Hybrid AI
0
Creates a transparent, inspectable decision layer using McCulloch-Pitts neurons to bridge the 'black box' problem of neural networks for safety-critical and regulated AI.
Acerca de
This server implements the 'post-NN calculator' pattern, offering a solution to the opacity of modern neural networks. It enables complex perception via traditional NNs while ensuring final decisions are made by transparent, auditable McCulloch-Pitts neurons. This hybrid approach allows applications to leverage the power of neural networks for pattern recognition and feature extraction, then enforce business rules, safety constraints, and regulatory compliance through a fully explainable logical layer, making AI decisions understandable, trustworthy, and debuggable.
Características Principales
- Transparent MCP Neuron Engine for explainable decisions
- Compose complex logic using standard logic gates (AND, OR, NOT, XOR, etc.)
- Create and apply named decision rules with human-readable descriptions
- Seamless 'post-NN decision' integration for hybrid AI workflows
- Comprehensive decision logging and audit trails with full explanations
- 0 GitHub stars
Casos de Uso
- Enforcing safety protocols in self-driving cars with explainable decisions
- Automating loan approval processes with clear, compliant reasoning
- Supporting medical diagnosis by flagging cases for review with transparent indicators
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