Subsymbolic AI

Subsymbolic AI focuses on artificial intelligence approaches that leverage connectionist models, like neural networks, where meaning emerges from the overall system rather than individual components. Current research emphasizes integrating subsymbolic methods with symbolic AI, which uses explicit representations of knowledge, aiming to combine the strengths of both paradigms—for example, by using symbolic reasoning to guide or interpret the outputs of neural networks. This integration is particularly relevant in areas like natural language processing, where the goal is to create explainable and robust models that overcome limitations of purely subsymbolic approaches, such as large language models. The resulting hybrid systems promise more reliable, interpretable, and powerful AI systems with broader applications.