What does it mean for RNNs to be 'recurrent'?

RNNs, or Recurrent Neural Networks, are termed 'recurrent' because they have connections that loop back on themselves. This architecture allows them to maintain a form of memory, capturing information from previous inputs and using it to influence how they process the current input. The recurrent connections enable RNNs to analyze sequences of data over time, making them particularly effective for tasks involving temporal or sequential dependencies, such as time series prediction, natural language processing, and speech recognition.

The ability to loop on themselves enables RNNs to create a hidden state that evolves as new inputs are processed, effectively allowing the network to remember past inputs for future reference. This characteristic is crucial for learning patterns over sequences and understanding context within the data—features that are essential for many applications of AI.