Which key feature defines machine learning applications?

Machine learning applications are characterized by their ability to learn from data without requiring explicit programming. This means that instead of being programmed with specific instructions for every possible scenario, machine learning algorithms can identify patterns and make decisions based on the data they are trained on. This aspect allows these algorithms to adapt to new, unseen data, improving their performance over time as they are exposed to more information.

In contrast, the other options depict features that do not capture the essence of machine learning. While large databases can certainly enhance the training of machine learning models, the mere presence of large data does not define the function of machine learning itself. Static algorithms, which do not adapt based on data, are contrary to the dynamic nature of machine learning. Lastly, reliance on human intervention suggests a lack of autonomy in the decision-making process, which is counter to the self-learning capability that is fundamental to machine learning applications.