Hybrid Inference is an AI approach that combines different inference methods, models, or computing resources to produce more effective results. It may combine machine learning predictions with rule-based reasoning, specialized algorithms, or human-defined logic. By selecting or combining suitable methods for a particular task, the system can process information efficiently while improving accuracy, flexibility, and reliability.
Hybrid Inference is commonly used in healthcare, cybersecurity, robotics, finance, industrial automation, and intelligent decision-support systems. Its purpose is to handle complex problems where a single inference method may not be sufficient. For example, a machine learning model can recognize patterns in data while a rule-based component applies specific conditions or constraints. This combination can improve decision quality, provide greater control, and support more dependable AI-driven applications.
