In her laboratory at the German Aerospace Center, Elizabeth Robertson is tinkering with the inner workings of a computer that looks nothing like a conventional one. This computer is made of lasers, lenses, mirrors, and a small cell filled with cesium vapor. “The idea is for this computer to work with light someday, instead of electricity like a conventional PC,” says the young doctoral candidate, describing her work. “Eventually, AI algorithms could run significantly faster on a photonic processor like this one while also consuming far less energy than they do now.” Robertson’s project is part of a special graduate school called. Since 2018, the Helmholtz Einstein International Berlin Research School in Data Science has been training young scientists with the goal of equipping them to introduce cutting-edge data science methods into a broad range of specialist disciplines.
“The data sciences are become more and more important for research every year,” explains Uwe Ohler, a specialist in bioinformatics at the Max Delbrück Center for Molecular Medicine and one of the initiators of HEIBRiDS. This is how modern imaging processes create images at very high resolutions—which means the masses of data that need to be stored and processed are growing at an incredible rate as well. Data security and statistical methods are also taking on an increasingly prominent role. But what’s particularly striking is the advances in artificial intelligence. As computers get faster and faster and data volumes grow unabated, algorithms in neuronal networks and machine learning have now become extremely efficient. “In the past, we primarily used computers to automate human capabilities,” says Ohler. “These days, data-driven AI can uncover insights that humans would never detect in the data. This creates scope for entirely new ways of looking at scientific problems.”