Explainable Manufacturing Artificial Intelligence
Eρευνητικό Έργο - Ευρωπαϊκό
- Υπεύθυνος ΙΠΣΥ: Θοδωρής Δαλαμάγκας
- Θεματικές ετικέτες: Big Data , Data integration , Data Infrastructures , Digital preservation , Distributed data management , Graph databases , Interoperability , Knowledge extraction , Knowledge organization , Knowledge representation , Query processing , Machine learning
- Ημερομηνία έναρξης: 1-11-2020
- Διάρκεια: 40μήνες
- Πρόγραμμα: H2020-ICT-2018-20, ICT-38-2020
- Χρηματοδότηση: 5.9M
- Χρηματοδότηση ΙΠΣΥ: 318K
- Συνεργάτες: TXT E-SOLUTIONS SPA (TXT), FRAUNHOFER, TYRIS SOFTWARE SL, POLITECNICO DI MILANO, AIDEAS OU, SUITE5 DATA INTELLIGENCE SOLUTIONS LIMITED, INNOVALIA, ATHENA Research Center, KNOWLEDGEBIZ, WHIRLPOOL, FORD ESPANA SL, UBITECH, DEEP BLUE SRL, UNIMETRIK SA, CNH INDUSTRIAL ITALIA SPA.
Despite the indisputable benefits of AI, humans typically have little visibility and knowledge on how AI systems make any decisions or predictions due to the so-called “black-box effect” in which many of the machine learning/deep learning algorithms are not able to be examined after their execution to understand specifically how and why a decision has been made. The inner workings of machine learning and deep learning are not exactly transparent, and as algorithms become more complicated, fears of undetected bias, mistakes, and miscomprehensions creeping into decision making, naturally grow among manufacturers and practically any stakeholder In this context, Explainable AI (XAI) is today an emerging field that aims to address how black box decisions of AI systems are made, inspecting and attempting to understand the steps and models involved in decision making to increase human trust. XMANAI aims at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that “our AI is only as good as we are”. XMANAI, demonstrated in 4 real-life manufacturing cases, will help the manufacturing value chain to shift towards the amplifying AI era by coupling (hybrid and graph) AI "glass box" models that are explainable to a "human-in-the-loop" and produce value-based explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact.