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MIT Review recently published this article about a chip from IBM, which is more or less a Artificial neural network. Why IBM’s New Brainlike Chip May Be “Historic” | MIT Technology Review

The article suggests that the chip might have borrowed a page from the future. It might be the beginning of an era of new and evolved computation power. And also talks about programming for the chip.

One downside is that IBM’s chip requires an entirely new approach to programming. Although the company announced a suite of tools geared toward writing code for its forthcoming chip last year (see “IBM Scientists Show Blueprints for Brainlike Computing”), even the best programmers find learning to work with the chip bruising, says Modha: “It’s almost always a frustrating experience.” His team is working to create a library of ready-made blocks of code to make the process easier.

Which brings me to the question, can everyday computing tasks be broken down into ones solvable by a neural network (theoretically and/or practically)?

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Neural networks can arbitrarily approximate any computable function; see http://en.wikipedia.org/wiki/Artificial_neural_network or http://en.wikipedia.org/wiki/Cybenko_theorem

Constructing a NN for a given function -- or worse yet, learning it from examples -- is a hard problem.

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RNN Turing Completeness is proven by Siegelmann while back(search for 'On the Computational Power of Neural Nets'). If you are question is to ask whether NN based computer can be TC, the answer is yes.

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