Implement Current Priorities and Planned Improvements

[mentatbot]

Description

The following priorities and planned improvements mentioned in the README need to be implemented in the codebase:

1. Enhancement of Reasoning and Concept Understanding:

   • Implement advanced natural language processing techniques for better comprehension of mathematical problems.
   • Develop a more robust symbolic reasoning module to handle abstract mathematical concepts.

2. Optimization of Data Loading and Problem Generation:

   • Implement a DynamicProblemGenerator to generate problems on-demand, improving memory efficiency.
   • Create create_dynamic_dataset to integrate dynamic problem generation with tf.data.
   • Modify smooth_curriculum_learning to use dynamic datasets, allowing real-time adjustments of difficulty.

3. Improvement of Memory Usage (High Priority):

   • Optimize the external memory mechanism for more efficient use of computational resources.
   • Implement pruning and quantization techniques to reduce model size without significantly sacrificing performance.
   • Develop a more sophisticated memory management system to handle complex mathematical concepts efficiently.

4. Training Methodology Enhancement (High Priority):

   • Implement advanced curriculum learning strategies with dynamic difficulty adjustment.
   • Develop a hybrid training approach combining supervised learning with reinforcement learning for problem-solving strategies.
   • Introduce meta-learning techniques to improve the model's ability to learn new mathematical concepts quickly.

5. Model Usage on CPU and GPU:

   • Ensure the model fully utilizes system resources, whether on CPU or GPU.
   • Optimize the implementation to take full advantage of the GPU's capabilities.
   • Ensure compatibility between CPU and GPU.

6. Code Modularization and Maintenance Improvement (High Priority):

   • Refactor the codebase into smaller, more manageable components.
   • Create separate modules for problem generation, model architecture, training loops, and evaluation metrics.
   • Implement a plugin architecture to allow easy addition of new mathematical concepts and problem types.

7. Expansion to Visual Tasks:

   • Implement a Convolutional Neural Network (CNN) for processing mathematical image tasks.
   • Develop methods to extract and analyze activations from intermediate CNN layers.
   • Create a sparse autoencoder to decompose activations and identify visual patterns in mathematical notations.

8. Advanced Pattern Recognition:

   • Implement visual attention techniques to identify key elements in visually presented mathematical problems.
   • Develop a mathematical symbol recognition system to interpret handwritten equations.

9. Model Behavior Manipulation:

   • Experiment with artificial modification of activations to alter model behavior in problem-solving.
   • Develop methods to control model perception by manipulating specific components.

10. Enhanced Visualization:

    • Create advanced techniques to visualize learned concepts across different mathematical domains.
    • Implement tools for visualizing "polysemantic neurons" in mathematical contexts.

11. Interpretability Enhancements:

    • Develop interpretable regularization techniques.
    • Implement mechanisms to track neuron evolution during training.
    • Create tools for gradient analysis to better understand feature importance in problem-solving.

12. Robustness Testing:

    • Develop a suite of tests to evaluate model robustness against various types of manipulations.

Tasks

1. Enhancement of Reasoning and Concept Understanding:

   • Implement advanced NLP techniques.
   • Develop a robust symbolic reasoning module.

2. Optimization of Data Loading and Problem Generation:

   • Implement DynamicProblemGenerator.
   • Create create_dynamic_dataset.
   • Modify smooth_curriculum_learning to use dynamic datasets.

3. Improvement of Memory Usage:

   • Optimize external memory mechanism.
   • Implement pruning and quantization techniques.
   • Develop a sophisticated memory management system.

4. Training Methodology Enhancement:

   • Implement advanced curriculum learning strategies.
   • Develop a hybrid training approach.
   • Introduce meta-learning techniques.

5. Model Usage on CPU and GPU:

   • Ensure full utilization of system resources.
   • Optimize for GPU capabilities.
   • Ensure compatibility between CPU and GPU.

6. Code Modularization and Maintenance Improvement:

   • Refactor codebase into smaller components.
   • Create separate modules for different functionalities.
   • Implement a plugin architecture.

7. Expansion to Visual Tasks:

   • Implement a CNN for mathematical image tasks.
   • Develop methods to analyze activations from CNN layers.
   • Create a sparse autoencoder for visual patterns.

8. Advanced Pattern Recognition:

   • Implement visual attention techniques.
   • Develop a mathematical symbol recognition system.

9. Model Behavior Manipulation:

   • Experiment with artificial modification of activations.
   • Develop methods to control model perception.

10. Enhanced Visualization:

    • Create techniques to visualize learned concepts.
    • Implement tools for visualizing "polysemantic neurons".

11. Interpretability Enhancements:

    • Develop interpretable regularization techniques.
    • Implement mechanisms to track neuron evolution.
    • Create tools for gradient analysis.

12. Robustness Testing:

    • Develop tests to evaluate model robustness.

References

• Kistmath_AI/models/kistmat_ai.py
• Kistmath_AI/models/external_memory.py
• Kistmath_AI/training/curriculum_learning.py

Additional Notes

Please ensure that the new features and improvements are well-documented and include appropriate unit tests to verify their functionality.