  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).
