ML: Auto ML and Hyperparameter Tuning
Automated Machine Learning can save valuable time and energy when using ML algorithms. By continously adjusting hyperparameters, performance can significantly increase.
PEAI: Bias, Fairness & Equity
Adressing fairness and biases in algorithms is extremely relevant when incorporating technology into our society.
SNLP: Sentiment Analysis and Stylistic Analysis
Increasing use of LLMs calls for more fine grained analysis of style and sentiment to ensure that models give an accurate representation of what is intended.
SNLP: Machine Translation & Multilinguality/Multimodality
AAAI Description Developing LLMs for mutliple languages, including low resource languages, is essential to ensure gobal access to new technologies.
HAI: Human-Machine Teams
Enabling teamwork between humans and AI surely is the key to success for solving complex problems in the digital age. The fear of automation overtaking jobs is dominating the conversation about AI but what if the promising prospect of human-AI collaboration simply facilitates certain tasks and does not take them away but leave room for […]
HAI: Human-Computer Interaction
Without a doubt, the nature of collaboration between humans and computers is one of the most pressing research needs in the field of AI. We need to learn how to integrate new technology into our daily lives to enhance our given competences and make our lives more efficient.
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
Zeitreihenanalyse (Time series) treten in vielen verschiedenen Bereichen auf, von medizinischen Daten (z. B. EEG) bis hin zu Mobilität und Verkehr. Diese Mikro Fokus Gruppe konzentriert sich auf CNN-Modelle zur Analyse ihrer Daten.
DMKM: Linked Open Data, Knowledge Graphs & KB Completion
Die Organisation von Wissen ist grundlegend für die Nutzung von KI-Algorithmen, da sie oft die Erklärbarkeit und Transparenz erleichtert.
