What does machine learning enable systems to do?

Machine learning enables systems to learn and improve from experience by processing large amounts of data and identifying patterns within that data. This capability allows algorithms to evolve and enhance their performance over time without being explicitly programmed for each specific task. For instance, a machine learning model used in image recognition becomes more accurate as it analyzes more images, adapting its approach based on the feedback received from previous classifications. This dynamic learning process is fundamental in various applications, from recommendation systems and speech recognition to self-driving cars and medical diagnosis.

In contrast, other options such as compiling code faster, storing data indefinitely, or preventing errors in programming do not capture the essence of what machine learning fundamentally does. While these are important aspects in computing and software development, they do not relate directly to the learning and adaptive capabilities that define machine learning.