Drawing inspiration from the natural rhythms of the human brain, researchers are exploring how periods of "sleep" could enhance artificial intelligence learning capabilities and prevent the problem of catastrophic forgetting, writes Darcy Bounsall. 

For around eight hours each night - roughly a third of our lives in total - we exist in a state of unconscious paralysis. Our heartrate and breathing slow, our body temperature falls, and we lie immobile and unresponsive. But as we slowly progress through the stages of sleep something strange begins to happen: our brain activity shoots back up to levels similar to when we’re awake. Unbeknownst to us, as we drift off, our brains are spontaneously reactivating all that we’ve experienced in our waking states.

SUGGESTED READING Why AI must learn to forget By Ali Boyle

There is a vast interdisciplinary literature spanning both psychology and neuroscience that supports the vital role sleep plays in learning and memory. Now a recent study undertaken by Maxim Bazhenov and his colleagues at the University of California San Diego has shown that artificial neural networks also learn better when combined with periods of off-line reactivation that mimic biological sleep. 

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One weakness of many of these AI models is that, unlike humans, they are often only able to learn one self-defined task extremely well.

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Artificial neural networks loosely model the neurons in a biological brain, and are currently one of the most successful machine-learning techniques for solving a variety of tasks, including language translation, image classification, and even controlling a nuclear fusion reaction.