Artificial Intelligence: Google's DeepMind Creates Neural Network That Can 'Logically Reason' Its Way Around London Underground

This is a problem for scientists working toward the creation of Artificial Intelligence (AI) systems capable of performing complex tasks with minimal human supervision. In a step toward overcoming this hurdle, researchers at Google's DeepMind -- the company that developed the Go-playing computer program AlphaGo -- announced earlier this week the creation of a neural network that can not only learn, but can also use data stored in its memory to "logically reason" and make inferences to answer questions. DeepMind's new system -- called a Differentiable Neural Computer (DNC) -- combines deep learning, wherein it can learn from examples and make sense of complex input it has never received before, with an external memory, which, as the DeepMind researchers Alexander Graves and Greg Wayne explain in a blog post, allows it to "store knowledge quickly and reason about it flexibly." In order to achieve this, the researchers first trained the neural network using randomly generated map-like structures -- a process that allowed the DNC to learn how to store connections between various parts in its external memory. After this, when it was confronted with a new map, the DNC was able to provide answers that were not explicitly stated in the data set.