Define "machine learning."

Machine learning is indeed best defined as a subset of artificial intelligence that enables systems to learn from data. This approach allows algorithms to identify patterns and make decisions based on input data without needing explicit programming for each task. The key element here is that machine learning algorithms improve their performance over time as they are fed more data, which distinguishes them from traditional programming methods that require direct instructions for every action.

In machine learning, the systems analyze data to find trends or patterns, thus allowing them to make predictions or automate processes based on previously learned information. This ability to adapt and improve based on experience is central to the functionality of machine learning.

Contrasting with the incorrect answers, manual programming involves strictly defined instructions and does not adapt over time. Replicating human intelligence suggests an attempt to fully mimic human cognitive processes, which is broader than the focused scope of machine learning. Overwriting existing software refers to the process of replacing software data without implying any learning or adaptive capabilities, which is unrelated to the concept of machine learning. Overall, the essence of machine learning lies in the ability to learn and make decisions based on data, differentiating it from traditional computing methods.