AI4R provides a lightweight, dependency-free Ruby playground for AI and machine learning researchers to explore and understand core algorithms. It offers clean, readable implementations of various AI techniques, enabling users to modify and experiment with them directly, fostering a deeper comprehension of how these models function.

How It Works

AI4R is structured into distinct toolkits, each containing Ruby implementations of fundamental AI algorithms. The project emphasizes transparency, avoiding "black boxes" and large dependencies. This design allows users to trace the execution of algorithms like Transformers, Classifiers, and Search Algorithms, facilitating hands-on learning and modification.

Quick Start & Requirements

• Install via RubyGems: gem install ai4r
• Requires Ruby 3.2 or later.
• Clone the repository for examples and benchmarks: git clone https://github.com/SergioFierens/ai4r && cd ai4r && bundle install
• Official quick-start examples are available within the repository's directories and linked in the README.

Highlighted Details

• Features a dependency-free Transformer implementation supporting encoder-only, decoder-only, and Seq2Seq modes.
• Includes a comprehensive suite of classifiers (e.g., Logistic Regression, SVM, Random Forest) and clusterers (e.g., KMeans, DBSCAN).
• Offers implementations for search algorithms (A*, MCTS), genetic algorithms, reinforcement learning (Q-Learning), HMMs, and Self-Organizing Maps.
• Provides benchmark runners for each algorithm family to facilitate performance comparisons.

Maintenance & Community

The project is maintained for personal enjoyment and community contribution. Feedback is welcomed via project comments.

Licensing & Compatibility

The library is explicitly stated as "unlicensed." This implies no formal license is attached, which may create ambiguity for commercial use or integration into closed-source projects.

Limitations & Caveats

The "unlicensed" status requires careful consideration for any form of distribution or commercial application. While the project aims for clarity, the lack of a formal license could be a significant adoption blocker for many use cases.