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discussion 2024-06-21

Summary​

Shaw cleaned up their workspace, addressing issues with the rope working algorithm which converged slower than expected compared to sinusoidal methods but decided to give it a chance. They planned to develop a wfc generator, hypothesizing that AI could deduce rule sets from patterns. Shaw also began generating synthetic data and considered using cropped black edges in training sets for efficiency. Despite challenges with the difficulty of generated problems, Shaw proposed simplifying them by removing padding tokens and creating an elementary-level training set to pretrain foundational concepts.

FAQ​

  • What is the purpose of Shaw's synthetic data generation?

  • Shaw: The goal of generating synthetic data is likely to create a diverse set of training examples for machine learning models, particularly in the context of challenges or pattern recognition tasks. By using synthetic data, Shaw can control various aspects and complexities within the dataset, which may help improve model performance on real-world data by exposing it to a wider range of scenarios during training.

  • How does Shaw plan to address the slow convergence issue with sinusoidal embeddings?

    • Shaw: Shaw is considering using synthetic program generators and downloading neoneyes, which might help in generating more challenges or improving the quality of data used for training models. By doing so, they hope to enhance the model's ability to converge faster on sinusoidal embeddings by providing it with a better understanding of patterns within the dataset.

  • What is Shaw's approach to making challenge problems more accessible?

    • Shaw: To make challenge problems more accessible and easier for models to learn, Shaw suggests creating simpler training sets that resemble concepts learned at an early age (e.g., grade 1 level). They also propose deleting padding tokens from the data as a potential solution. This approach is based on the idea of building simple concepts first before moving onto more complex ones, similar to how one would learn math starting with basic arithmetic and gradually progressing towards calculus.

Who Helped Who​

  • Shaw helped with generating synthetic data by creating a program to generate challenges.
  • Shaw assisted in discussing potential improvements for training AI, such as simplifying concepts and removing padding tokens from datasets.

Action Items​

  • Technical Tasks

  • Generating synthetic data (mentioned by Shaw)

  • Working on WFC generator (mentioned by Shaw)

  • Exploring sinusoidal embeddings and their convergence rates (mentioned by Shaw)

  • Creating a simple training set for pre-training AI models (suggested by Shaw)

  • Documentation Needs

    • No specific documentation needs were explicitly requested.

  • Feature Requests

    • Cropping black edges from the training set where no information is present (requested by Shaw)

  • Community Tasks

    • Generating and sharing challenge problems for community engagement (mentioned by Shaw)