  The sixth-generation (6G) mobile networks are expected to feature the
ubiquitous deployment of machine learning and AI algorithms at the network
edge. With rapid advancements in edge AI, the time has come to realize
intelligence downloading onto edge devices (e.g., smartphones and sensors). To
materialize this version, we propose a novel technology in this article, called
in-situ model downloading, that aims to achieve transparent and real-time
replacement of on-device AI models by downloading from an AI library in the
network. Its distinctive feature is the adaptation of downloading to
time-varying situations (e.g., application, location, and time), devices'
heterogeneous storage-and-computing capacities, and channel states. A key
component of the presented framework is a set of techniques that dynamically
compress a downloaded model at the depth-level, parameter-level, or bit-level
to support adaptive model downloading. We further propose a virtualized 6G
network architecture customized for deploying in-situ model downloading with
the key feature of a three-tier (edge, local, and central) AI library.
Furthermore, experiments are conducted to quantify 6G connectivity requirements
and research opportunities pertaining to the proposed technology are discussed.
