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Attend a comprehensive workshop exploring the intersection of causal reasoning and neuro-symbolic artificial intelligence, designed to address the growing need for interpretable and robust AI systems in critical decision-making processes. Discover how this emerging field bridges data-driven learning with symbolic reasoning to create systems capable of understanding causality beyond mere correlation. Learn about three key objectives: enriching neuro-symbolic AI systems with explicit causal representations, integrating causal knowledge with domain knowledge, and enabling neuro-symbolic AI techniques for causal AI tasks. Explore how explicit causal representation provides insights that predictive models may miss from observational data and assists in decision-making scenarios requiring understanding of cause-and-effect relationships. Gain insights from two keynote presentations: Dr. Kristian Kersting's "Reasonable AI: Bridging 'Intuition' and (Causal) Reasoning" and Dr. Kun Zhang's "Causal Representation Learning and Generative AI." Engage with researchers and practitioners from academia and industry as they share experiences and insights on applying causality and neuro-symbolic AI techniques to real-world problems, representing the convergence of causal reasoning, neuro-symbolic approaches, and artificial intelligence.
