  Deep learning is driving a radical paradigm shift in wireless communications,
all the way from the application layer down to the physical layer. Despite
this, there is an ongoing debate as to what additional values artificial
intelligence (or machine learning) could bring to us, particularly on the
physical layer design; and what penalties there may have? These questions
motivate a fundamental rethinking of the wireless modem design in the
artificial intelligence era. Through several physical-layer case studies, we
argue for a significant role that machine learning could play, for instance in
parallel error-control coding and decoding, channel equalization, interference
cancellation, as well as multiuser and multiantenna detection. In addition, we
will also discuss the fundamental bottlenecks of machine learning as well as
their potential solutions in this paper.
