Neural networks learning to forget useless information
A team of researchers at the University of Leuven, together with Facebook AI Research, is aiming to improve machine learning by training artificial neural networks to learn and forget skills akin to how humans absorb information. Researchers have developed a way for these networks to employee Hebbian learning - the idea neurons work better when they "fire" together. If an action is perceived as correct, or useful, it can be reinforced while incorrect, or less useful, actions can be discarded. 
That’s an important skill. The world provides a never-ending source of data, much of which is irrelevant to the tricky business of survival, and most of which is impossible to store in a limited memory. So humans and other creatures have evolved ways to retain important skills while forgetting irrelevant ones.

Neural networks are now being taught how to forget.

The same cannot be said of machines. Any skill they learn is quickly overwritten, regardless of how important it is. There is currently no reliable mechanism they can use to prioritize these skills, deciding what to remember and what to forget.

Today that looks set to change thanks to the work of Rahaf Aljundi and pals at the University of Leuven in Belgium and at Facebook AI Research. These guys have shown that the approach biological systems use to learn, and to forget, can work with artificial neural networks too.

The key is a process known as Hebbian learning, first proposed in the 1940s by the Canadian psychologist Donald Hebb to explain the way brains learn via synaptic plasticity. Hebb’s theory can be famously summarized as “Cells that fire together wire together.”

In other words, the connections between neurons grow stronger if they fire together, and these connections are therefore more difficult to break. This is how we learn—repeated synchronized firing of neurons makes the connections between them stronger and harder to overwrite.

So Aljundi and co have developed a way for artificial neural networks to behave in the same way. They do this by measuring the outputs from a neural network and monitoring how sensitive they are to changes in the connections within the network.

This gives them a sense of which network parameters are most important and should therefore be preserved. “When learning a new task, changes to important parameters are penalized,” say the team. They say the resulting network has “memory aware synapses.”

They’ve put this idea through its paces with a set of tests in which a neural network trained to do one thing is then given data that trains it to do something else. For example, a network trained to recognize flowers is then shown birds. The researchers then show it flowers again to see how much of this skill is preserved.