  The recent revival of artificial intelligence (AI) is revolutionizing almost
every branch of science and technology. Given the ubiquitous smart mobile
gadgets and Internet of Things (IoT) devices, it is expected that a majority of
intelligent applications will be deployed at the edge of wireless networks.
This trend has generated strong interests in realizing an "intelligent edge" to
support AI-enabled applications at various edge devices. Accordingly, a new
research area, called edge learning, emerges, which crosses and revolutionizes
two disciplines: wireless communication and machine learning. A major theme in
edge learning is to overcome the limited computing power, as well as limited
data, at each edge device. This is accomplished by leveraging the mobile edge
computing (MEC) platform and exploiting the massive data distributed over a
large number of edge devices. In such systems, learning from distributed data
and communicating between the edge server and devices are two critical and
coupled aspects, and their fusion poses many new research challenges. This
article advocates a new set of design principles for wireless communication in
edge learning, collectively called learning-driven communication. Illustrative
examples are provided to demonstrate the effectiveness of these design
principles, and unique research opportunities are identified.
