Machine learning is being hailed as the new savior. As the hype around artificial intelligence (AI) increases, trust is being placed in it to solve even the most complex of problems. Results from the lab back up these expectations. Detecting a Covid-19 infection using X-ray images or even speech, autonomous driving, automatic deepfake recognition — all of this is possible using AI under laboratory conditions. Yet when these models are applied in real life, the results are often less than adequate. Why is that? If machine learning is viable in the lab, why is it such a challenge to transfer it to real-life scenarios? And how can we build models that are more robust in the real world? This blog article scrutinizes scientific machine learning models and outlines possible ways of increasing the accuracy of AI in practice.

How secure is artificial intelligence (AI)? Does a machine perceive its environment in a different way to humans? Can an algorithm’s assessment be trusted? These are some of the questions we are exploring in the project “SuKI — Security for and with artificial intelligence”. The more AI is integrated into our everyday lives, the more important these questions become: When it comes to critical decisions — be it on the roads, in the financial sector or even in the medical sector — which are taken by autonomous systems, being able to trust AI is vital. As part of our ongoing SuKI project, we have now successfully deceived the state-of-the-art object recognition system YoloV3 [0].