adjustments this causes the early layers to not learn because of the vanish ingredients the RNN doesn't learn the long-range dependencies across time steps this means that there is a possibility that the word light and time are not considered when trying to predict a user's intention the network has to make its best guess with is it that's pretty ambiguous and would be difficult even for a human so not being able to learn on earlier time steps causes the network tap short-term memory okay so RNN suffer from short-term