Convolutional Neural Networks (CNNs) are a type of artificial intelligence model designed to process and analyze visual data, such as images and videos. They use a specialized structure that mimics the way humans perceive visual information, allowing them to identify patterns, shapes, and objects effectively. CNNs consist of layers that perform convolutions, pooling, and activation functions to extract features from the input data. These networks are particularly useful in applications like image recognition, where they can classify objects or detect faces. By leveraging their hierarchical structure, CNNs can learn increasingly complex features at each layer, making them powerful tools in fields like computer vision and deep learning, often associated with machine learning and artificial intelligence.