One thing I wish they would make more explicit (and in all their papers that I've read for that matter) is how much computational power it takes to train these networks and achieve these results.
I'm not sure if this is something they usually leave out because it's not interesting, or because it's something that people that work with deep networks all the time (I.E. not me) already have a feel for.
As someone in a related field (sometimes using deep networks but not researching them for their own sake). I certainly would like to know which of deep minds results would be feasible to replicate using my research groups resources, and it can be hard to do that without spending a lot of time actually trying to replicate the results and benchmarking them on your hardware.