  Massive emerging applications are driving demand for the ubiquitous
deployment of computing power today. This trend not only spurs the recent
popularity of the \emph{Computing and Network Convergence} (CNC), but also
introduces an urgent need for the intelligentization of a management platform
to coordinate changing resources and tasks in the CNC. Therefore, in this
article, we present the concept of an intelligence-endogenous management
platform for CNCs called \emph{CNC brain} based on artificial intelligence
technologies. It aims at efficiently and automatically matching the supply and
demand with high heterogeneity in a CNC via four key building blocks, i.e.,
perception, scheduling, adaptation, and governance, throughout the CNC's life
cycle. Their functionalities, goals, and challenges are presented. To examine
the effectiveness of the proposed concept and framework, we also implement a
prototype for the CNC brain based on a deep reinforcement learning technology.
Also, it is evaluated on a CNC testbed that integrates two open-source and
popular frameworks (OpenFaas and Kubernetes) and a real-world business dataset
provided by Microsoft Azure. The evaluation results prove the proposed method's
effectiveness in terms of resource utilization and performance. Finally, we
highlight the future research directions of the CNC brain.
