What is the main goal of using Machine Learning models in AI?

The primary goal of using Machine Learning models in AI is to enable predictive capabilities through data analysis. Machine Learning algorithms analyze large datasets, identify patterns, and make predictions based on the insights gained from the data. This predictive capability allows businesses and organizations to make informed decisions, optimize processes, and anticipate future trends or behaviors, which is a fundamental aspect of AI applications.

For instance, in applications such as recommendation systems, predictive maintenance, or fraud detection, Machine Learning models use past data to predict future outcomes effectively. This makes it possible to tailor services to individual needs, improve efficiency, and ultimately drive better performance in various domains.

In contrast, focusing solely on increasing hardware performance does not inherently involve intelligent data analysis, while automating repetitive tasks is more about process efficiency rather than predictive insights derived from ML. Facilitating project management pertains to organization and planning, which falls outside the core functions of Machine Learning aimed at prediction and data-driven decision-making.