-Their chip only has a superficial connection to biological neurons. Any characterization of this chip in terms of "brains" is frankly bullshit. This is like measuring Google's computing clusters in terms of human brainpower.
-Everything their new chip can do has been done in software / FPGAs. While moving ANN's to ASICs can improve training speed (and is important), it does not help with the unsolved algorithmic challenges they present. Furthermore, the hardware structure severely constrains the ways in which these ANNs can be applied.
-Lastly, please refrain from drawing conclusions or extrapolating on science-related articles in the popular press. They are characterized by hyperbole and misinformation, and in many cases are flat-out wrong.
I'm sure you're quite right and the IEEE link makes oodles of sense, but for people examining notions of creativity in computational intelligence (for example), the idea of these chips is quite attractive.
In fact, I remember programming ANNs in horribly non-distributed C paradigms, and even PureData objects trying to come up with non-garbage computer music in the 90's and thinking we needed precisely the kind of chip they're trying to engineer.
This, and the advent of HTMs and other non-ANN ways of going about it, mean that chips that handle distributed processing for applications that model human creativity (which is necessarily about concurrent time-based activities), are a -good- thing no matter what the degree of success, IMHO.
