Which of the following would NOT be a typical application of machine learning?

Developing software for basic arithmetic does not typically involve the complexities that machine learning techniques are designed to address. Machine learning is most beneficial in scenarios where there is a need to identify patterns, make predictions based on data, or learn from past experiences—contexts where arithmetic operations are straightforward and defined.

In contrast, predictive maintenance in manufacturing, automating data entry tasks, and medical diagnosis support systems all leverage machine learning to discern patterns from historical data, optimize processes, and enhance decision-making. Predictive maintenance uses algorithms to predict equipment failures before they occur, automating data entry utilizes machine learning to recognize and categorize data inputs, and medical diagnosis support systems analyze patient data to assist healthcare professionals in making clinical decisions. These applications require advanced algorithms, data processing, and learning capabilities that are the core strengths of machine learning, distinguishing them from simple arithmetic software development.