The ALMA Project has released the Open-AML Engine—an open-source implementation of Algebraic Machine Learning (AML), now available on GitHub. This release marks a key step toward transparent, symbolic AI that aligns with ALMA’s vision for trustworthy and human-centric machine learning. The engine supports reproducible research, modular experimentation, and symbolic reasoning, and comes with complete documentation, including API reference, tutorials, and examples to get started.
The ALMA Project is pleased to announce the release of the Open-AML Engine, now publicly available as an open-source repository on GitHub.
This release marks a key milestone in ALMA’s mission to develop trustworthy, transparent, and human-aligned AI through Algebraic Machine Learning (AML). The Open-AML Engine provides a foundational implementation of AML algorithms, enabling experimentation, benchmarking, and further research by the broader community.
Supporting ALMA’s Vision for Symbolic, Transparent AI
The Open-AML Engine is the first complete, open-source implementation of the symbolic learning paradigm at the core of ALMA. Its release supports the project's goals of fostering open innovation in AI and accelerating scientific progress through reproducible and inspectable codebases.
The engine is designed to support:
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Research and prototyping of AML-based learning systems
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Interoperability with external components and datasets
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Evaluation of AML performance across real-world scenarios
By sharing this tool openly, the ALMA consortium continues to advance the principles of open science and contribute technical assets to the EU’s AI ecosystem.
Documentation and Getting Started
To help new users get started, the repository includes:
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API reference
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Step-by-step tutorials
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Working examples
Whether you're a researcher exploring symbolic learning or a developer integrating AML into larger systems, this book provides detailed guidance and practical materials to accelerate your work.
🔗 Explore the repository and documentation.
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952091.