Hybrid AI

1. Hybrid Artificial Intelligence (AI) combines different methods of Artificial Intelligence or different representations of knowledge in order to leverage the strengths of each method and compensate for their weaknesses. 
   In medicine, such a learning system can combine clinical data with guideline knowledge to better classify diagnoses or therapy recommendations. In the legal field, model-based proposals can be reviewed using complex sets of rules or contract logic – always with the aim of combining contextual knowledge and data-based analysis.
2. Hybrid AI makes systems more robust and reliable. They are less susceptible to bias and require smaller amounts of data. For companies, this means more informed decisions, lower data annotation costs, and greater traceability.
3. Hybrid AI is the foundation for agentic AI. Because the technology combines Machine Learning with knowledge, logic, and contextual understanding, AI agents can make better decisions.

Hybrid AI demonstrates its strengths wherever decisions must be made based on both data and knowledge. Especially in regulated and safety-critical areas – such as the financial and legal sectors, healthcare, or public sectors – it is key to making complex processes reliable, traceable, and efficient.

By combining learning and rule-based methods, hybrid AI can integrate expertise, guidelines, or process logic directly into AI systems. This enables applications that not only recognize patterns but also understand their meaning and act in a context-appropriate manner – for example, when checking regulatory requirements, automatically handling sensitive data, or performing quality assurance in industrial processes.

At Fraunhofer IAIS, we conduct research on hybrid AI systems consisting of various components: machine learning from symbolic methods to generative AI, AI-based optimization and forecasting, knowledge and logic, and quantum machine learning. This allows us to process even the largest amounts of structured and unstructured data. In addition to conventional centralized learning, we also rely on distributed learning, which meets particularly high data protection requirements. We also develop edge ML models that work on devices without internet access. We use MLOps methods to better integrate software and model development. With agentic AI technologies, we develop intelligent agents that use selected AI models, knowledge from structured data, external information sources, and tools as needed to solve complex tasks.