with symbolic AI (logic/reasoning) to create smarter, more reliable, and transparent systems.
It combines the data-driven perception of neural nets with the logical, rule-based reasoning of AI,
enabling machines to learn from data, explain their decisions, and understand context without hallucinations
Key Components and Benefits:
- Neural Networks (Perception): Processes unstructured data like images, text, and signals to identify patterns.
- Symbolic AI (Reasoning): Uses explicit rules, logic, and knowledge graphs to interpret data and make decisions.
- Key Benefits:
- Explainability: Provides transparent, auditable reasoning, rather than "black box" decisions.
- Robustness: Operates effectively on smaller datasets and handles edge cases better.
- Efficiency: Requires less data and energy compared to large, purely connectionist models.
