What defines machine learning in simple terms?

Machine learning can be defined simply as a system that learns and improves from data over time. This means that unlike traditional programming, where a system operates based on explicitly programmed rules, machine learning algorithms can identify patterns, make predictions, and improve their performance based on the data they process. As they are exposed to more data and feedback, they adjust their models and refine their ability to perform specific tasks, thereby enhancing their accuracy and efficiency without needing constant human intervention. This characteristic of evolving with experience is what truly sets machine learning apart from other static systems.

In contrast, the other choices represent characteristics not aligned with the core definition of machine learning. Systems relying on fixed rules do not have the adaptability that machine learning offers. Similarly, requiring constant human supervision contradicts the autonomy that machine learning strives to achieve as it evolves and learns from data independently. Lastly, labeling machine learning merely as a tool for data storage misses its dynamic and analytical nature, which is focused on deriving insights and improving processes through learned experiences rather than just keeping data.