An artificial neural network learns by adjusting synaptic weights—how strongly one artificial neuron connects to another—which in turn leads to a sort of “memory” of its learnings that’s embedded into the weights.
Because retraining the neural network on another task disrupts those weights, the AI is essentially forced to “forget” its previous knowledge as a prerequisite to learn something new.
Imagine gluing together a bridge made out of toothpicks, only having to rip apart the glue to build a skyscraper with the same material. The hardware is the same, but the memory of the bridge is now lost.
But here’s the thing: if the human brain can do it, nature has already figured out a solution. Why not try it on AI?
A recent study by researchers at the University of Massachusetts Amherst and the Baylor College of Medicine did just that. Drawing inspiration from the mechanics of human memory, the team turbo-charged their algorithm with a powerful capability called “memory replay”—a sort of “rehearsal” of experiences in the brain that cements new learnings into long-lived memories.
What came as a surprise to the authors wasn’t that adding replay to an algorithm boosted its ability to retain its previous trainings… A bastardized version of the memory, generated by the network itself based on past experiences, was sufficient to give the algorithm a hefty memory boost.