©aiforgood
21.03.2025
Explainable Multimodal Agents With Symbolic Representations & Can AI Be Less Biased?
Our Junior Member Ruotong Liao at United Nations AI for Good
More than 170 audiences visited the online lecture of our Junior Member Ruotong Liao on Monday, 17. March 2025, as an invited speaker at the United Nations “AI for Good”. With her talk “Perceive, Remember, and Predict: Explainable Multimodal Agents with Symbolic Representations,” Ruotong Liao took …
©TUM
18.03.2025
New Method Significantly Reduces AI Energy Consumption
TUM News Article
Researchers at the Technical University of Munich have developed an innovative method that drastically lowers the energy consumption of artificial intelligence systems. The approach optimizes computational efficiency, making AI applications more sustainable and cost-effective. Our Associate Felix …
13.03.2025
ReNO: A Smarter Way to Enhance AI-Generated Images
MCML Research Insight - With Luca Eyring, Shyamgopal Karthik, Karsten Roth and Zeynep Akata
Despite their impressive capabilities, Text-to-Image (T2I) models frequently misinterpret detailed prompts, leading to errors in object positioning, attribute accuracy, and color fidelity. Traditional improvements rely on extensive dataset training, which is not only computationally expensive but …