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Artificial intelligence

Deep neural reasoning

Nature volume 538, pages 467–468 (2016)Cite this article

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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

A classic example of logical reasoning is the syllogism, “All men are mortal. Socrates is a man. Therefore, Socrates is mortal.” According to both ancient and modern views1, reasoning amounts to a rule-based mental manipulation of symbols — in this example, the words 'All', 'men', and so on. But human brains are made of neurons that operate by exchanging jittery electrical pulses, rather than word-like symbols. This difference encapsulates a notorious scientific and philosophical enigma, sometimes referred to as the neural–symbolic integration problem2, which remains unsolved. On page 471, Graves et al.3 use the machine-learning methods of 'deep learning' to impart some crucial symbolic-reasoning mechanisms to an artificial neural system. Their system can solve complex tasks by learning symbolic-reasoning rules from examples, an achievement that has potential implications for the neural–symbolic integration problem.

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Authors and Affiliations

  1. Herbert Jaeger is at Jacobs University Bremen, 28759 Bremen, Germany.,

    Herbert Jaeger

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  1. Herbert Jaeger
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Correspondence to Herbert Jaeger.

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Jaeger, H. Deep neural reasoning. Nature 538, 467–468 (2016). https://doi.org/10.1038/nature19477

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