  This article introduces a neural network-based signal processing framework
for intelligent reflecting surface (IRS) aided wireless communications systems.
By modeling radio-frequency (RF) impairments inside the "meta-atoms" of IRS
(including nonlinearity and memory effects), we present an approach that
generalizes the entire IRS-aided system as a reservoir computing (RC) system,
an efficient recurrent neural network (RNN) operating in a state near the "edge
of chaos". This framework enables us to take advantage of the nonlinearity of
this "fabricated" wireless environment to overcome link degradation due to
model mismatch. Accordingly, the randomness of the wireless channel and RF
imperfections are naturally embedded into the RC framework, enabling the
internal RC dynamics lying on the edge of chaos. Furthermore, several practical
issues, such as channel state information acquisition, passive beamforming
design, and physical layer reference signal design, are discussed.
