4 2 / 2025 // LEOPOLDINA / NEWS
“ Between the poles of depth of analysis and speed of reaction”
Leopoldina Members Thomas Lengauer and Klaus-Robert Müller organise the Annual Assembly
Significant progress has been made in research and development in the field of artificial intelligence in recent years. The Annual Assembly will focus on these developments and their consequences with contributions from all four classes of the Leopoldina.
Image: Siarhei | AdobeStock
Artificial intelligence( AI) has created many new opportunities in a very short space of time and simultaneously thrown up as many new questions. This year‘ s Annual Assembly, which will take place on 25 – 26 September in Halle( Saale)/ Germany, will address this development. The two scientific coordinators, Leopoldina Members Thomas Lengauer and Klaus-Robert Müller, provide an insight into the topic and the preparations.
How did you come to choose AI as the topic for the Leopoldina‘ s annual conference? Thomas Lengauer: This is the third annual conference that I have organized. In 2009, it was about computer models in science, and in 2019 we had a conference on the phenomenon of time in nature and culture. For me, this was and is a fundamental question of all sciences. This time the topic was a natural choice. For me, the impetus came from two recent technical developments: the language model ChatGPT and the solution of the protein folding prediction problem with the AlphaFold algorithm. I used to work on the protein problem myself – and even months before AlphaFold, I would not have thought that I would live to see the solution. These are not continuous developments, something completely new is being created and we still don‘ t really understand how it happens. We didn‘ t have to convince anyone at the Leopoldina that these breakthroughs were important. And, of course, AI is also on everyone‘ s mind in society in general. Klaus-Robert Müller: For me, one of the important breakthroughs was that AI models based on large amounts of data can now create new knowledge, for example in quantum chemistry or mathematics. The system learns something that nobody knew before, and this is happening more and more often. And with techniques like“ explainable AI,” you can actually watch the system learn.
Does this mean AI is outsmarting humans? Müller: Not at all. These are supporting tools. Mathematicians can use them