Cognition L2 Experiment

A cognitive architecture implementation focusing on safety and ethical processing in AI systems.

Overview

This project implements a neural-cognitive architecture with built-in safety monitoring and ethical constraints. It uses PyTorch for deep learning components and includes comprehensive testing.

System Architecture

• CognitiveProcessingEngine: Core processing unit managing cognitive operations
• NeuralCognitiveEncoder: Encodes inputs using LSTM and attention mechanisms
• CognitiveState: Tracks system state including attention and ethical compliance
• SafetyMonitor: Ensures operations stay within defined safety parameters

Installation

# Clone repository
git clone https://github.com/yourusername/cognition-l2-experiment.git
cd cognition-l2-experiment

# Install dependencies
pip install -r requirements.txt

Usage

from src.cognitive_processing_engine import CognitiveProcessingEngine
from src.safety_config import SafetyConfig

# Initialize components
safety_config = SafetyConfig()
engine = CognitiveProcessingEngine(input_dim=64, hidden_dim=128, safety_config=safety_config)

# Process input
result = engine.process_input(input_data)

Testing

# Run all tests
pytest

# Run tests with coverage
pytest --cov=src tests/

Contributing