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将其用于交流、推理、规划等高级认知功能，是人类智能所特有且至关重要的能力。当前深度神经网络在创造此类符号用于高级认知功能方面仍远不及人类。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、语义理解及交流的能力。SEA-net生成的符号可动态配置网络以执行特定任务，这些符号捕获了组合语义信息，使系统能够仅通过符号操作或交流获取新功能。此外，我们发现这些自生成符号展现出类似自然语言的内在结构，暗示人类大脑与人工神经网络在符号生成与理解层面共享同一基础框架。我们期望该工作能推动未来构建融合连接主义与符号主义优势的更强大AI系统。