Körber-Prize 2019

Simon Guist, a doctoral candidate at the MPI Tübingen, trains an artificial arm to return virtual balls.

Schölkopf’s team at the MPI Tübingen is currently conducting research on algorithms that can also recog­nize causal relationships in data. This new, promising field of research is called causal inference. One of its goals is to make AI systems more robust against inter­ference. »If a 30 speed limit sign within the city limits has been altered to look like a 120 sign, then the AI system of a self-driving car must be able to conclude that the sign is to be ignored,« Schölkopf explains. Self-driving cars in the US have in fact already caused several deadly accidents.

AI technology works but is clearly not yet perfect. »A neural network can for example recognize a cow on most images without any difficulty,« Schölkopf says. »But it has problems if the image of a cow is shown on an ocean beach. This is a result of the training data, which usually show cows on meadows. The system is led astray, as it were, by the fact that the cow is in an inappropriate environment, and the system does not recognize the cow since it only pays attention to correlations, ignoring causality.« According to Schölkopf, future AI systems »should also understand causality: Thinking is, according to Konrad Lorenz, nothing but acting in an imagined space. The repre­sentations that we learn should reflect an understanding of how the world reacts to our actions. This goes beyond the statistical methods that are the foundation of the present methods.«

Mistakes also occur, for instance, in the automatic processing of online credit applications. Over and over again it happens that AI systems refuse credits although the borrower shows he is fully creditworthy. Experts believe that such mistakes will never be completely eliminated because different computer scientists often have differ­ent opinions as to which algorithm is best suited for which application. Furthermore, the sensors in autonomous vehicles can, for example, age or get dirty.


The direction of the newest trend in machine learning is for the systems not to be trained elaborately, some­times employing millions of data. On the contrary, they are to recognize patterns, structures, and rules com­pletely independently. Following supervised learning (with training) we now have unsupervised learning.