While AI systems can match many human capabilities, they take 10 times longer to learn. Now, by copying the way the brain works, Google DeepMind has built a machine that is closing the gap.
Intelligent machines have humans in their sights. Deep-learning machines already have superhuman skills when it comes to tasks such as face recognition, video-game playing, and even the ancient Chinese game of Go. So it’s easy to think that humans are already outgunned.
But not so fast. Intelligent machines still lag behind humans in one crucial area of performance: the speed at which they learn. When it comes to mastering classic video games, for example, the best deep-learning machines take some 200 hours of play to reach the same skill levels that humans achieve in just two hours.
So computer scientists would dearly love to have some way to speed up the rate at which machines learn.
Today, Alexander Pritzel and pals at Google’s DeepMind subsidiary in London claim to have done just that. These guys have built a deep-learning machine that is capable of rapidly assimilating new experiences and then acting on them. The result is a machine that learns significantly faster than others and has the potential to match humans in the not too distant future.
First, some background. Deep learning uses layers of neural networks to look for patterns in data. When a single layer spots a pattern it recognizes, it sends this information to the next layer, which looks for patterns in this signal, and so on.
So in face recognition, one layer might look for edges in an image, the next layer for circular patterns of edges (the kind that eyes and mouths make), and the next for triangular patterns such as those made by two eyes and a mouth. When all this happens, the final output is an indication that a face has been spotted.
Of course, the devil is in the details. There are various systems of feedback to allow the system to learn by adjusting various internal parameters such as the strength of connections between layers. These parameters must change slowly, since a big change in one layer can catastrophically affect learning in the subsequent layers. That’s why deep neural networks need so much training and why it takes so long.
Pritzel and co have tackled this problem with a technique they call neural episodic control. “Neural episodic control demonstrates dramatic improvements on the speed of learning for a wide range of environments,” they say. “Critically, our agent is able to rapidly latch onto highly successful strategies as soon as they are experienced, instead of waiting for many steps of optimisation.”
The basic idea behind DeepMind’s approach is to copy the way humans and animals learn quickly. The general consensus is that humans can tackle situations in two different ways. If the situation is familiar, our brains have already formed a model of it, which they use to work out how best to behave. This uses a part of the brain called the prefrontal cortex.