July 2022

Impressions from The Joint TAILOR-EurAI Summer school in Barcelona

During the week from 13th to 17th of June, the 19th EurAI Advanced Course on AI (ACAI) and 2nd TAILOR summer school was organised in Barcelona. This joint initiative was devoted to the themes of explainable and trustworthy AI and organized by Carles Sierra and Karina Gibert from the Intelligent Data Science and Artificial Intelligence Research Center at Universitat […]

Impressions from The Joint TAILOR-EurAI Summer school in Barcelona Read More »

Call for Papers: ACM Computing Surveys – Special Issue on Trustworthy AI

Guest Editors    Roberta Calegari, Alma Mater Studiorum-Università di Bologna, Italy – roberta.calegari@unibo.it      Fosca Giannotti, Scuola Normale Superiore, Italy – fosca.giannotti@isti.cnr.it     Michela Milano, Alma Mater Studiorum-Università di Bologna, Italy – michela.milano@unibo.it     Francesca Pratesi, National Research Council, Italy – francesca.pratesi@isti.cnr.it        This special issue calls for surveys that address at least one dimension of Trustworthy Artificial Intelligence (TAI) and provide a broad and

Call for Papers: ACM Computing Surveys – Special Issue on Trustworthy AI Read More »

Report from Theme Development Workshop “AI for Future Manufacturing”

On the 10th of May 2022, the fourth joint  “Theme Development Workshop” (TDW) focused on the latest trends and challenges of “AI for Future Manufacturing”. The discussion hosted close to 80 key experts from 20 European countries, from both academia and the manufacturing sector, as well as other relevant stakeholders. The participants had one primary, but still very challenging

Report from Theme Development Workshop “AI for Future Manufacturing” Read More »

Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Pratical Domains

Meta-learning aims to leverage the experience from previous tasks to solve new tasks using only little training data, train faster and/or get better performance. The proposed challenge focuses on “cross-domain meta-learning” for few-shot image classification using a novel “any-way” and “any-shot” setting. This challenge is part of TAILOR WP2 (see more information here). Goal The

Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Pratical Domains Read More »