Explaining Transformers Transformers are neural network architectures that have delivered performant solutions in several fields including Natural Language Processing (NLP), computer vision & audio/speech analysis. In fact, state-of-the-art NLP models such as GPT4 and BERT are built using transformer blocks. The self-attention mechanisms upon which these models are built allow for parallel processing of the […]
How are the final MVP features contributing to the X-By-Design Concept? Introduction to the Final MVP As already discussed in a previous post, the XMANAI Minimum Viable Product (MVP) is designed with the minimum set of features and functionalities that can satisfy early adopters who, in turn, can promptly provide feedback for future improvements. During […]
The Power of X-by-design: Pioneering Transparency and Explainability through Design In recent years, artificial intelligence (AI) has rapidly transformed various industries, revolutionizing how we interact with technology and process information. However, alongside these advancements, concerns about the transparency and interpretability of AI systems have emerged. The concept of “explainable AI” (XAI) has thus gained significant […]
The XMANAI Hackathon, held in Athens, Greece on the 13th and 14th of July, provided a unique platform for students, data scientists, and industry experts to explore the critical need for explainability in AI applied to manufacturing.
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.
XAI models are essential in enabling businesses to make data-driven decisions more efficiently and effectively.
Artificial intelligence (AI) has become integral to modern manufacturing processes, enabling increased efficiency, productivity, and automation.
The ethics implications of AI and machine learning are becoming increasingly relevant due to their implementations across several sectors. This is the case also for their application to the manufacturing domain, which is still a quite new application of AI technology.
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.
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 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 […]
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.
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).
The Explainable AI (XAI) in Manufacturing (XMANAI) project aims to provide a framework for the development and deployment of AI models in the manufacturing industry.
In the new era of Industry 4.0, AI systems are becoming increasingly prevalent and cost-effective. With the ability to analyze vast amounts of data, factories can reduce expenses, boost productivity, and minimize waste.