  Edge artificial intelligence (AI) has been a promising solution towards 6G to
empower a series of advanced techniques such as digital twins, holographic
projection, semantic communications, and auto-driving, for achieving
intelligence of everything. The performance of edge AI tasks, including edge
learning and edge AI inference, depends on the quality of three highly coupled
processes, i.e., sensing for data acquisition, computation for information
extraction, and communication for information transmission. However, these
three modules need to compete for network resources for enhancing their own
quality-of-services. To this end, integrated sensing-communication-computation
(ISCC) is of paramount significance for improving resource utilization as well
as achieving the customized goals of edge AI tasks. By investigating the
interplay among the three modules, this article presents various kinds of ISCC
schemes for federated edge learning tasks and edge AI inference tasks in both
application and physical layers.
