  A radical paradigm shift of wireless networks from ``connected things'' to
``connected intelligence'' undergoes, which coincides with the Shanno and
Weaver's envisions: Communications will transform from the technical level to
the semantic level. This article proposes a semantic communication method with
artificial intelligence tasks (SC-AIT). First, the architecture of SC-AIT is
elaborated. Then, based on the proposed architecture, we implement SC-AIT for a
image classifications task. A prototype of SC-AIT is also established for
surface defect detection, is conducted. Experimental results show that SC-AIT
has much lower bandwidth requirements, and can achieve more than $40\%$
classification accuracy gains compared with the communications at the technical
level. Future trends and key challenges for semantic communications are also
identified.
