Keynote Lectures

Lessons in Democratising AI
Alun Preece, Cardiff University, United Kingdom, United Kingdom

Technology Monitoring Leveraging eXtreme Multi-Label Classification
Philippe Cudré-Mauroux, University of Fribourg, Switzerland, Switzerland

Available Soon
Marta Sabou, Vienna University of Economics and Business (WU), Austria, Austria

Lessons in Democratising AI

Alun Preece
Cardiff University, United Kingdom

Brief Bio
Prof Alun Preece is Director of the Hartree Centre Cardiff Hub, Co-Director of Cardiff University's Security, Crime and Intelligence Innovation Institute, and Director of Impact in the School of Computer Science and Informatics. He has a 35-year career in artificial intelligence (AI), applying the technology in fields including healthcare, telecommunications, and defence. Between 2007 and 2016 he held leadership roles in joint UK and US Government International Technology Alliance fundamental research programmes involving 190 researchers working across academia, industry, and government. Since 2016 he has focused on developing the outputs of these programmes towards higher technology readiness levels. As Director of the Hartree Centre Cardiff Hub, he leads programmes of AI support to small and medium-sized enterprises across sectors including Creative, FinTech, MedTech, Net Zero, and Security. Prof Preece’s research focuses on techniques for information provisioning and decision-support in complex environments centred on human-computer collaboration and human-machine teams.

Abstract
This talk is about putting AI in the hands of decision-makers at the point of need. I’ve been fortunate over three-and-a-half decades working in AI and AI-adjacent areas to have benefited from long-term collaborations across disciplines. Reflecting on lessons learned, co-creation and reducing friction are key to successful adoption and transition of AI and related technologies. I’ve also seen genuine long-term progress towards addressing the fundamental problem in information and knowledge management: delivering the “right information to the right people at the right time”. I’ve always had a particular interest in delivering to people in pressured “front line” situations, including healthcare and security workers. Recently, I’ve led efforts to improve AI adoption and capability-building among small enterprises in our part of the UK (South Wales and the South West) including companies, social enterprises, and charities. My team is also engaged in school and community AI outreach. We sit between, on the one side, the traditional base of university research in AI and related areas and, on the other side, the needs of diverse sectors, groups, and individuals poised to benefit from the “right AI in the right place at the right time”. This talk will share some of what we’ve learned in our work.

Technology Monitoring Leveraging eXtreme Multi-Label Classification

Philippe Cudré-Mauroux
University of Fribourg, Switzerland

Brief Bio
Philippe Cudre-Mauroux is a Full Professor and the Director of the eXascale Infolab at the University of Fribourg in Switzerland. He received his Ph.D. from the Swiss Federal Institute of Technology EPFL, where he won both the Doctorate Award and the EPFL Press Mention in 2007. Before joining the University of Fribourg, he worked on information management infrastructures at IBM Watson (NY), Microsoft Research Asia and Silicon Valley, and MIT. He recently won the Verisign Internet Infrastructures Award, a Swiss National Center in Research award, a Google Faculty Research Award, as well as a 2 million Euro grant from the European Research Council. His research interests are in next-generation, Big Data management infrastructures for non-relational data and AI. Webpage: http://exascale.info/phil

Abstract
As innovation cycles accelerate across industries, organizations struggle to track emerging technologies, detect weak signals, and map fast-evolving knowledge landscapes. Traditional technology-monitoring pipelines rely on manual curation or classical machine-learning models that scale poorly when confronted with millions of documents and tens of thousands of possible technology tags. In this talk, I will present a scalable framework for technology monitoring based on eXtreme Multi-Label Classification (XMLC). I will highlight several advances developed in my group, including methods for explainable text classification, automated taxonomy evolution, and classification techniques designed to handle very large, structured label spaces.

Available Soon

Marta Sabou
Vienna University of Economics and Business (WU), Austria

Brief Bio
Available Soon

Abstract
Available Soon