New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Figure 1: (a) Field of view of the blowfly Calliphora vicina mapped onto a panoramic view of an indoor scene. Blue and red borders outline the visual field of the left and right eyes; tinted area denotes area of binocular overlap.
Generalization and invariance are two essential properties of machine learning models. Generalization captures a model's ability to classify unseen data while invariance measures the consistency of model predictions on transformed data.