The XMANAI reference architecture aims at providing the basis for the detailed specification, development and integration activities of the overall XMANAI platform along its Data and AI related Services Bundles, its XAI Algoritmms and Models Catalogue, and the different Manufacturing Apps.
In the new digital world where tremendous amount of information is generated from the increasing number of data sources, the emerging need for security and privacy techniques, methods and solutions has been raised.
Name: Dr Serafeim Moustakidis
Job title: Co-founder / CTO
Bio: Dr Serafeim Moustakidis has wide experience in computational intelligence, machine learning and data processing with more than 12 years of research experience in various fields.
In XMANAI, the exploration of the Explainable AI landscape, the analysis of the business requirements from its 4 demonstrators and the elicitation of the technical requirements have culminated with the definition of the Minimum Viable Product (MVP).
The implementation of Artificial Intelligence (AI) in the manufacturing domain enables higher production efficiency, and outstanding performance.
The management of assets in XMANAI should meet a number of critical requirements. One of them is the explainability of data, since Explainable AI is the main objective in the project.
Name: Dr. David Monzo
Job Title: Director of AI
Organization: Tyris AI:
Bio: Dr. David Monzo is the technical director and main investigator at Tyris AI.
XMANAI partner spotlight – Politecnico di Milano Giacomo Tavola Name: Giacomo Tavola Job title: Technology Counselor Organisation: Politecnico di Milano Bio: Giacomo Tavola (male) has a Degree in Electronic Engineering at Politecnico di Milano. He worked 25 years in ICT market for major ICT solution providers and hardware manufacturers. He has specific experience on application […]
Artificial Intelligence has a crucial role in the digital transformation roadmap of traditional manufacturing companies as if from one side it may bring great step improvement in several areas, on the other side it is probably the most difficult technology to be implemented in a sustainable way, due to the lack of knowledge and to the natural negative reaction to adoption that this type of technology generates in the involved people.
Fraunhofer FOKUS is the leader of the working package “Asset Management Bundles Methods and System Designs”. In this working package, management and sharing methods are defined and prototypically implemented for the assets. The assets mentioned are industrial data as well as AI models and analyses based on these data.