Neurosymbolic AI: Integrating Logic & Learning is an emerging field that combines the strengths of neural networks with symbolic reasoning to create more intelligent and interpretable AI systems. While neural networks excel at pattern recognition and learning from raw data, they often struggle with reasoning, generalization, and explainability. On the other hand, symbolic AI is based on rules and logic, making it better suited for structured reasoning and knowledge representation but less adaptable to noisy or unstructured data. By integrating these approaches, neurosymbolic AI aims to build systems that can both learn from data and reason about it logically. This fusion enables more robust and explainable AI applications in areas such as natural language understanding, robotics, and decision-making. As the demand for trustworthy and human-like AI grows, neurosymbolic methods are gaining attention for their potential to bridge the gap between perception and reasoning.
Relevant Conferences: International Conference on Machine Learning | Association for the Advancement of Artificial Intelligence | International Joint Conference on Artificial Intelligence | Conference on Computer Vision and Pattern Recognition | International Conference on Learning Representations | Annual Meeting of the Association for Computational Linguistics | European Conference on Machine Learning | International Conference on Robotics and Automation | Knowledge Discovery and Data Mining | Artificial Intelligence Congress | Artificial Intelligence Summit | Artificial Intelligence Events | Artificial Intelligence Meeting | World Congress on Artificial Intelligence | Global Artificial Intelligence Summit | Artificial Intelligence Symposium
Useful Links: Brochure Download | Abstract Submission | Register Now
Stay updated! Like, share & follow us for the latest news & insights: LinkedIn | Facebook | Twitter | Instagram | YouTube
