Neurosymbolic AI is a hybrid approach aiming to bridge the gap between neural networks' ability to learn patterns and symbolic AI's capacity for logical reasoning and explainability.

This approach may offer the best of both worlds combining robust learning from data and clear with understandable reasoning based on knowledge. It has the potential to outperform systems relying solely on either neural networks or symbolic logic and to provide clear explanations for its decisions.

The approach involves encoding structured symbolic knowledge into a format that can be integrated with neural networks and then mapping information from neural patterns back to structured symbolic representations.