Past psychology and behavioral science studies have identified various ways in which people’s acquisition of new knowledge can be disrupted. One of these, known as interference, occurs when humans are learning new information and this makes it harder for them to correctly recall knowledge that they had acquired earlier.
Interestingly, a similar tendency was also observed in artificial neural networks (ANNs), computational models inspired by biological neurons and the connections between them. In ANNs, interference can manifest as so-called catastrophic forgetting, a process via which models “unlearn” specific skills or information after they are trained on a new task.
In some other instances, knowledge acquired in the past can instead help humans or ANNs to learn how to complete a new task. This phenomenon, known as “transfer,” entails the application of existing knowledge of skills to a novel task or problem.
