Hybrid AI refers to the integration of multiple AI methodologies to create systems that leverage the strengths of various approaches. This combination allows for more robust and effective solutions to complex problems. In natural language processing (NLP), Hybrid AI workflows often blend symbolic AI techniques (such as rule-based systems or knowledge graphs) with machine learning methods (like deep learning or statistical models).
By using both symbolic reasoning, which excels at handling structured knowledge and logic, and machine learning, which is adept at recognizing patterns in large datasets, Hybrid AI systems can achieve better accuracy, scalability, and interpretability. This approach is particularly useful in scenarios where purely machine-learning-based or rule-based solutions fall short, such as in legal document analysis, healthcare applications, or customer service automation.