Convolutional Neural Networks (CNNs) are a type of artificial intelligence model designed to process and analyze visual data. They are particularly effective for tasks like image recognition and classification. CNNs use layers of filters to automatically detect features such as edges, textures, and shapes in images, allowing them to learn complex patterns. The architecture of CNNs typically includes convolutional layers, pooling layers, and fully connected layers. The convolutional layers apply filters to the input data, while pooling layers reduce the dimensionality, making the model more efficient. This structure enables CNNs to achieve high accuracy in tasks related to computer vision, such as identifying objects in photos or videos.