Alípio Jorge, University of Porto, Portugal
Narrative Extraction from Text
Difference types of textual sources contain narratives, refer to their participants, the events, when they happened, where they happened, and how these elements relate to each other. In this talk I will describe the effort of automatically extracting narratives from text using information extraction, NLP and linguistics. The result of narrative extraction is a formal structured representation that can be used for different types of visualisation and other sorts of representation or manipulation. The work is mainly the result of the Text2Story and StorySense projects, where computer scientists and linguists are working together in these challenges. It is also our aim to increase the available resources for Portuguese, especially for its European variant.
Alípio Jorge is full professor at University of Porto and a researcher at INESC TEC. He is currently head of the Department of Computer Science and Coordinator of the Artificial Intelligence and Decision Support Laboratory (LIAAD). He has dedicated his research career to machine learning, including inductive logic programming, frequent pattern extraction, recommendation systems and natural language processing. He launched with colleagues several MSc and BSc on Data Science and AI. He was a Sherpa for Portugal at the European Commission and responsible for the Portuguese Artificial Intelligence Strategy document.
Paolo Missier, Newcastle University, United Kingdom
Data-centric AI and the convergence of data and model engineering: opportunities to streamline the end-to-end data value chain.
The past few years have seen the emergence of what the AI community calls “Data-centric AI”, namely the recognition that some of the limiting factors in AI performance are in fact in the data used for training the models, as much as in the expressiveness and complexity of the models themselves. One analogy is that of a powerful engine that will only run as fast as the quality of the fuel allows. A plethora of recent literature has started exploring the connection between data and models in depth, along with startups that offer “data engineering for AI" services. Some concepts are well-known to the data engineering community, including incremental data cleaning, multi-source integration, or data bias control; others are more specific to AI applications, for instance the realisation that some samples in the training space are "easier to learn from” than others. In this “position talk” I will suggest that, from an infrastructure perspective, there is an opportunity to efficiently support patterns of complex pipelines where data and model improvements are entangled in a series of iterations. I will focus in particular on end-to-end tracking of data and model versions, as a way to support MLDev and MLOps engineers as they navigate through a complex decision space.
Paolo is currently a Professor of Scalable Data Analytics with the School of Computing at Newcastle University, and has been a Fellow (2018-2023) of the Alan Turing Institute, UK's National Institute for Data Science and Artificial Intelligence. His qualifications include first and MSc degrees in Computing at Universita' di Udine, Italy (1990), a further MSc in Computer Science from University of Houston (USA), and a PhD in Computer Science from the University of Manchester (2008), with focus on infrastructure to support data quality in workflow programming for scientific applications. Paolo leads post-graduate teaching on Data Engineering for AI, and introduction to Predictive Analytics at the undergraduate level.
He has been Sr. Associate Editor for the ACM Journal on Data and Information Quality (JDIQ), 2016-2023.
Carlos Cotta, Universidad de Málaga, Spain
Resilience in Bioinspired Algorithms: Towards Future-Proofing Optimization
Resilience is the capacity of a system to recover and resume normal operation following a disruption. It is a crucial feature in many domains, and particularly in bioinspired optimization algorithms, such as evolutionary computation and swarm intelligence methods. We devise a field map for resilience and show how bioinspired algorithms do not only possess notorious built-in resilience, but are also flexible enough to accommodate algorithmic add-ons oriented to boost resilience. This paves the way for outlining potential research directions in this field.
Carlos Cotta obtained his PhD in Computer Science from the University of Malaga in 1998, and has held the position of Full Professor at the same institution since 2017. His primary research area centers around heuristic optimization using hybrid and memetic approaches, with a strong emphasis on both algorithmic issues and practical applications. Throughout his career, he has contributed to this field authoring over 250 scientific publications and leading various research projects. Presently, his research interests are primarily focused on simulation-based optimization and algorithmic resilience.