Artificial Intelligence Association of Lithuania

Are you ready for an action-packed keynote at #ECMLPKDD '24 by dr. Patrick Lucey? 🏀⚽️🎾 Patrick Lucey is the Chief Scientist at Stats Perform, where he leads the AI team in maximizing the value of the company’s vast sports data. With over 20 years of experience in machine learning and computer vision, he has held research positions at Disney Research, Carnegie Mellon University, and IBM’s T.J. Watson Research Center. Originally from Australia, dr. Lucey received his BEng(EE) from the University of Southern Queensland and his Ph.D. from Queensland University of Technology. He has authored over 100 peer-reviewed papers and has been a standout at the MIT Sloan Sports Analytics Conference, winning best paper in 2016 and runner-up in 2017 and 2018. dr. Lucey’s research focuses on AI in sports and AI education, recently piloting a course on "AI in Sport", which aims to give students intuition behind AI methods using the interactive and visual nature of sports data. Keynote title: "How to Utilize (and Generate) Player Tracking Data in Sport" Abstract: Even though player tracking data in sports has been around for 25 years, it still poses as one of the most interesting and challenging datasets in machine learning due to its fine-grained, multi-agent, team-based, and adversarial nature. Despite these challenges, it is also extremely valuable as it is (relatively) low-dimensional, interpretable, and interactive, allowing us to measure performance and answer questions we couldn’t objectively address before. In this talk, I will first give a brief history of tracking data in sports, then highlight the challenges associated with utilizing it. I will then show that by obtaining a permutation invariant representation, we can not only measure aspects of sports that couldn’t be done before, but also interact with and simulate plays akin to a video game via our “visual search” and “ghosting” technology. Finally, I will show how we can use both tracking and event data to create a multimodal foundation model, which enables us to generate player tracking data at scale and achieve our goal of “digitizing every game of professional sport.” Throughout the talk, I will utilize examples from top-tier basketball, soccer, and tennis. Don't miss this chance to discover the cutting-edge innovations in sports data! 📊 🏆🚀 Register for the conference now: https://lnkd.in/dYDsycZt

Introducing another must-see keynote at this year’s ECML PKDD in Vilnius! Don't miss the insightful talk by Prof. Katharina Morik. 🌟 Prof. Morik, a pioneer in merging machine learning with computing architectures, has made significant strides in executing and training ML models on resource-restricted devices. She established the chair of Artificial Intelligence at TU Dortmund in 1991 and recently retired in 2023. Her achievements include acquiring and leading the Collaborative Research Center CRC 876 for 12 years, resulting in 3 books on Resource-Constrained Machine Learning. Prof. Morik has been actively involved in numerous European research projects and has served as Program Chair and founding member of the IEEE International Conference on Data Mining (ICDM). She co-founded the Lamarr Institute for Machine Learning and AI and is a member of several prestigious academies. In 2019, she was honored as a Fellow of the German Society of Computer Science (GI e.V.). Keynote title: "Resource-Aware Machine Learning — a User-Oriented Approach" 🌱🔍 Abstract: Machine Learning (ML) has become integrated into several processes, ranging from medicine, manufacturing, logistics, smart cities, sales, recommendations and advertisements to entertainment and many more business and private processes. The applications together consume a considerable amount of energy and emit CO2. ML research investigates how to make models smaller and faster through pruning and quantization. Also, the use of more energy-efficient hardware is an encouraging field. Research on ML under resource constraints is an active field proposing novel algorithms and scenarios. The aim is that for each application a variety of implementations is offered from which customers and the different types of users may choose the most thrifty one. This, in turn, would push tech providers to focus on the production of economical systems. However, if the customers, users, stakeholders do not know, which of the models offers the best tradeoff between performance and energy-efficiency, they cannot select the most frugal one. Hence, testing implementations of learning and inference needs to be developed. They should be easy to use, produce visualizations that are mass-tailored for specific user groups. Automatized testing is difficult due to the diversity of models, computing architectures, training and evaluation data, and the fast rate of changes. The talk will illustrate work on resource-aware ML and advocate to pay more attention to the role of users in the development of scenarios, models, and tests. Join us for this keynote that highlights the importance of sustainability in machine learning and discover how resource-aware ML can drive efficiency and reduce environmental impact! 🌿💡🌍

