  Practical applications of artificial intelligence increasingly often have to
deal with the streaming properties of real data, which, considering the time
factor, are subject to phenomena such as periodicity and more or less chaotic
degeneration - resulting directly in the concept drifts. The modern concept
drift detectors almost always assume immediate access to labels, which due to
their cost, limited availability and possible delay has been shown to be
unrealistic. This work proposes an unsupervised Parallel Activations Drift
Detector, utilizing the outputs of an untrained neural network, presenting its
key design elements, intuitions about processing properties, and a pool of
computer experiments demonstrating its competitiveness with state-of-the-art
methods.
