Computer scientists have created a machine learning model that improves efficiency by intentionally forgetting and relearning information.

The model's adaptability is enhanced through a 'periodic forgetting' technique, which is particularly beneficial for learning new languages.

By resetting its embedding layer in early training, the model is better equipped to handle smaller data sets and computational constraints.

This approach draws inspiration from human cognitive processes, aiming to make AI more adaptable and capable of abstract thinking.

The technique has exhibited promising results, outperforming traditional models in learning new languages with improved accuracy.

The innovation has the potential to democratize AI across various languages, especially those with scarce training resources.