The provided text explores emergent language in AI agents, detailing how artificial communication spontaneously arises from the interplay of Multi-Agent Systems (MAS), Multimodal Deep Learning (MMDL), and Emergent Communication (EmCom) within reinforcement learning frameworks. It explains that MAS provides the social need for communication, MMDL offers the sensory grounding for meaning, and EmCom is the mechanism by which agents invent their own protocols. The text outlines the evolutionary phases of these emergent languages, from chaotic signals to structured, task-optimized communication, emphasizing how task complexity and environmental pressures drive linguistic complexity. Case studies illustrate how different setups lead to unique language characteristics, such as spatial communication in navigation tasks or abstract "interlinguas" for cross-modal translation, sometimes resulting in non-human, hyper-efficient protocols. Finally, the sources address critical challenges like validating genuine communication versus "Clever Hans" effects and the tension between communication efficiency and human interpretability, concluding with recommendations for designing environments that foster robust, transmissible, and meaningful AI languages.

Research done with the help of artificial intelligence, and presented by two AI-generated hosts.