Google recently hired Geoffrey Hinton,  a computer science professor focused on neural computational machine learning models.  Hinton is well known from an earlier landmark paper, Rumelhart, Hinton, and Williams (1986). As every graduate student in both neural systems and machine learning disciplines know, that paper demonstrated how to construct a self-adaptive Multi-Layer Perceptron with a backpropagating learning method. It helped revive interest in neural networks as applied machine learning models derived from neuroscience findings.  What does this hiring indicate is Google's vote for the future?

Today, neural networks embed behind the scenes in the research and development of mundane business and commercial functions.  One popular example is of IBM's Watson that competed live on Jeopardy.  Neural networks formed the top layer in a highly heterogeneous mixture of expert systems that extracted the question and answer intents that form the contest.  The researchers credited neural networks as finally enabling Watson to produce acceptable human-like accuracy. Another example is of Microsoft's Bing translator and voice recognition system.  Microsoft collabrated with Carnegie Mellon University and University of Toronto researchers on neural networks to reduce speech translation error rates from 1 in 5 words to about 1 in 8. Holding even simple question and answer conversations or translating speech are complex tasks only humans are known to perform at human levels. Replicating hallmark human activity may require replicating hallmark human brains. Does Google believe neural networks can replicate human brains on some level?

Brains mean a great deal different things to different scientists. The neural networks that model them mean a great deal many more.  Deep learning neural nets and Restricted Boltzmann Machines self-program after several passes through a training environment. They then "fixate" or "equilibrate" and get "released into production," depending on the user's needs. A very interesting property of these networks, especially for computer scientists is their O(c) production runtime with respect to the data. This computational notation says the processing time is not affected by the data size. It is fast.

In order to achieve this remarkable feat, the Hinton-esque deep learning neural network requires the operator to pre-define the network size and structure. The user essentially needs to pre-allocate a fixed memory and CPU size. Then the user also needs to pre-designate a highly controlled training environment for this network.  The network automatically self-designs its rules within these constraints.  Finally, the network gets frozen and never learns again. In effect, these networks are literally brain washed. In a closet. With brain clamps. And then lobotomized. The typical data and setup methodology (e.g. Hinton, et al., 2006) demonstrates this dance of data mining, validation, and selection.

Of course, these are machine learning models. Not only is this procedure acceptable, it is highly appropriate for machine products. It generates results. It generates them consistently. It generates them fast. These are computer science goals.

The question is not whether Hinton-esque deep learning neural networks can transform datasets, it is whether human brains and human level tasks are about transforming datasets. By hiring Hinton, Google shows they believe it is, or that is not their goal.