LSTM networks represented a milestone in addressing the vanishing gradient problem that plagued the training of deep neural architectures. This problem occurs when gradients used to train the network diminish exponentially as they are back-propagated through layers, making it difficult to train networks with many layers. LSTMs solved this by introducing memory cells and gates that control the flow of information, enabling the network to retain important information over long sequences and ignore irrelevant details.
This invention fundamentally changed the landscape of neural network training, making it feasible to train much deeper architectures than previously possible. LSTMs proved to be particularly effective for tasks involving sequential data, such as language modeling, speech recognition, and time series prediction. The ability to capture long-term dependencies and maintain context over long input sequences has allowed LSTMs to excel in these domains, paving the way for significant advancements in natural language processing and other areas of machine learning.