Periodic Reporting for period 3 - ALMA (ALMA: Human Centric Algebraic Machine Learning)
Reporting period: 2023-09-01 to 2025-02-28
What is the problem/issue being addressed?
Algebraic Machine Learning (AML) is a new paradigm built on abstract algebra and model theory. Unlike deep learning and other popular algorithms, AML is not statistical. Instead, it produces generalising models from semantic embeddings of data into discrete algebraic structures. The result is a paradigm that:
1. is far less sensitive to the statistical characteristics of training data and does not fit (or even use) parameters;
2. can flexibly combine unstructured and complex information with formal specifications of human knowledge, constraints and task goals;
3. offers higher mathematical transparency than deep networks and other optimisation-based methods, using sets and graphs to generate human-understandable descriptions of what, why, and how has been learned; and
4. can be implemented in a distributed way that avoids centralised, privacy-invasive data collections in favour of collaboration among many local learners.
Why is it important for society?
Humans do not think in a vacuum but need substantial accumulated knowledge to resolve even simple real-life problems. High-impact areas such as healthcare, where AI could do the most good, require enormous knowledge to tackle challenges. AI can perform tasks humans cannot, but for this to happen, machines must learn what we know, communicate with each other, and collaborate with us. Current machine learning struggles to combine heterogeneous knowledge because it is statistical and lacks processes for encoding formal knowledge.
AML has the right properties to address these issues. It is less affected by training data statistics and provides mechanisms to encode heterogeneous knowledge. It can learn and produce complex models with multiple interrelated variables while also generalising and memorising. Its non-statistical nature and ability to encode constraints make AML suited to overcoming bias in training data and aligning machine intelligence with human ethical principles. Compared to statistical “black boxes,” AML models offer greater human control and understanding in both directions of communication. Ambitiously, AML makes augmentation of human intelligence possible, enabling true “human-computer partnerships” based on mutual understanding of world models, goals, and ethics.
What are the overall objectives?
The aim is to leverage AML properties for a new generation of interactive, human-centric ML systems. Interactive means humans and intelligent machines jointly learn and reason. AML enables users not only to reflect upon learning but also to drive it, enhancing cognitive powers through interaction. In line with Human-Centric AI, we will:
- reduce bias and prevent discrimination by reducing dependence on statistical data (P1), integrating formalised human knowledge (P2), and exploring the how and why of learning (P3);
- facilitate trust and reliability by respecting hard human-defined constraints (P2) and enhancing explainability (P3);
- integrate complex ethical constraints into Human-AI systems by going beyond bias prevention to interactively shape ethics between humans and machines (P3);
- enable distributed, incremental collaborative learning beyond centralised and offline approaches (P4).
Conclusions of the action
The project successfully delivered on these objectives. AML was formalised through new theorems and the AML Description Language (D2.1–D2.4 D3.1–D3.4). Collaborative learning was realised via AML-IP, a distributed integration framework with DDS Router and Dashboard support (D6.1–D6.4). Transparency and explainability were advanced through human-AI interaction methods and prototypes (D4.1–D4.11). Ethical and cultural world models were embedded into AML, demonstrating its ability to integrate sociocultural constraints (D5.3–D5.8). Finally, the release of an open-source AML engine and applied use cases in robotics and creative HCI confirmed AML as a viable and explainable alternative.
Algebraic Machine Learning (AML) is a new paradigm built on abstract algebra and model theory. Unlike deep learning and other popular algorithms, AML is not statistical. Instead, it produces generalising models from semantic embeddings of data into discrete algebraic structures. The result is a paradigm that:
1. is far less sensitive to the statistical characteristics of training data and does not fit (or even use) parameters;
2. can flexibly combine unstructured and complex information with formal specifications of human knowledge, constraints and task goals;
3. offers higher mathematical transparency than deep networks and other optimisation-based methods, using sets and graphs to generate human-understandable descriptions of what, why, and how has been learned; and
4. can be implemented in a distributed way that avoids centralised, privacy-invasive data collections in favour of collaboration among many local learners.
Why is it important for society?
Humans do not think in a vacuum but need substantial accumulated knowledge to resolve even simple real-life problems. High-impact areas such as healthcare, where AI could do the most good, require enormous knowledge to tackle challenges. AI can perform tasks humans cannot, but for this to happen, machines must learn what we know, communicate with each other, and collaborate with us. Current machine learning struggles to combine heterogeneous knowledge because it is statistical and lacks processes for encoding formal knowledge.
AML has the right properties to address these issues. It is less affected by training data statistics and provides mechanisms to encode heterogeneous knowledge. It can learn and produce complex models with multiple interrelated variables while also generalising and memorising. Its non-statistical nature and ability to encode constraints make AML suited to overcoming bias in training data and aligning machine intelligence with human ethical principles. Compared to statistical “black boxes,” AML models offer greater human control and understanding in both directions of communication. Ambitiously, AML makes augmentation of human intelligence possible, enabling true “human-computer partnerships” based on mutual understanding of world models, goals, and ethics.
