Explore the integration of Machine Learning and Symbolic Reasoning in AI applications through this insightful conference talk by Nicole Prentzas and Mario Fusco. Discover how combining these complementary AI branches can lead to more transparent and explainable decision-making processes. Learn about the limitations of relying solely on Machine Learning and the benefits of incorporating Symbolic Artificial Intelligence for encoding domain-specific knowledge. Gain practical insights into implementing this hybrid approach using Quarkus with its langchain4j and drools extensions. Understand the importance of explainable AI in critical applications, such as mortgage approvals, where transparency is crucial. Benefit from the expertise of Nicole Prentzas, a senior software engineer and PhD candidate in Explainable AI, and Mario Fusco, a senior principal software engineer and Drools project lead. Delve into real-world examples demonstrating the advantages of this architectural choice in common scenarios. Read more