Our Objectives
The aim of the EU-funded ALMA project is to leverage AML properties to develop a new generation of interactive, human-centric machine learning systems. These systems are expected to reduce bias and prevent discrimination, remember what they know when they are taught something new, facilitate trust and reliability and integrate complex ethical constraints into human–artificial intelligence systems. Furthermore, they are expected to promote distributed, collaborative learning.
Our Methodology
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:
It is far less sensitive to the statistical characteristics of the training data and does not fit (or even use) parameters.
P2
It has the potential to seamlessly integrate unstructured and complex information contained in training data with a formal representation of human knowledge and requirements.
It uses internal representations based on discrete sets and graphs, offering a good starting point for generating human understandable, descriptions of what, why and how something has been learned.
It 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.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952091.