NeuroSymbolic AI: A Scalable Framework for Cognitive-Inspired AI

NeuroSymbolic AI has emerged as powerful paradigm that integrates the pattern-recognition strengths of neural networking along with the logical reasoning provided by the Symbolic systems. By embedding symbolic rules, knowledge graphs, and logical constraints into neural learning pipelines, NeuroSymbolic AI enables system that can learn from data while simultaneously performing high-level reasoning and transparent decision-making, The analysis of these topics includes differentiable logic layers, neural theorem provers, and knowledge-enhanced transformers- and evaluates their performance across domains such as natural language understanding, visual question answering, and anomaly detection. The findings underline its potential as a foundational direction for developing next-generation AI systems capable of robust, interpretable, and cognitively inspired problem-solving.