this is a recent breakthrough google research result where a large distributed network running stochastic gradient descent optimization (SGD) and working with unlabeled images (from random youtube videos and frames) was able to develop highly meaningful emergent feature detectors including facial recognition and other object recognition (cats, human bodies, etc). as the researchers note in the article, this was previously considered impossible by conventional wisdom. most prior experiments tend to focus on labelled data samples or supervised algorithms. note however the training phase is extremely CPU intensive.
it seems likely the technology will have wideranging applications in the future including eg, most basically, for detecting similarity of images, but with much more advanced possibilities such as for cutting edge problems in AI.
based on its operational similarity to an old biological evidence/observations/speculation sometimes referred to as "grandmother neurons" (ie high level "feature detectors") it is seen as not so merely "ad hoc" and may have some real relation to actual "algorithms" used by the human brain for image recognition (or perhaps even deeper or more general cerebral processing).
presented at ICML 2012: 29th International Conference on Machine Learning,
Edinburgh, Scotland, June, 2012.
 Building High-level Features Using Large Scale Unsupervised Learning by Le et al
 How Many Computers to Identify a Cat? 16,000 by John Markoff/NYT