Google’s artificial intelligence arm has made a breakthrough in the development of thinking computers by creating a learning machine that combines a “neural network” computing system with conventional computer memory.

Scientists at DeepMind, the tech group’s London-based AI unit, have built a “differentiable neural computer”, or DNC, that for the first time can solve small-scale problems without prior knowledge, such as planning the best route between distant stations on the London Underground or working out relationships between relatives on family trees.

Neural networks — connected systems modelled on biological networks such as the brain — have played a big role in the recent and rapid progress in AI research. They are excellent at deducing patterns, for example, to enable speech recognition in digital assistants such as Google Voice or Apple’s Siri. But until now they have only been able to access the data contained within their own network. In the journal Nature the 20-strong DeepMind team said the DNC provides neural networks with access to previously incompatible external data, such as text encoded in conventional digital form.