Unlike previous generation networks that were mainly designed to meet the requirements of human-type communications, 5G networks enable the collection of data from machines with the total number of devices expected to be about 26 billion in 2026 according to Ericsson Mobility Report. The next step in 6G systems is to enable a new spectrum of control applications based on these data, such as extended reality, remote surgery, autonomous vehicle platoons. The design of communication systems for control applications requires meeting the strict delay and reliability requirements of communication systems and addressing the semantics of the control systems. This can only be achieved by using a heterogeneous network architecture, including terrestrial communication, satellites, UAVs, and underwater communication, and higher frequencies, including mmwave, THz, and optical communications, in addition to sub-6GHz transmission. All together increases the complexity of the networks while requiring their adaptivity to various applications and networks. In the first part of this talk, AI-based communication techniques, technologies, and architectures are introduced by demonstrating the usage of extreme value theory, federated learning, and reinforcement learning. In the second part of the talk, the fundamental paradigm shift from the Shannon paradigm is introduced. While the Shannon paradigm aims to guarantee the correct reception of each single transmitted bit, irrespective of the meaning conveyed by transmitted bits, communication for control applications focuses on guaranteeing the success of the task execution, such as plant stability for automated production lines, and detection accuracy in cooperative vehicle systems. Novel AI based resource allocation techniques for the joint design of control and communication systems are presented.