"It might seem that the obvious course is not to make multiple models but rather to grow a network. Instead of developing two networks for recognizing cats and horses respectively, for instance, it might appear easier to teach the cat-savvy network to also recognize horses. This approach, however, forces AI designers to confront one of the main issues in lifelong learning, a phenomenon known as catastrophic forgetting. A network trained to recognize cats will develop a set of weights across its artificial neurons that are specific to that task. If it is then asked to start identifying horses, it will start readjusting the weights to make it more accurate for horses. The model will no longer contain the right weights for cats, causing it to essentially forget what a cat looks like. “The memory is in the weights. When you train it with new information, you write on the same weights,” says Siegelmann"

https://www.nature.com/articles/d41586-022-01962-y