  The increasing popularity of machine learning solutions puts increasing
restrictions on this field if it is to penetrate more aspects of life. In
particular, energy efficiency and speed of operation is crucial, inter alia in
portable medical devices. The Reservoir Computing (RC) paradigm poses as a
solution to these issues through foundation of its operation: the reservoir of
states. Adequate separation of input information translated into the internal
state of the reservoir, whose connections do not need to be trained, allow to
simplify the readout layer thus significantly accelerating the operation of the
system. In this brief review article, the theoretical basis of RC was first
described, followed by a description of its individual variants, their
development and state-of-the-art applications in chemical sensing and
metrology: detection of impedance changes and ion sensing. Presented results
indicate applicability of reservoir computing for sensing and validating the
SWEET algorithm experimentally.
