16.07.2026
Gitta Kutyniok Featured in Heise on Energy-Efficient AI
Exploring the Potential of Neuromorphic Chips
MCML PI Gitta Kutyniok is featured in a recent Heise article exploring how neuromorphic chips could help reduce the energy consumption of artificial intelligence. The article examines how new computing architectures inspired by the human brain could make AI applications more efficient.
Kutyniok discusses the mathematical and computational foundations behind efficient AI and the potential of neuromorphic approaches. The article highlights how combining advances in AI algorithms and specialized hardware could help address the growing energy demands of modern AI systems.
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When Flatter Is Not Always Better
In their ICML 2026 paper, MCML researchers show why the flattest regions found during neural network training are not always the best.
13.10.2026
Björn Schuller: What Your Voice Reveals About Your Health
MCML PI Björn Schuller explores how AI can analyze the human voice to detect signs of disease and reveal insights into our health.
