Körber-Prize 2019

Image: Bernhard Schölkopf also uses Machine Learning in astronomical applications, automatically evaluating the images of the powerful telescopes in the roof of the MPI.

»Crucial to the great advances in machine learning are above all the huge amounts of data called, in the US, Big Data.«   BERNHARD SCHÖLKOPF 

A support vector machine could, for example, be given the task of detecting the biologically important classes in segments of DNA, our genetic material. »As long as we only have a limited amount of training data, say a few thousand, the precision is very low. We humans would presumably only look at a couple of thousand at most. This would not be sufficient for us to recognize patterns. But with large amounts of data, e.g., 10 to 15 million, the precision increases. A support vector machine can then detect structures that a human would never be in a position to find.«

The great significance of big data can also be seen in the increased frenzy with which data are being collected by the larger IT corporations. Facebook has the world’s largest data collection of faces with over a billion—frequently from different perspectives—to which users have often also assigned names in group photographs. This immense data set is, for example, excellent material for training in order to create particularly precise AI software for facial recognition. 


Following the robotization of factories, AI systems will subsequently revolutionize office life. In the meantime, they have come to ›understand‹ the contents of documents without any difficulty and file them automatically. In a similar manner, they filter spam emails. The systems can even process simple insurance claims. In quality control in manufacturing they discover the smallest defects in a flash, defects that humans could easily overlook. In medicine they detect tumors on x-rays or CT images as accurately as human experts. The editorial offices of several well-known US newspapers and agencies such as the Associated Press even already let robot journalists prepare simple standard reports from the fields of business and sports.