My work has been featured by different forms of media and science communication (e.g. print, online, radio, podcast, …).

Partial success in closing the gap between human and machine vision:

• Machine Learning for Science: Do machines see like humans? They are getting closer

Shortcut Learning in Deep Neural Networks:

• Frankfurter Allgemeine Zeitung: Hier irrt der Algorithmus
• The Gradient: Shortcuts: How Neural Networks Love to Cheat
• Tech Xplore: Exploring the notion of shortcut learning in deep neural networks
• Knowable Magazine: Why some artificial intelligence is smart until it’s dumb (also in The Week)
• Deeplearning.ai: The Batch newsletter
• Towards Data Science: Shortcut Learning, The Reason ML Models Often Fail in Practice
• Underrated ML podcast: Energy functions and shortcut learning

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness:

• Quanta Magazine: Where We See Shapes, AI Sees Textures
• Spektrum der Wissenschaft (German edition of the Scientific American): Form versus Textur
• The Register: Object-recognition AI – the dumb program’s idea of a smart program: How neural nets are really just looking at textures
• Two Minute Papers: Do Neural Networks Need To Think Like Humans?