AI Dual-Network Framework (Gut-Brain Architecture)

Overview

The AI Dual-Network Framework implements a novel approach to AI response generation by simulating both emotional ("gut") and analytical ("brain") processing pathways. This architecture enables more nuanced, context-aware, and emotionally intelligent responses while maintaining analytical rigor.

Implementation Files

• dual-network.py: A simple implementation of the dual-network architecture demonstrating the core concepts.
• llm_dual_network.py: An advanced implementation that leverages LLM agents for enhanced processing capabilities.
• app.py: A Streamlit application (work in progress) that demonstrates the concepts of the gut-brain network

System Architecture

Dual Processing Networks

Gut Network

• Handles immediate emotional processing
• Performs pattern matching
• Processes emotional valence
• Manages stress levels
• Provides intuitive responses

Brain Network

• Conducts analytical processing
• Performs context analysis
• Evaluates logical implications
• Plans structured responses
• Validates gut reactions

Integration Layer

Combines outputs from both networks to generate balanced, appropriate responses that consider both emotional and analytical aspects.

Key Features

Emotional Processing

• Real-time emotion detection
• Confidence scoring
• Stress level monitoring
• Empathy level assessment
• Emotional stability tracking

Analytical Processing

• Context analysis
• Response planning
• Strategy generation
• Logical validation
• Performance optimization

Metadata Tracking

{
  "confidence": 0.57,
  "emotional_influence": {
    "valence": 0.9,
    "arousal": 0.8,
    "dominance": 0.7
  },
  "stress_level": 0.43,
  "emotional_awareness": {
    "detected_emotion": "excited",
    "empathy_level": 0.9
  }
}

Use Cases

1. Code Review and Security Analysis

• Gut Network: Pattern matching, quick detection
• Brain Network: Detailed analysis, security implications
• Integration: Prioritized recommendations

2. Content Moderation

• Gut Network: Immediate toxicity detection
• Brain Network: Context analysis, policy compliance
• Integration: Moderation decisions

3. Real-time Data Analysis

• Gut Network: Anomaly detection
• Brain Network: Root cause analysis
• Integration: Action recommendations

4. Creative Tasks

• Gut Network: Style matching, aesthetic evaluation
• Brain Network: Structure analysis, goal alignment
• Integration: Creative solutions

5. Technical Support

• Gut Network: Quick problem recognition
• Brain Network: Root cause analysis
• Integration: Solution planning

6. Risk Assessment

• Gut Network: Immediate risk detection
• Brain Network: Detailed analysis
• Integration: Mitigation strategies

7. Learning Assistance

• Gut Network: Learning style recognition
• Brain Network: Knowledge structure analysis
• Integration: Personalized guidance

8. Decision Support

• Gut Network: Quick option evaluation
• Brain Network: Detailed impact assessment
• Integration: Balanced recommendations

Example Implementation

class GutNetwork:
    def __init__(self, state_size: int = 5):
        self.state_size = state_size
        self.internal_state = np.zeros(state_size)
        self.homeostasis_target = np.ones(state_size) * 0.5
        self.stress_level = 0.0
        
    def process_input(self, input_signal):
        # Process emotional aspects
        response = self.generate_emotional_response(input_signal)
        return response, self.stress_level

class BrainNetwork:
    def __init__(self, input_size: int = 5):
        self.input_size = input_size
        self.memory = []
        
    def process_input(self, input_signal, gut_signal):
        # Process analytical aspects
        response = self.analyze_and_plan(input_signal, gut_signal)
        return response

class DualNetwork:
    def __init__(self):
        self.gut = GutNetwork()
        self.brain = BrainNetwork()
        
    def process(self, input_signal):
        # Integrate both networks
        gut_response, stress = self.gut.process_input(input_signal)
        final_response = self.brain.process_input(input_signal, gut_response)
        return final_response

Visualization

Response Examples

Example 1: Excitement Processing

Input: "I'm really excited about this new project!"

Response: "That's fantastic to hear! Starting a new project is always exciting..."

Metadata:
{
  "confidence": 0.57,
  "stress_level": 0.43,
  "detected_emotion": "excited",
  "empathy_level": 0.9
}

Example 2: Concern Processing

Input: "I'm worried about the deadline..."

Response: "It's understandable to feel worried about deadlines..."

Metadata:
{
  "confidence": 0.61,
  "stress_level": 0.39,
  "detected_emotion": "worry",
  "empathy_level": 0.85
}

Best Practices

1. Regular calibration of emotional sensitivity
2. Balanced weighting between networks
3. Context-appropriate response selection
4. Continuous monitoring of stress levels
5. Regular validation of analytical processes

Implementation Guidelines

1. Start with basic emotional processing
2. Add analytical capabilities incrementally
3. Calibrate integration layer carefully
4. Monitor system stress levels
5. Implement appropriate fallbacks

Contributing

We welcome contributions to improve the framework. Please:

1. Follow coding standards
2. Add tests for new features
3. Document changes thoroughly
4. Submit detailed pull requests

License

MIT License - See LICENSE file for details

Contact

For questions or support, please open an issue in the repository.