Neural symbolic processing is a hybrid approach that combines neural networks with symbolic AI methods.
Neural networks are well suited for learning from large amounts of data but often show difficulties in explanation and dealing with abstract concepts.
Symbolic systems, on the other hand, use explicit knowledge representations and logical rules but are often less flexible and require manual input.
Neural symbolic processing combines neural models with symbolic modules to leverage the advantages of both worlds.
This can be done through methods such as neural programming, differentiable logic, or symbolic reasoning components.
The goal is to create systems that can learn, explain, and solve complex tasks better.