A GPS app can plan the best route between two subway stops if it has been specifically programmed for the task. But a new artificial intelligence system can figure out how to do so on the fly by learning general rules from specific examples, researchers report October 12 in Nature.
Artificial neural networks, computer programs that mimic the human brain, are great at learning patterns and sequences, but so far they’ve been limited in their ability to solve complex reasoning problems that require storing and manipulating lots of data. The new hybrid computer links a neural network to an external memory source that works somewhat like RAM in a regular computer.
Scientists trained the computer by giving it solved examples of reasoning problems, like finding the shortest distance between two points on a randomly generated map. Then, the computer could generalize that knowledge to solve new problems, like planning the shortest route between stops on the London Underground. Rather than being programmed, the neural network, like the human brain, responds to training: It can continually integrate new information and change its response accordingly.
The development comes from Google DeepMind, the same team behind the Alpha Go computer program that beat a world champion at the logic-based board game.