  Advances in artificial intelligence are driven by technologies inspired by
the brain, but these technologies are orders of magnitude less powerful and
energy efficient than biological systems. Inspired by the nonlinear dynamics of
neural networks, new unconventional computing hardware has emerged with the
potential for extreme parallelism and ultra-low power consumption. Physical
reservoir computing demonstrates this with a variety of unconventional systems
from optical-based to spintronic. Reservoir computers provide a nonlinear
projection of the task input into a high-dimensional feature space by
exploiting the system's internal dynamics. A trained readout layer then
combines features to perform tasks, such as pattern recognition and time-series
analysis. Despite progress, achieving state-of-the-art performance without
external signal processing to the reservoir remains challenging. Here we show,
through simulation, that magnetic materials in thin-film geometries can realise
reservoir computers with greater than or similar accuracy to digital recurrent
neural networks. Our results reveal that basic spin properties of magnetic
films generate the required nonlinear dynamics and memory to solve machine
learning tasks. Furthermore, we show that neuromorphic hardware can be reduced
in size by removing the need for discrete neural components and external
processing. The natural dynamics and nanoscale size of magnetic thin-films
present a new path towards fast energy-efficient computing with the potential
to innovate portable smart devices, self driving vehicles, and robotics.
