Neuro-Symbolic AI
Approaches that combine neural networks (pattern recognition, learning from data) with symbolic AI (logical reasoning, knowledge representation) to get the strengths of both.
Why It Matters
Neuro-symbolic AI addresses deep learning's weaknesses in reasoning and interpretability by combining it with symbolic logic — potentially the path to more robust AI.
Example
A system that uses a neural network to perceive objects in an image and symbolic reasoning to answer questions like 'Is the red ball to the left of the blue box?'
Think of it like...
Like combining instinct (neural) with logic (symbolic) — humans use both intuition and deliberate reasoning, and AI may need both too.
Related Terms
Symbolic AI
An approach to AI that represents knowledge using symbols and rules, and reasons by manipulating those symbols logically. Symbolic AI dominated before the deep learning era.
Neural Network
A computing system inspired by the biological neural networks in the human brain. It consists of interconnected nodes (neurons) organized in layers that process information and learn to recognize patterns.
Reasoning
An AI model's ability to think logically, make inferences, draw conclusions, and solve problems that require multi-step thought. Reasoning goes beyond pattern matching to genuine logical analysis.
Knowledge Graph
A structured representation of real-world entities and the relationships between them, stored as a network of nodes (entities) and edges (relationships). Knowledge graphs capture factual information in a machine-readable format.