SNLP: Machine Translation & Multilinguality/Multimodality
AAAI Description Die Entwicklung von LLM für mehrere Sprachen, einschließlich Sprachen mit geringer Ressourcenausstattung, ist unerlässlich, um den weltweiten Zugang zu neuen Technologien zu gewährleisten.
HAI: Human-Machine Teams
Die Ermöglichung von Teamarbeit zwischen Menschen und KI ist sicherlich der Schlüssel zum Erfolg bei der Lösung komplexer Probleme im digitalen Zeitalter. Die Angst vor der Übernahme von Arbeitsplätzen durch die Automatisierung beherrscht die Diskussion über KI. Was aber, wenn die vielversprechende Aussicht auf eine Zusammenarbeit zwischen Mensch und KI einfach nur bestimmte Aufgaben erleichtert […]
HAI: Human-Computer Interaction
ML: Multimodal Learning
As the world around us is complex, it is important to incorporate multiple modalities into systems created to support humans whether it is in medicine as robotic surgical assistants or other work environments.
PEAI: Societal Impact of AI
Societal impact of AI is one of the most talked about topics in media at the moment due to its grave importance. Focusing on human-centered needs whether in applications in healthcare or governance of sociotechnological systems is essential.
ML: Time-Series / Data Streams
Time series analysis occurs in many different areas, from medical data (e.g. EEG) to mobility and transportation. This micro focus group concentrates on CNN models to analyze their data.
DMKM: Linked Open Data, Knowledge Graphs & KB Completion
The organization of knowledge is fundamental to the use of AI algorithms, as it often facilitates explainability and transparency.
Cyber/ IT-Security
Software systems must be secure and robust against external attacks. This group focuses on cyber and IT security.
ML: Graph-based Machine Learning
Graph-based machine learning uses coherent data structures (graphs) to model relationships and dependencies. This approach improves the ability of AI to analyze and predict complex relationships in various domains, from social networks to recommendation systems.
CV: Vision for Robotics & Autonomous Driving
Image recognition and processing are not only crucial for the future of autonomous driving, but also in robotics. LiDAR sensors and deep neural network architectures help to accurately process different types of perceptions.