Descrizione del progetto
Sfruttare l’algebra astratta per un’intelligenza artificiale più trasparente
L’apprendimento automatico algebrico costituisce una tecnica di apprendimento automatico relativamente nuova, basata sulle rappresentazioni algebriche dei dati. A differenza dell’apprendimento statistico, gli algoritmi dell’apprendimento automatico algebrico dimostrano solidità in fatto di proprietà statistiche dei dati e non necessitano di parametri. Lo scopo del progetto ALMA, finanziato dall’UE, è l’impiego delle proprietà dell’apprendimento automatico algebrico per sviluppare una nuova generazione di sistemi di apprendimento automatico interattivi e incentrati sugli individui. Questi sistemi dovrebbero ridurre gli errori ed evitare la discriminazione, ricordando quello che sanno quando imparano qualcosa di nuovo, favorendo l’attendibilità e l’affidabilità e l’integrazione di vincoli etici complessi all’interno di sistemi di intelligenza umana-artificiale. Inoltre, dovrebbero promuovere l’apprendimento distribuito e collaborativo.
Obiettivo
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)
Campo scientifico
- natural sciencesmathematicspure mathematicsdiscrete mathematicsmathematical logic
- social sciencessociologysocial issuessocial inequalities
- natural sciencesmathematicspure mathematicsalgebra
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesdata sciencedata processing
Parole chiave
Programma(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-EIC-FETPROACT-2019
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
28760 TRES CANTOS MADRID
Spagna
L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.