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Currently it doesn't work, but saying it'll never work is pretty strong.

This kind of demo shows that deep neural networks can capture the structure of language, if not the semantics, in a very general way. And we have separate evidence that they can (in principle) capture semantic meaning and algorithmic reasoning as well, for example: http://arxiv.org/pdf/1410.5401v2.pdf (the "neural Turing machines" paper from DeepMind)



This is better, but you get pretty far with just markov chains with probabilities for letters actually.


Show me markov chain implementation that can write code letter by letter and I'll give you a car.

(And I mean plain markov chain, not something with additional logic that understands code structure)

comment by samizdatum shows pretty well how well markov chains work without some tweaking.




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