Some of the current problems related to a lack of trust in AI systems are a direct result of the massive use of black-box methods that depend solely on the processing of data. Instead, the new AI generation has its foundation built on hybrid AI systems (also known as neuro-symbolic or neuro-explicit). These hybrids do not rely solely on data-driven approaches but on the full range of AI technologies (“All of AI”), which includes symbolic AI methods for search, reasoning, planning, acting and other operations.

Neuro-Explicit Modelling is achieved through the combination of Machine Learning with symbolic conclusions and the explicit representation of knowledge in hybrid AI systems. Knowledge no longer needs to be machine learned when it is represented by semantic and other explicit models, which can also guide the learning process in a direction that improves generalisation, robustness, and interpretability. This hybrid approach is also known as the third wave of AI (Garcez, Lamb, and Gabbay 2009; Garcez and Lamb 2023). The requirements are particularly strict when it comes to applications with significant physical, economic, or social risk. The AI systems used in such applications are required – for example by the European AI Act – to have been validated and certified.

Contact: Timo Gros, Nicola Müller, André Meyer-Vitali