Artificial neural networks are computer systems inspired by the human brain's structure and function. They consist of interconnected nodes, or "neurons," that process information in layers. Each neuron receives input, applies a mathematical function, and passes the output to the next layer, allowing the network to learn patterns from data. These networks are commonly used in various applications, such as image recognition, natural language processing, and machine learning. By adjusting the connections between neurons based on the data they process, artificial neural networks can improve their performance over time, making them powerful tools for solving complex problems.