Seminars
CETINIA Invited lectures:
Christoph Lütge, Technical University of Munich (Germany)
Date & Venue: 18/09/2023. Room 170, Departamental II, 11
Abstract: The increasing presence of artificial intelligence is associated with ethical and governance questions that we are only just beginning to analyze. This presentation explores some ethical opportunities as well as challenges of the use of AI in a variety of fields, including autonomous driving and generative AI. AI ethics fundamentals such as key ethical principles are discussed, as well as initiatives to govern the responsible and safe adoption of AI, in particular the recent EU AI act.
Short Bio: Christoph Lütge is Full Professor of Business Ethics at Technical University of Munich (TUM) and the Director of the TUM Institute for Ethics in Artificial Intelligence (IEAI). He is Distinguished Visiting Professor of Tokyo University and has held further visiting positions at Harvard (Berkman Klein Center), Taipei, Kyoto, Stockholm and others. He has a background both in philosophy as well as information studies, having taken his PhD at the Technical University of Braunschweig in 1999 and his habilitation at the University of Munich (LMU) in 2005. In 2007, he was awarded a Heisenberg Fellowship by the German Research Foundation. His most recent books are: “An Introduction to Ethics in Robotics and AI” (Springer, 2021, with coauthors) and “Business Ethics: An Economically Informed Perspective” (Oxford University Press, 2021, with Matthias Uhl). He is a member of the European AI Ethics initiative AI4People and of the German Ethics Commission on Automated and Connected Driving (2016-17). Since 2020, he is Consortium Leader of the Global AI Ethics Consortium.
Title: Limitations and New Frontiers in Deep Learning and its Applications to Data Science
Date & Venue: 29/04/2024.
Abstract: The advent of deep learning (DL) models has seen neural networks being successfully applied across many classification and prediction problems with implications especially in the areas of image and video, or text and speech processing and analysis. Since its (re-)emergence in 2009 deep neural networks have consistently replaced state-of-the-art approaches to machine learning and largely overcame the problem of hand-crafted feature extraction that dominated to little avail the field precedently, for over 50 years. One of the key enablers of DL models is the availability of compute, yet, although scale has proven to be a definitive driver in building increasingly better performing models, neural nets still exhibit serious vulnerabilities and erroneous behavior in terms of brittleness, spurious correlations, lack of interpretability, or more recently, hallucinating misinformation. In this talk I will set-out by identifying a few key underlying limitations in the deep learning literature, as well as addressing shortcomings of machine learning techniques more broadly. Following, I will point towards several research directions by reflecting on contributions from my previous and ongoing research. The presentation will focus on approaches showcased across various application domains of DL to Data Science problems including computer vision, natural language processing and internet of things settings.
Short Bio: Dr. Radu-Casian Mihailescu is an Associate Professor in Computer Science at the Faculty Mathematical and Computer Sciences, in Heriot-Watt University (UK). He received a degree in Computing from TU Timisoara (Romania) and a PhD from University Rey Juan Carlos in Madrid (Spain). His research focuses primarily on advances in the field of machine learning (ML), with particular emphasis on state-of-the-art deep learning architectures. Key areas of his research include topics such as out-of-distribution generalization, transfer learning, meta-learning, active learning, interactive learning, few-shot learning, domain adaptation, machine understanding, as well as building upon distributed representations learned by deep networks and incorporating reasoning as an integral part of the learning procedure. Moreover, he is also active in numerous applications of ML approaches to various real-world case-studies such as computer vision, natural language processing for fake news detection, activity recognition based on the internet of things infrastructures, context-adaptive surveillance systems or ambulance coordination for acute stroke care. He is also affiliated with the Internet of Things and People Research Center and has previously acted as Program Director for Applied Data Science Master’s Degree at Malmö University in Sweden.
Marija Slovkovik, University of Bergen (Norway)
Title: tbd
Date & Venue: 16/05/2024.
Abstract: tbd
Short Bio: tbd
Other seminars
A full list of invited lectures and previous seminars can be found here.