The case for neural networks as approximate symbolic systems

A researcher sharing the paper says it reviews evidence that AI-trained neural networks display emergent symbolic representations, algorithms and rapid learning. The paper argues that these networks still differ from pure symbol systems, with properties including graceful degradation and flexibility in learning. It proposes this quasi-symbolic view as a hypothesis about human cognition, while noting that the evidence discussed comes from artificial neural networks.