This new Machine Learning algorithm proposes a totally different approach to AI. Instead of a statistical based approach in which the learning process is a “black box”, it is based in an Algebra, leading to very interesting features.
Using this Algebra, you can describe what you already know about the problem, and unlike statistical learning, AML algorithms are robust regarding the statistical properties of the data and are parameter-free.
eProsima, the company behind Fast DDS, is the coordinator of the ALMA project and also in charge of the middleware: AML can be implemented in a distributed way, and thus Facilitate a new distributed, incremental collaborative learning method by going beyond the dominant off-line and centralized data processing approach.
And sure, part of our work is to integrate AML with ROS 2 and Fast DDS. The goal is to enable distributed learning in a ROS 2 system, creating Robots with the ability of sharing their knowledge and learning together. Cool right?
The ALMA consortia include the best AI research institutes of Europe, and the original authors of the Algebraic Machine Learning theory, and an initial budget of 4.000.000 Eur.
ALMA Consortia Members: eProsima, Algebraic.ai, Champalimaud, DFKI, INRIA, KAISERSLAUTERN, VTT, UC3M and Fiware Foundation.