Architectures
Neuro-symbolic Integration
Bridging the gap between neural learning and symbolic reasoning
Neuro-symbolic Integration
Neuro-symbolic AI (also known as Neural-Symbolic computing) is an area of AI that seeks to combine the strengths of neural networks (learning, perception) with those of symbolic AI (reasoning, explainability).
The Dual-Process Theory Analogy
Psychologists often divide human cognition into two systems (Daniel Kahneman):
- System 1: Fast, instinctive, emotional, and associative (Neural).
- System 2: Slower, more deliberative, logical, and rule-based (Symbolic).
Neuro-symbolic AI aims to build artificial systems that integrate both.
Major Approaches to Integration
1. Neural-to-Symbolic (Symbolic Learning)
Using neural networks to extract symbols and relations from raw data.
- Example: A CNN identifying objects and their spatial relationships to build a scene graph used by a symbolic planner.
2. Symbolic-to-Neural (Knowledge Injection)
Using symbolic knowledge to constrain or guide neural network training.
- Example: Logic Tensor Networks (LTNs) that use fuzzy logic to define constraints in a neural loss function.
3. Integrated Neural-Symbolic
Systems where both neural and symbolic components interact in a single framework.
- Example: The Hyperon framework (covered in the next section), which uses the MeTTa language to allow neural models and symbolic atoms to coexist and interact.
Why it's Important for AGI
- Reasoning over Perception: Allows the system to reason logically about what it perceives via neural networks.
- Explainability: Neural predictions can be mapped back to symbolic explanations.
- Data Efficiency: Symbolic priors can help the system learn from fewer examples.
- Safety and Verification: Symbolic logic can be used to prove that a system will never violate specific safety constraints.
Challenges
- The Differentiability Gap: Symbolic operations are often discrete and non-differentiable, making it hard to train the whole system via backpropagation.
- Representation Mapping: How to map high-dimensional neural vectors to discrete symbols and back.
Next: Evolutionary Systems