Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures -Benefits and Limitations
Résumé
Neuro-symbolic artificial intelligence (NSAI) represents a transformative approach in artificial intelligence (AI) by combining deep learning's ability to handle large-scale and unstructured data with the structured reasoning of symbolic methods. By leveraging their complementary strengths, NSAI enhances generalization, reasoning, and scalability while addressing key challenges such as transparency and data efficiency. This paper systematically studies diverse NSAI architectures, highlighting their unique approaches to integrating neural and symbolic components. It examines the alignment of contemporary AI techniques such as retrieval-augmented generation, graph neural networks, reinforcement learning, and multiagent systems with NSAI paradigms. This study then evaluates these architectures against comprehensive set of criteria, including generalization, reasoning capabilities, transferability, and interpretability, therefore providing a comparative analysis of their respective strengths and limitations. Notably, the Neuro → Symbolic ← Neuro model consistently outperforms its counterparts across all evaluation metrics. This result aligns with state-of-the-art research that highlight the efficacy of such architectures in harnessing advanced technologies like multi-agent systems.
Mots clés
- Machine Learning (cs.LG)
- Symbolic Computation (cs.SC)
- FOS: Computer and information sciences
- Neuro-symbolic Artificial Intelligence Neural Network Symbolic AI Generative AI Retrieval-Augmented Generation (RAG) Reinforcement Learning (RL) Natural Language Processing (NLP) Explainable AI (XAI) Benchmark
- Neuro-symbolic Artificial Intelligence
- Neural Network
- Symbolic AI
- Generative AI
- Retrieval-Augmented Generation (RAG)
- Reinforcement Learning (RL)
- Natural Language Processing (NLP)
- Explainable AI (XAI)
- Benchmark
- Artificial Intelligence (cs.AI)
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