AI systems that combine neural network learning with symbolic reasoning for more reliable, explainable intelligence.
Neurosymbolic AI (also written neuro-symbolic AI) is an approach to artificial intelligence that integrates neural network-based learning with symbolic reasoning systems. Neural networks excel at pattern recognition from raw data but struggle with logical consistency and explainability. Symbolic AI (including ontologies, knowledge graphs, and rule systems) excels at precise reasoning but struggles to learn from unstructured data. Neurosymbolic AI combines both, using neural networks for perception and pattern recognition while using symbolic systems for reasoning, constraint enforcement, and explanation.
Pure neural AI systems — large language models — have demonstrated fundamental limitations in logical consistency, factual accuracy, and verifiable reasoning. Neurosymbolic AI has emerged as the leading approach to building enterprise AI that is both capable and reliable. Systems like AlphaProof (mathematics), AlphaFold (protein structure), and enterprise AI agents grounded in knowledge graphs are all examples of neurosymbolic architectures achieving results that pure neural approaches cannot.
Neurosymbolic systems are implemented in several ways: LLM + Knowledge Graph (the LLM generates natural language, the knowledge graph provides factual grounding), Neural Theorem Provers (neural networks that learn to construct logical proofs), Differentiable Programming (embedding symbolic operations in differentiable computation graphs), and Semantic Parsing (converting natural language to formal logical representations). The key is that the symbolic component provides a verifiable, auditable reasoning layer.
A financial AI system uses a neurosymbolic architecture: a large language model interprets natural language queries and generates preliminary answers, while an OWL ontology of financial regulations and a knowledge graph of market entities verify that the answers are logically consistent with regulatory constraints. If the LLM suggests a trade that violates a compliance rule encoded in the ontology, the symbolic layer rejects it — providing both capability and compliance.