  This paper presents a technology for simple and computationally efficient
improvements of a generic Artificial Intelligence (AI) system, including
Multilayer and Deep Learning neural networks. The improvements are, in essence,
small network ensembles constructed on top of the existing AI architectures.
Theoretical foundations of the technology are based on Stochastic Separation
Theorems and the ideas of the concentration of measure. We show that, subject
to mild technical assumptions on statistical properties of internal signals in
the original AI system, the technology enables instantaneous and
computationally efficient removal of spurious and systematic errors with
probability close to one on the datasets which are exponentially large in
dimension. The method is illustrated with numerical examples and a case study
of ten digits recognition from American Sign Language.
