On the contrary, I think these are symptoms of machine learning coming of age [or getting industrialized]. I am not sure "short sighted commercial ends" are the main motivator for likes of Prof. Hinton to shift, but rather the availability of vast amount of data and opportunity to understand them in a scale which was not previously possible.
To bring out the real magic out of techniques like deep learning ( http://en.wikipedia.org/wiki/Deep_learning ), availability of large training sets and the infrastructure required to crunch them are a pre-requisite. Once you have that, it is turning out to be a different ball game all together http://deeplearning.net/2012/12/13/googles-large-scale-deep-.... It turns out that groups like google research are the ones at present which have access to such dataset and infrastructure.
I also predict the reverse shift to happen within few years, once the interesting fundamental research problems has been tackled such people might move back to universities. If that happens, that is indeed a healthy process of academica and industry supplementing each other.
To bring out the real magic out of techniques like deep learning ( http://en.wikipedia.org/wiki/Deep_learning ), availability of large training sets and the infrastructure required to crunch them are a pre-requisite. Once you have that, it is turning out to be a different ball game all together http://deeplearning.net/2012/12/13/googles-large-scale-deep-.... It turns out that groups like google research are the ones at present which have access to such dataset and infrastructure.
I also predict the reverse shift to happen within few years, once the interesting fundamental research problems has been tackled such people might move back to universities. If that happens, that is indeed a healthy process of academica and industry supplementing each other.