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：在人类智力中，创造有意义的符号并熟练使用它们进行更高级的认知功能如交流、推理、规划等非常重要和独特。目前的深度神经网络离人类创造这种高级符号的能力还很遥远。在这里，我们提出了一种名为SEA-net的解决方案，可赋予神经网络创造符号、语义理解和交流的能力。SEA-net生成动态配置网络执行特定任务所需的符号。这些符号捕捉了组合语义信息，使得系统通过符号操作或交流就可以获得新的功能。此外，我们发现这些自动生成的符号具有类似于自然语言的内在结构，表明人类大脑和人工神经网络在符号的生成和理解方面有类似的框架。希望在未来能够产生更能够协同使用连接主义和符号方法进行人工智能的更强大的系统。