  In this paper we present theory and algorithms enabling classes of Artificial
Intelligence (AI) systems to continuously and incrementally improve with
a-priori quantifiable guarantees - or more specifically remove classification
errors - over time. This is distinct from state-of-the-art machine learning,
AI, and software approaches. Another feature of this approach is that, in the
supervised setting, the computational complexity of training is linear in the
number of training samples. At the time of classification, the computational
complexity is bounded by few inner product calculations. Moreover, the
implementation is shown to be very scalable. This makes it viable for
deployment in applications where computational power and memory are limited,
such as embedded environments. It enables the possibility for fast on-line
optimisation using improved training samples. The approach is based on the
concentration of measure effects and stochastic separation theorems and is
illustrated with an example on the identification faulty processes in Computer
Numerical Control (CNC) milling and with a case study on adaptive removal of
false positives in an industrial video surveillance and analytics system.
