This issue arises particularly in artificial intelligence and machine learning, especially in neural networks when they are trained sequentially on different tasks. When a model is trained on a new task, the weights of the network are adjusted, which can overwrite the weights associated with prior tasks. As a result, the model may perform well on the new task but perform poorly or forget the previous tasks it learned. This presents a significant challenge in developing systems that can learn continuously and adaptively without losing earlier knowledge.
Catastrophic Forgetting Example
For example, an example of catastrophic forgetting can be seen in a language model that is trained to recognize English and then later trained on Spanish. If the model is not designed to retain information from the English training phase, it may excel at Spanish but struggle to recall its understanding of English, essentially “forgetting” it.