems learn from data to improve performance over time.