The capacity to generate meaningful symbols and effectively employ them for advanced cognitive processes, such as communication, reasoning, and planning, constitutes a fundamental and distinctive aspect of human intelligence. Existing deep neural networks still notably lag human capabilities in terms of generating symbols for higher cognitive functions. Here, we propose a solution (symbol emergence artificial network (SEA-net)) to endow neural networks with the ability to create symbols, understand semantics, and achieve communication. SEA-net generates symbols that dynamically configure the network to perform specific tasks. These symbols capture compositional semantic information that allows the system to acquire new functions purely by symbolic manipulation or communication. In addition, these self-generated symbols exhibit an intrinsic structure resembling that of natural language, suggesting a common framework underlying the generation and understanding of symbols in both human brains and artificial neural networks. We believe that the proposed framework will be instrumental in producing more capable systems that can synergize the strengths of connectionist and symbolic approaches for artificial intelligence (AI).

翻译：生成有意义的符号并有效将其用于高级认知过程（如通信、推理和规划）的能力，构成了人类智能中基础且独特的方面。现有的深度神经网络在生成用于高级认知功能的符号方面仍显著落后于人类能力。在此，我们提出一种解决方案（符号涌现人工网络（SEA-net）），以赋予神经网络创建符号、理解语义并实现通信的能力。SEA-net生成的符号可动态配置网络以执行特定任务。这些符号捕获组合语义信息，使得系统能够仅通过符号操作或通信获取新功能。此外，这些自生成符号展现出类似自然语言的内在结构，暗示了人脑与人工神经网络在符号生成和理解背后存在共同框架。我们相信，所提出的框架将有助于构建更强大的系统，将联结主义与符号主义方法的优势协同用于人工智能（AI）。