Long short-term memory (LSTM) is explicitly designed to address the long-term dependency problem, by maintaining a state of what to remember and what to forget.
For an artificial intelligence interview question asking to define LSTM (Long Short-Term Memory), the correct answer would be:
“LSTM stands for Long Short-Term Memory, which is a type of recurrent neural network (RNN) architecture designed to overcome the vanishing gradient problem, which is a common issue in traditional RNNs. LSTM networks are particularly effective in capturing long-term dependencies in sequential data. They achieve this by introducing a memory cell that can maintain information over time, selectively forgetting or updating information based on input signals. The key components of an LSTM unit include the input gate, forget gate, output gate, and memory cell. These gates regulate the flow of information through the cell, allowing LSTMs to learn and retain information over long sequences, making them well-suited for tasks such as speech recognition, natural language processing, and time series prediction.”