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Artificial Neural Networks (ANNs) are computing systems inspired by the biological neural networks that constitute animal brains. ANNs are designed to simulate the way in which the human brain analyzes and processes information. They consist of interconnected layers of nodes, or "neurons," each of which takes in some input, performs a computation on it, and passes the output to the next layer. In the hierarchy of Technology/Artificial Intelligence/Deep Learning/Neural Networks, Artificial Neural Networks are a specific type of Neural Network that is used in the field of Deep Learning, which is itself a subset of Artificial Intelligence. ANNs are capable of learning and improving their performance on a task over time, through a process known as training. During training, the network is exposed to a large dataset and adjusts the weights of its connections in order to minimize the error in its outputs. ANNs are particularly well-suited to tasks that involve pattern recognition, such as image and speech recognition. They have been used to create systems that can identify objects in images, transcribe spoken language, and even compose music. ANNs are also used in natural language processing, robotics, and many other fields. In summary, Artificial Neural Networks are a type of computing system inspired by the structure and function of biological neural networks. They are a key component of Deep Learning, which is a subfield of Artificial Intelligence, and are used for tasks such as pattern recognition, natural language processing, and robotics.