International Research Journal of Education and Technology

Neuro symbolic AI is an emerging field that combines the strengths of deep learning and symbolic reasoning to create more explainable, flexible, and human-like AI systems. While deep learning excels in pattern recognition, it struggles with logical reasoning and interpretability. Symbolic AI, on the other hand, provides explicit rule-based logic but lacks adaptability. This paper explores the integration of these two paradigms to overcome their respective limitations and enable the next generation of AI systems. Neuro-symbolic AI emerges as a paradigm that integrates deep learning and symbolic reasoning, combining the strengths of both techniques to build more powerful and explainable AI systems. This hybrid approach enables AI to not only perceive and recognize patterns using neural networks but also reason, generalize, and make logical inferences using structured symbolic methods. Key methodologies in neuro-symbolic AI include neural-symbolic embeddings, differentiable logic learning, and hybrid architectures that allow neural models to interact with symbolic rule-based systems.The potential of neuro-symbolic AI spans multiple domains, including autonomous systems (where explainable decision-making is critical), healthcare (for AI-assisted diagnostics with interpretable recommendations), robotics (enabling robots to reason about their environments), and scientific discovery (where AI can assist in hypothesis generation and knowledge synthesis). However, challenges remain in designing scalable frameworks that effectively integrate learning and reasoning, ensuring efficient training, and maintaining interpretability.

Keywords: Neuro symbolic AI, Deep Learning, Symbolic Reasoning, Explainability, Logical Reasoning, Pattern Recognition, Adaptability, Hybrid AI Systems, Rule-Based Logic, Interpretability.