No próximo dia 24 de Fevereiro, pelas 11h00 na sala FC6 029 do DCC FCUP, Amparo Alonso Betanzos irá dar uma palestra intitulada "Rethinking AI: More with less".
A palestra é organizada pelo DCC-FCUP & LIAAD.
Short Bio
Amparo Alonso Betanzos is a Full Professor in the area of Computer Science and Artificial Intelligence at CITIC-University of A Coruña (UDC), where she coordinates the LIDIA group (Artificial Intelligence R&D Laboratory). Her research lines are the development of Scalable Machine Learning models, Reliable and Explainable Artificial Intelligence, and Green AI, among others. She has published more than 200 articles in journals and international conferences, and books and book chapters, participating in more than 30 competitive European, national and local research projects. She was formerly President of the Spanish Association of Artificial Intelligence (2013-21). She is a Senior Member of IEEE and ACM. She has participated as a member of the Working Group on Artificial Intelligence of the Spanish Ministry of Science, Innovation, and Universities, which collaborated in drafting the Spanish R&D&I Strategy in Artificial Intelligence in 2018. She is currently a member of CAIA, the Advisory Council on Artificial Intelligence of the Ministry of Digital Transformation and Public Function of the Government of Spain, since 2020, as well as a Member of the Spanish Research Ethics Committee of the Ministry of Science, Innovation and Universities of the Government of Spain, since 2023. She is also a Senior Member of IEEE and ACM. Since October 2023, she has been a corresponding member of the Royal Spanish Academy of Exact, Physical, and Natural Sciences. She has received several awards, including the Helena Rubinstein-UNESCO "Women in Science" in Spain and European finalist (1998), Galicia ICT Award for Digital Innovation (2004), Galicia ICT Award for Professional Career (2019), Josefa Wonenburger Planells Award from the Xunta de Galicia (2020), and Galician of the Year Award, 2020, Grupo Correo Gallego.
Title
Rethinking AI: More with less
Abstract
The success of Artificial Intelligence (AI) has so far relied on developing increasingly precise models. However, this has come at the cost of greater complexity, requiring a higher number of parameters to estimate. As a result, model transparency and explainability have diminished, while the energy demands for training and deployment have skyrocketed. It is estimated that by 2030, AI could account for more than 30% of the planet's total energy consumption. In this context, green and responsible AI has emerged as a promising alternative, characterized by lower carbon footprints, reduced model sizes, decreased computational complexity, and improved transparency. Various strategies can help achieve these goals, such as improving data quality, developing more energy-efficient execution models, and optimizing energy efficiency in model training and inference. These innovation approaches highlight the potential of green AI to challenge the prevailing paradigm of ever-growing models.