Google’s DeepMind team is back with an updated deep neural net dubbed the “differentiable neural computer (DNC).” Taking inspiration from plasticity mechanisms in the hippocampus, our brain’s memory storage system, the team has added a memory module to a deep learning neural network that allows it to quickly store and access learned bits of knowledge when needed.

With training, the algorithm can flexibly solve difficult reasoning problems that stump conventional neural networks — for example, navigating the London Underground subway system or reasoning about interpersonal relationships based on a family tree.

That might not sound impressive, but DNCs could be a gateway to more powerful computational engines that marry deep learning with rational thinking.

Posted by Peter Scott

Peter Scott’s résumé reads like a Monty Python punchline: half business coach, half information technology specialist, half teacher, three-quarters daddy. After receiving a master’s degree in Computer Science from Cambridge University, he has worked for NASA’s Jet Propulsion Laboratory as an employee and contractor for over thirty years, helping advance our exploration of the Solar System. Over the years, he branched out into writing technical books and training. Yet at the same time, he developed a parallel career in “soft” fields of human development, getting certifications in NeuroLinguistic Programming from founder John Grinder and in coaching from the International Coaching Federation. In 2007 he co-created a convention honoring the centennial of the birth of author Robert Heinlein, attended by over 700 science fiction fans and aerospace experts, a unique fusion of the visionary with the concrete. Bridging these disparate worlds positions him to envisage a delicate solution to the existential crises facing humanity. He lives in the Pacific Northwest with his wife and two daughters, writing the Human Cusp blog on dealing with exponential change.

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