Modern distributed systems often need to connect and share information across separate networks. DDS Router is a cross-platform application developed by eProsima and powered by Fast DDS that allows users to create a communication bridge that connects two DDS networks that otherwise would be isolated. It’s a simple, reliable tool that helps systems on different local area networks (LANs) share information as if they were on the same network.
The AML Dashboard is a versatile tool designed to bridge the gap between the complexity of algebraic machine learning (AML) and its practical application. Tailored to professionals, including researchers, computer scientists, and creative experts, this intuitive platform streamlines the process of training, evaluating, and applying AML models across diverse datasets. With its user-friendly interfaces, the AML Dashboard enhances data management, model training, and real-time exploration, making AML more accessible and efficient. Whether advancing machine learning research or exploring new creative possibilities, the AML Dashboard provides the essential tools to leverage the AML framework fully.
At eProsima, we recognize the importance of exploring new mathematical and computational approaches in Artificial Intelligence. That’s why we are excited to highlight ALTAI2025, an interdisciplinary meeting that will bring together mathematicians, computer scientists, and neuroscientists to discuss and develop alternative methodologies in AI.
Algebraic Machine Learning (AML) is an innovative AI approach that departs from traditional statistical methods. By utilizing abstract algebra, AML learns from data and addresses complex problems without depending on statistical techniques, search algorithms, or error minimization.
Champalimaud Foundation Explores the Intersection of AI and Cancer Research
The ALMA project leverages AML properties to develop a new generation of interactive, human-centered machine learning systems.
Plaza de la Encina 10-11, Núcleo 4, 2ª Pl.
28760 Tres cantos - Madrid (España)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952091.
