Hybrid AI Paradigms
Neuro-symbolic AI unifies the strengths of Connectionism (gradient-based learning) and Symbolism (logic and rules). This convergence addresses the limitations of modern Large Language Models, particularly regarding grounded reasoning and consistency.
Core Hybrid Approaches
- Symbolic Front-ends: Utilizing CNLs to translate human intent into logic gates, which are kemudian parsed by neural architectures.
- Differentiable Logic: Frameworks that enable backpropagation through logical operators, such as Logical Neural Networks (LNN).
- High-Dimensional Symbols: Utilizing VSA/HDC to perform discrete symbolic manipulations within continuous vector spaces.
Theoretical Objective
The integration of symbolic constraints aims to produce agents capable of reasoning about Consistency, Causality, and Proof. By grounding neural outputs in formal logic, the architecture enables verifiable decision-making processes.