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.

通过提出 SEA-net 方案，使神经网络拥有符号创造、语义理解和通信的能力，并通过符号操作和通信获得新的功能。这些和人脑中的符号生成和理解有着共通的框架，有望在未来生产更加强大的人工智能系统。