ADR Awareness Centre

Z-Inspection® is a methodology for assessing the trustworthiness of AI systems.

AI has emerged as a critical area of interest to numerous stakeholders across the world.

The TAILOR Impact Stories is a powerful demonstration of the collective achievements and spirit of the TAILOR network.

This document is the first version of the Strategic Research and Innovation Roadmap of the TAILOR project, focussed on Trustworthy Artificial Intelligence through Learning, Optimization and Reasoning.

The main goal of the Handbook of Trustworthy AI (HTAI) is to provide to non-experts, researchers and students, an overview of the problem related to the developing of ethical and trustworthy AI systems.

Recognizing human activity from IoT events is essential for automatic light scene setting. A challenge with IoT systems is the diversity among customers.

This talk will explore the cutting-edge advancements in EdgeAI for biosignal analysis, focusing on innovative algorithms such as Spiking Neural Networks (SNNs), Transformers, and Autoencoders.

This presentation highlights the critical elements defining edge AI solutions’ performance and effectiveness.

In today’s fast-evolving technological landscape, edge AI stands at the forefront of innovation, enabling data processing at the edge, closer to the source of information.

Explaining AI decisions in in critical sectors like healthcare, autonomous vehicles, and
environmental monitoring is crucial for ensuring safety, trust, and transparency.

This paper outlines the initial FPGA-centric endeavors within the EdgeAI project, targeting scenarios where extremely constrained power-energy parameters intersect with the demand for high performance and accuracy in executing Artificial Intellige