Neuro Symbolic Architecture

Neuro-symbolic architectures aim to combine the strengths of neural networks (e.g., learning from data) and symbolic AI (e.g., reasoning with knowledge) to build more robust and explainable AI systems. Current research focuses on integrating symbolic knowledge into various neural network architectures, including transformers and energy-based models, often employing techniques like vector symbolic architectures and concept-based approaches for improved performance and interpretability in tasks such as human activity recognition, robot navigation, and visual concept learning. This hybrid approach holds significant promise for advancing AI capabilities in areas requiring both data-driven learning and logical reasoning, leading to more reliable and understandable AI systems across diverse applications.