🌟Introducing a yet another insightful keynote talk at this year’s ECML PKDD in Vilnius! Don’t miss the keynote that will be given by dr. Mounia Lalmas-Roelleke. dr. Lalmas-Roelleke is a Senior Director of Research at Spotify and Head of Tech Research in Personalization, leading a diverse team of research scientists. She holds an honorary professorship at University College London and is a Distinguished Research Fellow at the University of Amsterdam. Previously, she was Director of Research at Yahoo, focusing on advertising quality and user engagement. dr. Lalmas-Roelleke has an extensive background in academia, having held positions at the University of Glasgow and Queen Mary, University of London. A prominent figure in the research community, she has served on senior program committees for major conferences and has co-chaired SIGIR 2015, WWW 2018, WSDM 2020, and CIKM 2023. With over 250 published papers, dr. Lalmas-Roelleke is a recognized speaker and author, recently nominated for the VentureBeat Women in AI Awards for Research in both 2022 and 2023. Keynote title: "Enhancing User Experience with AI-Powered Search and Recommendations at Spotify" 🎶🤖 Abstract: This talk will explore the pivotal role of search and recommendation systems in enhancing the Spotify user experience. These systems serve as the gateway to Spotify's vast audio catalog, helping users navigate millions of music tracks, podcasts, and audiobooks. Effective search functionality allows users to quickly find specific content, whether it is a favorite song, a trending podcast, or an informative audiobook, while also satisfying broader search needs. Meanwhile, recommendation systems suggest new and relevant content that users might not have thought to search for, while ensuring their current needs for familiar content are met. This encourages exploration and discovery of new artists, genres, and shows, enriching the overall listening experience and keeping users engaged with the platform. Achieving this dual objective of precision and discovery requires sophisticated technology. It involves a deep understanding of representation learning, where both content and user preferences are accurately modeled. Advanced AI techniques, including machine learning and generative AI, play a crucial role in this process. These technologies enable the creation of highly personalized recommendations by understanding complex user behaviors and preferences. Generative AI, for instance, allows us to create personalized playlists, thereby enhancing the user experience with innovative features. This presentation is based on the collective research and publications of numerous contributors at Spotify. We hope you join this fascinating keynote and cannot stress this enough: if you haven't registered for the conference yet, now's the time! 🚀🚀

🌟 Excited for more insightful keynote talks at this year’s ECML PKDD in Vilnius!? Don't miss the one that will be given by dr. Moritz Hardt. dr. Hardt is a director at the Max Planck Institute for Intelligent Systems in Tübingen. Prior to this, he served as an Associate Professor for Electrical Engineering and Computer Sciences at the University of California, Berkeley. His work significantly contributes to the scientific foundations of machine learning and algorithmic decision-making, with a keen focus on social issues. He is also the co-author of "Fairness and Machine Learning: Limitations and Opportunities" (MIT Press) and "Patterns, Predictions, and Actions: Foundations of Machine Learning" (Princeton University Press). Keynote title: "The Emerging Science of Benchmarks" 🧠🔍 Abstract: Since the 1980s, benchmarks have been pivotal in advancing machine learning research. Despite their widespread use, our understanding of their mechanisms remains limited. In this talk, dr. Hardt will outline the nascent science of benchmarks through selected empirical and theoretical insights. Reflecting on the ImageNet era, he will explore the lessons learned about model ranking validity and label error impacts. Looking forward, he will address new benchmarking challenges in the age of large language models. The findings challenge conventional views and highlight the importance of a scientific approach to benchmarks. Join us for this enlightening keynote! 🚀🔥 If you haven't registered for the conference yet, now's the time! 🚀🚀 Register via: https://lnkd.in/dSJANUCa