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Machine learning/artificial intelligence (ML/AI) is fundamentally concerned with finding patterns in data. The popularity of ML/AI is growing rapidly because of its ability to make predictions and classify or label data in datasets that are too large to work with manually.

With all of the hype surrounding ML/AI, it's important to understand what it can't do. It can't autonomously write a coherent novel. It can't predict tomorrow's winning lottery numbers. In fact, the applications of ML/AI are much more limited than many assume. The capabilities of ML/AI are limited to three things:

 Making predictions—for example, forecasting the weather or calculating the arrival time to a destination

 Identifying patterns—Recognizing objects in images and classifying them according to their contents

 Identifying anomalies—Detecting fraud or hacking attempts

ML/AI requires significant human input, and some applications require more human input than others. In fact, ML/AI is broken down into learning models based on the degree of human input required for them to operate—supervised and unsupervised.