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Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) commonly used in deep learning models, an advanced field within artificial intelligence (AI). GRUs address the challenge of vanishing gradients that traditional RNNs face when handling long sequences by incorporating gating mechanisms. These gates regulate information flow between time steps, enabling GRUs to capture complex temporal dependencies and patterns in data. In technology applications, GRUs are utilized for tasks such as natural language processing, speech recognition, and time-series prediction, showcasing their versatility and significance within AI and deep learning domains.