What are the overall objectives?
The aim is to leverage AML properties for a new generation of interactive, human-centric ML systems. Interactive means humans and intelligent machines jointly learn and reason. AML enables users not only to reflect upon learning but also to drive it, enhancing cognitive powers through interaction. In line with Human-Centric AI, we will:
- reduce bias and prevent discrimination by reducing dependence on statistical data (P1), integrating formalised human knowledge (P2), and exploring the how and why of learning (P3);
- facilitate trust and reliability by respecting hard human-defined constraints (P2) and enhancing explainability (P3);
- integrate complex ethical constraints into Human-AI systems by going beyond bias prevention to interactively shape ethics between humans and machines (P3);
- enable distributed, incremental collaborative learning beyond centralised and offline approaches (P4).
Conclusions of the action
The project successfully delivered on these objectives. AML was formalised through new theorems and the AML Description Language (D2.1–D2.4 D3.1–D3.4). Collaborative learning was realised via AML-IP, a distributed integration framework with DDS Router and Dashboard support (D6.1–D6.4). Transparency and explainability were advanced through human-AI interaction methods and prototypes (D4.1–D4.11). Ethical and cultural world models were embedded into AML, demonstrating its ability to integrate sociocultural constraints (D5.3–D5.8). Finally, the release of an open-source AML engine and applied use cases in robotics and creative HCI confirmed AML as a viable and explainable alternative.
From the beginning of the ALMA project to the end of this reporting period, substantial advancements have been achieved in AML, establishing it as a promising human-centric paradigm. AML avoids traditional statistical approaches by leveraging abstract algebra and model theory, allowing for generalizable, interpretable, and bias-resistant learning models. Core technological outcomes include AML-DL for semantic embeddings, the AML Dashboard for usability, and AML-IP, a distributed integration framework.
The exploitation efforts have been significant in the latest project period. The core exploitation result for Algebraic AI is AML itself, as a novel and foundational machine learning paradigm. The ALMA project has laid the theoretical and practical foundation for AML to continue the research on this technology to become a viable alternative to deep learning. This includes foundational publications, the release of an open-source engine, and efforts to foster a new research and user community. For eProsima, exploitation is centred on tools developed within the project, particularly the DDS Router, AML-IP and AML Dashboard. The DDS Router tool is already in production environments and used by industrial clients, showcasing real-world impact and commercial viability. AML-IP, fully compatible with ROS 2 and FIWARE, is also a strategic asset for eProsima in bridging AI and robotics.
Importantly, ALMA has established the groundwork for future scientific research on AML. Its open formalisation, toolchain, and integration capabilities position it as a new frontier for ML research and application. Dissemination actions have effectively built visibility and awareness of AML. The consortium organised workshops, published scientific papers, and maintained an active online presence, achieving strong engagement from AI and robotics communities. The open-source releases, events, and collaborations fostered an ecosystem that ensures AML’s positioning as a credible, transparent, and explainable alternative.
The exploitation efforts have been significant in the latest project period. The core exploitation result for Algebraic AI is AML itself, as a novel and foundational machine learning paradigm. The ALMA project has laid the theoretical and practical foundation for AML to continue the research on this technology to become a viable alternative to deep learning. This includes foundational publications, the release of an open-source engine, and efforts to foster a new research and user community. For eProsima, exploitation is centred on tools developed within the project, particularly the DDS Router, AML-IP and AML Dashboard. The DDS Router tool is already in production environments and used by industrial clients, showcasing real-world impact and commercial viability. AML-IP, fully compatible with ROS 2 and FIWARE, is also a strategic asset for eProsima in bridging AI and robotics.
Importantly, ALMA has established the groundwork for future scientific research on AML. Its open formalisation, toolchain, and integration capabilities position it as a new frontier for ML research and application. Dissemination actions have effectively built visibility and awareness of AML. The consortium organised workshops, published scientific papers, and maintained an active online presence, achieving strong engagement from AI and robotics communities. The open-source releases, events, and collaborations fostered an ecosystem that ensures AML’s positioning as a credible, transparent, and explainable alternative.
ALMA has pushed beyond the state of the art by establishing Algebraic Machine Learning (AML) as a new paradigm that replaces statistical dependence with algebraic reasoning. AML integrates human knowledge and data, enabling transparent, interpretable, and bias-resistant models. This foundational shift supports a new generation of AI systems that are inherently explainable and ethically grounded. The project’s socio-economic impact lies in providing a trustworthy and explainable AI technology that can be adopted across sectors—from robotics to healthcare—reducing risks linked to opaque decision-making. Societally, AML empowers users to understand and influence AI behaviour. As a ground technology, AML sets the stage for safe, human-centric AI and responsible innovation. As a result, the ALMA project has set the basis for continuing research on this new explainable AI technology, paving the way for future scientific and industrial advancements in trustworthy Machine Learning systems.