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In 1940s, Claude Shannon developed the information theory focusing onquantifying the maximum data rate that can be supported by a communicationchannel. Guided by this, the main theme of wireless system design up until 5Gwas the data rate maximization. In his theory, the semantic aspect and meaningof messages were treated as largely irrelevant to communication. The classictheory started to reveal its limitations in the modern era of machineintelligence, consisting of the synergy between IoT and AI. By broadening thescope of the classic framework, in this article we present a view of semanticcommunication (SemCom) and conveying meaning through the communication systems.We address three communication modalities, human-to-human (H2H),human-to-machine (H2M), and machine-to-machine (M2M) communications. The lattertwo, the main theme of the article, represent the paradigm shift incommunication and computing. H2M SemCom refers to semantic techniques forconveying meanings understandable by both humans and machines so that they caninteract. M2M SemCom refers to effectiveness techniques for efficientlyconnecting machines such that they can effectively execute a specificcomputation task in a wireless network. The first part of the articleintroduces SemCom principles including encoding, system architecture, andlayer-coupling and end-to-end design approaches. The second part focuses onspecific techniques for application areas of H2M (human and AI symbiosis,recommendation, etc.) and M2M SemCom (distributed learning, split inference,etc.) Finally, we discuss the knowledge graphs approach for designing SemComsystems. We believe that this comprehensive introduction will provide a usefulguide into the emerging area of SemCom that is expected to play an importantrole in 6G featuring connected intelligence and integrated sensing, computing,communication, and control.

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence