There are many ways to minimize the risk of catastrophic interference. One approach is to add regularization terms such as L1 or L2 to control the complexity of the model and reduce its sensitivity to minor changes in the input data. Dropout involves randomly deleting a certain number of neurons during training. This can prevent the model from relying too heavily on specific neurons.