Why do some neural networks need to remember what they just saw - and how do LSTMs solve the forgetting problem?

The answer

Recurrent Neural Networks (RNNs) process sequential data by maintaining a hidden state that passes information from one time step to the next, making them ideal for language, speech, and time-series data. However, standard RNNs suffer from the vanishing gradient problem, losing the ability to learn long-range dependencies. In 1997, Sepp Hochreiter and Jurgen Schmidhuber introduced Long Short-Term Memory (LSTM) networks, which use gating mechanisms - input, forget, and output gates - to selectively retain or discard information over long sequences. LSTMs became the dominant architecture for speech recognition, machine translation, and text generation throughout the 2010s, until Transformers emerged in 2017.