How It Works
RNNs (Recurrent Neural Networks)
A type of AI model designed to handle sequences (text, speech, time-series data) by having a kind of memory: it processes information step by step and carries forward what it's seen so far. Before newer architectures took over, RNNs were the standard approach for language translation and speech recognition.
Origin · no single documented coiner
No single documented coiner. Developed gradually through John Hopfield's 1982 Hopfield network, Michael Jordan's 1986 recurrent network, and Jeffrey Elman's 1990 "Elman network." A major advance came in 1997 when Sepp Hochreiter and Jürgen Schmidhuber introduced LSTM, a variant that fixed RNNs' tendency to "forget" information over long sequences.