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A new so called 5D data storage that could potentially survive for billions of years. The research consists of nanostructured glass that can record digital data in five dimensions using femtosecond laser writing.

Yes Alex, indeed, the durability is the point I think they highlight and focus on (besides the fact the abstract says something as the extrapolated decay time being comparable to the age of the Universe..). Indeed memories face problems with retention time. Most of the disks retain the information up to 10 years. When enterprises want to store data for longer times than this they use... yeah, magnetic tapes :-). Check a interesting article about magnetic tape market revival here http://www.information-age.com/technology/data-centre-and-it-infrastructure/123458854/rise-fall-and-re-rise-magnetic-tape I compared for fun, to have one idea of what we were talking about. I am also very curious so see the writing and reading times in this new memory :)

But how can glass store the information so long? Glass is not even solid?!

as so many battery breakthroughs before, this one also looks promising ... for the time being

This has been around for over a year. The current trend in deep learning is "deeper is better". But a consequence of this is that for a given network depth, we can only feasibly evaluate a tiny fraction of the "search space" of NN architectures. The current approach to choosing a network architecture is to iteratively add more layers/units and keeping the architecture which gives an increase in the accuracy on some held-out data set i.e. we have the following information: {NN, accuracy}. Clearly, this process can be automated by using the accuracy as a 'signal' to a learning algorithm. The novelty in this work is they use reinforcement learning with a recurrent neural network controller which is trained by a policy gradient - a gradient-based method. Previously, evolutionary algorithms would typically be used. In summary, yes, the results are impressive - BUT this was only possible because they had access to Google's resources. An evolutionary approach would probably end up with the same architecture - it would just take longer. This is part of a broader research area in deep learning called 'meta-learning' which seeks to automate all aspects of neural network training.

Btw that techxplore article was cringing to read - if interested read this article instead: https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html