Earlier this month, the German national newspaper WELT awarded its second “German AI Prize” to Bernhard Schölkopf, in recognition of his groundbreaking achievements in the field of artificial intelligence.
WELT awarded the 100,000 Euro AI Innovation prize to Schölkopf, who is director of the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, and an Amazon distinguished scientist, working part time in Amazon’s Tübingen lab.
Schölkopf has been conducting top-level research in the field of artificial intelligence for years. He is an internationally renowned AI researcher and one of the most influential figures in the German AI community. For example, his research within machine learning has more than 160,000 citations.
“Machine learning and causal inference are essential sub-areas of modern artificial intelligence,” said Schölkopf. “My research group develops algorithms that are fed with large data sets and learn through this training to independently recognize regularities in the data — just as a brain recognizes regularities from observations and draws conclusions. A machine often finds structures in large amounts of data that a human being would not find. With my research, I would like to contribute to the application of theoretical methods of machine learning, for example in medicine or astronomy.”
At Amazon, Schölkopf’s work focuses on the application of causality and machine learning methods to customer-oriented problems.
In an interview with Amazon Science last year, Schölkopf said he considers causality one of the most interesting conceptual developments affecting modern machine learning. “This is the main topic that I have been interested in for the last decade,” he said.
“Normal machine learning builds on correlations or other statistical dependences,” Schölkopf explained. “This is fine as long as the source of the data doesn't change. For example, if in the training set of an image recognition system, all cows are standing on green pasture, then it is fine for an ML system to use the green as a useful feature in recognizing cows, as long as the test set looks the same. If in the test set, the cows are standing on the beach, then such a purely statistical system can fail.
“More generally,” he said, “causal learning and inference attempts to understand how systems respond to interventions and other changes, and not just how to predict data that looks more or less the same as the training data.”
Asked about his reaction to winning the German AI Prize earlier this month, Schölkopf said, “This is a great honor and it really goes to the whole team. The prize money will allow us to further push the envelope in trying to understand causal learning.”
A selection committee comprising representatives from AI application and research, chaired by Hans-Christian “Chris” Boos, selected Schölkopf for the award.
