Intelligent ETL Solutions for XMANAI: API- and File Data Harvester

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Data is a driving factor when it comes to the growing impact of AI across all domains. As more and more industries want to benefit from the current development and adopt AI-products in order to accelerate their businesses, intelligent solutions for the processing and transportation of data, also referred to as ETL (Extract Transform Load) become increasingly relevant.

Partner Spotlight – Ford Motor Company

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Name: Javier Colomer Barberá
Job title: New Technologies Engineer
Organization: Ford Motor Company
Bio: Javier Colomer is a Telecommunications Engineer from the Polytechnic University of Valencia.

XAI Model Guard: The XMANAI AI Models Security Framework

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As manufacturing organizations are embracing the Industry 4.0 initiative that is revolutionizing the manufacturing sector towards the realization of smart factories, the adoption rate of technologies related to Artificial Intelligence (AI), machine learning, and analytics is also growing.

Explainable AI: a key to trust and acceptance of AI-based decision support systems

Explainable AI: a key to trust and acceptance of AI-based decision support systems Artificial intelligence is often based on complex algorithms and mathematical models that are difficult to understand. One of the characteristics of modern AI, based mainly on neural networks, is that it uses ‘black-box’ models, i.e. ‘boxes’ that make decisions without the user […]

Partner Spotlight – CNH Industrial

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Name: Claudia Campanella
Job title: Manager of Ergonomics-HMI-VR-AR
Organization: CNH Industrial
Bio: Claudia Campanella graduated in Industrial Design at the Polytechnic of Turin, realizing a thesis in physical ergonomics. She started working at Fiat Auto in 2000 and at the same time, she attended the Master in Ergonomics in which she created a thesis in cognitive ergonomics.

Industrial Asset Graph Modelling in XMANAI

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In the Industrial sector specifically, graph networks can describe pathways of IoT devices and sensor networks (Aggarwal, et al., 2017) in the framework of predictive maintenance, or represent associations between resources, daily workload and production in decision-making and dynamic scheduling problems (Hu, et al., 2020).

XMANAI partner spotlight – Deep Blue

Partner Spotlight Deep Blue

Name: Linda Napoletano
Job title: Head of the Manufacturing Area
Organization: Deep Blue srl
Bio: Linda Napoletano holds a Ph.D. in Human-Computer Interaction. Since 2002, she has been working on EU-co-funded projects aiming at designing and validating humans integration and interactions into highly innovative processes.

Technical and Socio-Business assessments of AI Maturity in pilots of Explainable AI

Technical and Socio-Business assessments of AI Maturity in pilots of Explainable AI The EU-funded XMANAI project focuses on Explainable AI, as the ability to make machine decision-making processes understandable; Explainability has proven to be a key element in stimulating the adoption of AI in various areas because it provides transparent and understandable information about algorithmic […]

XMANAI partner spotlight – UNIMETRIK

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Name: Aitor San Vicente
Job title: General Manager
Organization: UNIMETRIK
Bio: Expert in advanced industrial metrology services, Aitor has extensive experience both in the field of advanced manufacturing processes, mainly in the die-cutting and stamping sector, and in the development of quality control and digitalization solutions and strategies.

AI Algorithms Lifecycle Management and Collaboration

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In XMANAI we have set out to develop robust and insightful AI pipelines that can assist manufacturers in their everyday operations and decision-making processes. To achieve our goal, we are creating a collaborative environment in which the explainability of the ML models’ decisions lies at the heart of our AI pipelines design, development and roll out. Needless to say, in order for these AI pipelines to be properly configured, trained, evaluated, deployed and applied, constantly monitored, assessed and refined as needed, numerous other processes need to be in place.