Well, Geoff Hinton is on record saying that the reason ANNs are working now, where they didn't in the past is more to do with having more data and bigger computers and less with algorithmic advances (though these did help) [1]:

AMT: Ok, so you have been working on neural networks for decades but it has only exploded in its application potential in the last few years, why is that?

Geoffrey Hinton: I think it’s mainly because of the amount of computation and the amount of data now around but it’s also partly because there have been some technical improvements in the algorithms. Particularly in the algorithms for doing unsupervised learning where you’re not told what the right answer is but the main thing is the computation and the amount of data.

The algorithms we had in the old days would have worked perfectly well if computers had been a million times faster and datasets had been a million times bigger but if we’d said that thirty years ago people would have just laughed.

As to the papers you mention- researchers publish papers and in them, they make claims. That's their job. The job of anyone who reads a paper is to take it in good faith but retain their critical and skeptical stance throughout. If you're not directly involved with ANN research, or implementing your own ANN systems for some commercial application, it's very hard to know whether a claim in a paper is useful to anyone besides the scientist who published it.

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[1] http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-...