Artificial intelligence: Deep neural reasoning.

Abstract

The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471 Conventional computer algorithms can process extremely large and complex data structures such as the worldwide web or social networks, but they must be programmed manually by humans. Neural networks can learn from examples to recognize complex patterns, but they cannot easily parse and organize complex data structures. Now Alex Graves, Greg Wayne and colleagues have developed a hybrid learning machine, called a differentiable neural computer (DNC), that is composed of a neural network that can read from and write to an external memory structure analogous to the random-access memory in a conventional computer. The DNC can thus learn to plan routes on the London Underground, and to achieve goals in a block puzzle, merely by trial and error—without prior knowledge or ad hoc programming for such tasks.