  Reservoir Computing offers a great computational framework where a physical
system can directly be used as computational substrate. Typically a "reservoir"
is comprised of a large number of dynamical systems, and is consequently
high-dimensional. In this work, we use just a single simple low-dimensional
dynamical system, namely a driven pendulum, as a potential reservoir to
implement reservoir computing. Remarkably we demonstrate, through numerical
simulations, as well as a proof-of-principle experimental realization, that one
can successfully perform learning tasks using this single system. The
underlying idea is to utilize the rich intrinsic dynamical patterns of the
driven pendulum, especially the transient dynamics which has so far been an
untapped resource. This allows even a single system to serve as a suitable
candidate for a "reservoir". Specifically, we analyze the performance of the
single pendulum reservoir for two classes of tasks: temporal and non-temporal
data processing. The accuracy and robustness of the performance exhibited by
this minimal one-node reservoir in implementing these tasks strongly suggest a
new direction in designing the reservoir layer from the point of view of
efficient applications. Further, the simplicity of our learning system offers
an opportunity to better understand the framework of reservoir computing in
general and indicates the remarkable machine learning potential of even a
single simple nonlinear system.
