Google’s DeepMind artificial intelligence lab does more than just develop computer programs capable of beating the world’s best human players in the ancient game of Go. The DeepMind unit has also been working on the next generation of deep learning software that combines the ability to recognize data patterns with the memory required to decipher more complex relationships within the data.
Deep learning is the latest buzz word for artificial intelligence algorithms called neural networks that can learn over time by filtering huge amounts of relevant data through many “deep” layers. The brain-inspired neural network layers consist of nodes (also known as neurons). Tech giants such as Google, Facebook, Amazon, and Microsoft have been training neural networks to learn how to better handle tasks such as recognizing images of dogs or making better Chinese-to-English translations. These AI capabilities have already benefited millions of people using Google Translate and other online services.
But neural networks face huge challenges when they try to rely solely on pattern recognition without having the external memory to store and retrieve information. To improve deep learning’s capabilities, Google DeepMind created a “differentiable neural computer” (DNC) that gives neural networks an external memory for storing information for later use.
“Neural networks are like the human brain; we humans cannot assimilate massive amounts of data and we must rely on external read-write memory all the time,” says Jay McClelland, director of the Center for Mind, Brain and Computation at Stanford University. “We once relied on our physical address books and Rolodexes; now of course we rely on the read-write storage capabilities of regular computers.”
McClelland is a cognitive scientist who served as one of several independent peer reviewers for the Google DeepMind paper that describes development of this improved deep learning system. The full paper is presented in the 12 Oct 2016 issue of the journal Nature.
The DeepMind team found that the DNC system’s combination of the neural network and external memory did much better than a neural network alone in tackling the complex relationships between data points in so-called “graph tasks.” For example, they asked their system to either simply take any path between points A and B or to find the shortest travel routes based on a symbolic map of the London Underground subway.
An unaided neural network could not even finish the first level of training, based on traveling between two subway stations without trying to find the shortest route. It achieved an average accuracy of just 37 percent after going through almost two million training examples. By comparison, the neural network with access to external memory in the DNC system successfully completed the entire training curriculum and reached an average of 98.8 percent accuracy on the final lesson.
The external memory of the DNC system also proved critical to success in performing logical planning tasks such as solving simple block puzzle challenges. Again, a neural network by itself could not even finish the first lesson of the training curriculum for the block puzzle challenge. The DNC system was able to use its memory to store information about the challenge’s goals and to effectively plan ahead by writing its decisions to memory before acting upon them.
In 2014, DeepMind’s researchers developed another system, called the neural Turing machine, that also combined neural networks with external memory.
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