Being able to create meaningful symbols and proficiently use them for higher cognitive functions such as communication, reasoning, planning, etc., is essential and unique for human intelligence. Current deep neural networks are still far behind human's ability to create symbols for such higher cognitive functions. Here we propose a solution, named SEA-net, to endow neural networks with ability of symbol creation, semantic understanding and communication. SEA-net generates symbols that dynamically configure the network to perform specific tasks. These symbols capture compositional semantic information that enables the system to acquire new functions purely by symbolic manipulation or communication. In addition, we found that 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 hope that it will be instrumental in producing more capable systems in the future that can synergize the strengths of connectionist and symbolic approaches for AI.

翻译：能够创造有意义的符号并熟练运用它们进行通信、推理、规划等高级认知功能，是人类智能的核心特质与独特能力。当前深度神经网络在创造此类高级认知功能所需符号方面仍远落后于人类。本文提出一种名为SEA-net的解决方案，赋予神经网络符号创造、语义理解与通信的能力。SEA-net生成的符号能够动态配置网络以执行特定任务，这些符号通过捕获组合语义信息，使系统仅通过符号操作或通信即可获取新功能。此外，我们发现这些自生成符号呈现出类似自然语言的内在结构，表明人脑与人工神经网络在符号生成与理解层面存在共同框架。我们期待该研究能为未来构建融合联结主义与符号主义优势的更强大AI系统提供助力。