  Edge intelligence, also called edge-native artificial intelligence (AI), is
an emerging technological framework focusing on seamless integration of AI,
communication networks, and mobile edge computing. It has been considered to be
one of the key missing components in the existing 5G network and is widely
recognized to be one of the most sought-after functions for tomorrow's wireless
6G cellular systems. In this article, we identify the key requirements and
challenges of edge-native AI in 6G. A self-learning architecture based on
self-supervised Generative Adversarial Nets (GANs) is introduced to
\blu{demonstrate the potential performance improvement that can be achieved by
automatic data learning and synthesizing at the edge of the network}. We
evaluate the performance of our proposed self-learning architecture in a
university campus shuttle system connected via a 5G network. Our result shows
that the proposed architecture has the potential to identify and classify
unknown services that emerge in edge computing networks. Future trends and key
research problems for self-learning-enabled 6G edge intelligence are also
discussed.

Networking and Internet Architecture;Artificial Intelligence