RESOURCES

DISSEMINATION MATERIALS:

5. NEWSLETTER #2 (November 2021)

Driven by our key message “Moving from ‘black box’ to ‘glass box’ Artificial Intelligence in Manufacturing”, the project has produced some interesting progress during the second semester. Check out our second newsletter with sections on:

  • Key Stakeholders
  • A Look into our Pilots
  • Requirements Elicitation
  • XMANAI Minimum Viable Product (MVP)
  • Dissemination and Collaboration
 

Available for download here.

4. XMANAI ROLL-UP

XMANAI roll-up to present the overall project concept and objectives. In 2022 it is expected that the covid-19 pandemic allows, at least in a certain degree, the return to the physical/hybrid interaction with the community where the roll-up can be used.

XMANAI focuses on explainable AI, a concept that contradicts the idea of the ‘black box’ in machine learning. Carving out a ‘human-centric’ trustful approach tested in industrial demonstrators, the project aims to transform the manufacturing value chain with ‘glass box’ models explainable to a ‘human in the loop’.

Available for download here.

3. XMANAI TRIFOLD

The trifold is an efective means to disseminate the project both on physical and online events, summarising not only the concept, factsheet, and objectives but also the main results available to the moment. Within the trifold you will find a brief presentation of the pilots and the high level architecture, highlighting the core XMANAI services.    

Available for download here.

2. NEWSLETTER #1 (May 2021)

Driven by our key message “Moving from ‘black box’ to ‘glass box’ Artificial Intelligence in Manufacturing”, the project has produced some interesting progress during the first six months. Check out our first newsletter with sections on:

  • Project Objective
  • Meet Our Team
  • XMANAI Concept and Approach
  • XMANAI Scientific Workshop
  • Collaboration
 

Available for download here.

1. XMANAI FLYER

XMANAI consists of 15 partners from 7 countries Italy, Germany, Spain, Estonia, Portugal, Greece and Cyprus. The consortium is very well balanced in terms of research-industry collaboration as the next table depicts, containing a very well though constructed mixture of collective expertise from industry, research, academia, technology providers with solid background on manufacturing, data science and AI, and big data, and human-machine ethics sectors.  

Available for download here.

PUBLIC DELIVERABLES:

At the moment, XMANAI does not have any public deliverables available.