IX Congreso Internacional de Inteligencia Artificial y Reconocimiento de Patrones IWAIPR 2025
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Opening of the IX International Congress on Artificial Intelligence and Pattern Recognition
Dr. C. Yanio Hernández Herediaoct.. 14
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Track 1: Computer Vision and Medical Imaging
oct.. 14
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From Pixels to Prognosis: An AI Framework for Volumetric RALE Scoring in Post-COVID Chest CT
Eduardo Garea-Llanooct.. 14
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Bone Fracture Recognition using Robust Deep Learning Techniques
Serestina Viririoct.. 14
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Toward a Microstructure-Informed Streamline Tractography Method via COMMIT-Based Convex Optimization
Vitali Herrera-Semenetsoct.. 14
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Leveraging Large-Scale Face Datasets for Deep Periocular Recognition via Ocular Cropping
Fernando Alonso-Fernandezoct.. 14
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FGSSNet: Feature-Guided Semantic Segmentation of Real World Floorplans
Fernando Alonso-Fernandezoct.. 14
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Temporal Integration with Confidence and Uncertainty Modeling for Robust License Plate Recognition in Low-Quality Video Sequences
Milton García-Borrotooct.. 14
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Welcoming Remarks by the President of the Scientific and Technological Park of Havana
Rafael Luis Torralbas Ezpeletaoct.. 15
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Opening Lecture: IA Alliance Network
oct.. 15
Ricardo Baeza-Yates is Director of Research at the Institute for Experiential AI of Northeastern University, as well as part-time professor at the Dept. of Computer Science of University of Chile. Before, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected for the ACM Council. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, and his areas of expertise are responsible AI, web search and data mining plus data science and algorithms in general.
El aprendizaje automático (ML), en particular el aprendizaje profundo, se utiliza en todas partes. Sin embargo, no siempre se utiliza bien, ética y científicamente. En esta charla nos adentraremos primero en las limitaciones del ML supervisado y en los datos, su componente clave. Se cubren los datos pequeños, la datificación, el sesgo, los problemas de optimización predictiva, la evaluación del éxito en lugar del daño y la pseudociencia, entre otros problemas. La segunda parte trata de las limitaciones en el uso del ML, incluyendo distintos tipos de incompetencia humana: sesgos cognitivos, aplicaciones poco éticas, falta de competencia administrativa, violaciones de los derechos de autor, desinformación y el impacto en la salud mental. En la última parte se aborda la regulación del uso de la IA y los principios de la IA responsable, que pueden mitigar los problemas antes señalados.