I may be missing something, but it seems that what is being described is a neural net architecture that can be trained on any of several games to get impressive

I may be missing something, but it seems that what is being described is a neural net architecture that can be trained on any of several games to get impressive results for those game. NOT that one neural net can be trained to play all of the games. So, while this is an interesting result and makes using the same architecture for specific applications easier and a bit more plug-and-play with little to no modification of the code, what it accomplishes is reducing the effort required by a software engineer or researcher on adapting the software before training even begins, but it pretty much still requires the same amount of training. What this doesn't seem to do is allow the same trained network to be applied to multiple tasks (which I think most of the AGI comments are assuming), and it certainly doesn't generalize anything among the games it is trained on.