Hybrid AI
Hybrid AI is an approach that combines symbolic AI (rule-based systems and knowledge graphs) with subsymbolic AI (machine learning and neural networks) to create more robust and interpretable artificial intelligence systems. It leverages the strengths of both paradigms—symbolic AI's logical reasoning and explicit knowledge representation, and subsymbolic AI's pattern recognition and learning from data—to overcome their individual limitations, such as the black-box nature of deep learning or the scalability issues of pure symbolic methods.
Developers should learn and use Hybrid AI when building applications that require both high accuracy from data-driven insights and transparent, explainable decision-making, such as in healthcare diagnostics, financial fraud detection, or autonomous systems where safety and interpretability are critical. It is particularly valuable in domains with limited data, as symbolic components can provide prior knowledge to guide learning, or in complex reasoning tasks where neural networks alone may struggle with logical consistency.