Innovating Works

ALMA

Financiado
ALMA Human Centric Algebraic Machine Learning
Algebraic Machine Learning (AML) has recently been proposed as new learning paradigm that builds upon Abstract Algebra, Model Theory. Unlike other popular learning algorithms, AML is not a statistical method, but it produces gener... Algebraic Machine Learning (AML) has recently been proposed as new learning paradigm that builds upon Abstract Algebra, Model Theory. Unlike other popular learning algorithms, AML is not a statistical method, but it produces generalizing models from semantic embeddings of data into discrete algebraic structures, with the following properties: P1: Is far less sensitive to the statistical characteristics of the training data and does not fit (or even use) parameters P2: Has the potential to seamlessly integrate unstructured and complex information contained in training data, with a formal representation of human knowledge and requirements; P3. Uses internal representations based on discrete sets and graphs, offering a good starting point for generating human understandable, descriptions of what, why and how has been learned P4. Can be implemented in a distributed way that avoids centralized, privacy-invasive collections of large data sets in favor of a collaboration of many local learners at the level of learned partial representations. The aim of the project is to leverage the above properties of AML for a new generation of Interactive, Human-Centric Machine Learning systems., that will: - Reduce bias and prevent discrimination by reducing dependence on statistical properties of training data (P1), integrating human knowledge with constraints (P2), and exploring the how and why of the learning process (P3) - Facilitate trust and reliability by respecting ‘hard’ human-defined constraints in the learning process (P2) and enhancing explainability of the learning process (P3) - Integrate complex ethical constraints into Human-AI systems by going beyond basic bias and discrimination prevention (P2) to interactively shaping the ethics related to the learning process between humans and the AI system (P3) - Facilitate a new distributed, incremental collaborative learning method by going beyond the dominant off-line and centralized data processing approach (P4) ver más
28/02/2025
PROYECTOS Y SISTEM...
4M€
Duración del proyecto: 56 meses Fecha Inicio: 2020-06-22
Fecha Fin: 2025-02-28

Línea de financiación: concedida

El organismo H2020 notifico la concesión del proyecto el día 2020-06-22
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
FETPROACT-EIC-05-2019: FET Proactive: emerging paradigms and communities
Cerrada hace 4 años
Presupuesto El presupuesto total del proyecto asciende a 4M€
Líder del proyecto
PROYECTOS Y SISTEMAS DE MANTENIMIENTO Consultoria tecnica, estudios de viabilidad, tecnicos, informes, auditorias funcionales y planes de sistemas de proyectos telematicos.
Perfil tecnológico TRL 4-5 965K