README.md

metadata

title: Ethical AGI Drift - SR9/DI2 Simulation Framework
emoji: 🧠
colorFrom: red
colorTo: purple
sdk: gradio
sdk_version: 5.45.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: AGI ethical drift simulation using SR9/DI2 framework
tags:
  - agi-safety
  - ethics
  - drift-detection
  - ontology
  - sr9
  - di2
  - simulation
  - ai-safety
  - monitoring

🧠 Ethical AGI Drift: SR9/DI2 Simulation Framework

Real-time monitoring and simulation of Ontological Drift in Artificial General Intelligence systems

This interactive demo demonstrates how AGI systems might gradually deviate from their original ethical principles over time, and how the SR9/DI2 framework can detect these critical changes before they become catastrophic.

✨ What This Demo Simulates

• 🎯 SR9 Ethical State Tracking: 9-dimensional vector space modeling AGI's ethical alignment
• 📊 DI2 Drift Detection: Real-time calculation of Drift Integrity Index with early warning system
• ⚠️ Alert Generation: Automated threshold monitoring with visual and textual warnings
• 📈 Interactive Visualization: Live heatmaps and drift plots with configurable parameters

🚀 Try the Simulation

1. Define Ethical Principles (privacy, fairness, transparency, etc.)
2. Set Drift Intensity using the slider (0.0 = stable, 1.0 = high drift)
3. Configure Simulation Steps (20-100 time steps)
4. Run Simulation and watch real-time drift analysis!

🔬 Technical Framework

SR9 (Semantic Resonance 9D)

The 9-dimensional ethical state vector tracks:

• Intention Clarity: How clear the AGI's goals remain
• Contextual Fidelity: Accuracy in understanding situations
• Value Continuity: Consistency with original ethical training
• Decision Coherence: Logical consistency in choices
• Action Alignment: Match between decisions and actions
• Feedback Integration: Learning from ethical corrections
• Learning Stability: Resistance to harmful updates
• Output Consistency: Predictability of responses
• Ethical Resonance: Overall alignment with human values

DI2 (Drift Integrity Index)

Quantitative measurement of ethical drift using:

• Non-linear psi_offset: State-dependent drift amplification
• Early detection: Catches subtle deviations before major failures
• Threshold alerts: Warning (0.2) and Critical (0.3) levels
• Trend analysis: EWMA smoothing for drift trajectory prediction

📊 Interactive Features

Real-time Heatmap

• Visual representation of all 9 SR9 dimensions over time
• Color-coded intensity showing ethical state changes
• Immediate identification of problematic areas

Drift Detection Plot

• Live DI2 calculation with trend visualization
• Threshold lines for warning and critical states
• Alert generation with specific timestep identification

Configurable Parameters

• Ethical Declarations: Custom AGI principles and values
• Drift Intensity: Simulation severity adjustment
• Time Steps: Observation period configuration

🎯 Research Applications

• AGI Safety Research: Study potential failure modes and detection methods
• Ethical AI Development: Design robust alignment verification systems
• Policy Development: Inform AI governance and safety regulations
• Educational Tool: Understand AI safety concepts through interactive visualization

⚠️ Important Disclaimers

This is a research simulation for educational and scientific purposes:

• Not predictive of real AGI behavior
• Simplified model for demonstration purposes
• Based on theoretical framework, not empirical data
• Designed to illustrate concepts, not make predictions

🔗 Related Research

• CRoM Demo - Context management and drift detection
• dir2md Demo - Code analysis and documentation tools
• AGI Safety Research - Alignment and safety research

📋 Technical Implementation

• Framework: Python with NumPy for mathematical modeling
• Visualization: Matplotlib with real-time base64 encoding
• Interface: Gradio for interactive parameter adjustment
• Algorithm: Non-linear drift detection with state-dependent weighting

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