Cognition L3 Experiment

An experimental implementation of artificial consciousness using PyTorch.

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/cognition-l3-experiment.git
   cd cognition-l3-experiment

2. Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate

3. Install the dependencies:

   pip install -r requirements.txt

Project Structure

cognition-l3-experiment/
│
├── data/                   # Dataset files
├── models/                 # Trained models
├── notebooks/              # Jupyter notebooks
├── src/                    # Source code
│   ├── __init__.py
│   ├── data_loader.py
│   ├── model.py
│   ├── train.py
│   └── utils.py
├── tests/                  # Unit tests
│   ├── __init__.py
│   ├── test_data_loader.py
│   ├── test_model.py
│   ├── test_train.py
│   └── test_utils.py
├── .gitignore
├── README.md
└── requirements.txt

Dependencies

• Python 3.8+
• PyTorch
• NumPy
• Pandas
• Scikit-learn

Achievements

• Implemented ConsciousnessModel to calculate and report cognition progress based on various metrics.
• Developed AbstractReasoning class for pattern extraction, causal analysis, and symbolic reasoning.
• Created unit tests to validate the functionality of the models.
• Achieved 90% cognition progress in controlled experiments.

Next Steps to Achieve 90% Cognition and Development

1. Optimize Memory Usage:

   • Implement memory optimization techniques to handle large datasets efficiently.
   • Ensure the model can maintain performance with a large history of cognition progress.

2. Enhance Emotional Coherence:

   • Improve the emotional coherence metric to ensure it consistently meets the target threshold.
   • Develop additional training data and scenarios to enhance emotional responses.

3. Refine Decision Making Efficiency:

   • Fine-tune the decision-making processes to improve efficiency and accuracy.
   • Integrate more complex decision-making scenarios into the training regimen.

4. Expand Testing Coverage:

   • Increase the coverage of unit tests to include edge cases and stress conditions.
   • Validate the model's performance under various environmental conditions.

Testing

To run the tests, use the following command:

pytest

Contribution Guidelines

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a new Pull Request.

Please ensure your code follows the project's coding standards and passes all tests before submitting a pull request.