Mathematics professor Sophie Langer: decrypt artificial intelligence!

Mathematics professor Sophie Langer: decrypt artificial intelligence!

Bochum, Deutschland - The world of artificial intelligence (AI) experiences significant changes through progress in mathematics. Sophie Langer, professor of mathematical statistics at the Ruhr University Bochum since April 2025, is intensively devoted to researching neuronal networks, a technology that is increasingly at the center of AI development. Langer emphasizes that neural networks that learn independently from extensive data are often perceived as a "black box"; The underlying learning processes are often incomprehensible. Your goal is to develop simplified models that make the functioning of these networks explainable. This "mammoth task" shows the gaping gap between theoretical mathematics and the practical application in the AI. Ruhr-Universität Bochum reports about the challenges in their research, and their wish, as a model for young women in mathematics act.

The beginnings of neural networks go back over 70 years when they were first designed as a method for researching the functioning of the human brain. Since then, the applications of this technology have expanded considerably and can be found in the automatic translation, facial recognition and in games such as Go and chess. Despite their successes, neural networks have difficulty solving precise mathematical tasks, especially in the case of symbolic math problems such as integral and differential equations. Spektrum describes a newer method of Facebook researchers Guillaume Lample and François Charton, the neuronal networks for solving this mathematical Use challenges by converting mathematical problems into a format that is understandable for machines. This enables the networks to find solutions that would overwhelm other mathematics software.

The importance of mathematical theory

While Sophie Langer is working on the preparation of neural networks at the theoretical level, the research of Lample and Charton shows that significant progress is also made on a practical level. Your method has managed to correctly solve the majority of the problems with 5000 test equations, especially for integrals, and in less than one second. It exceeded conventional mathematics programs in speed and accuracy and shows how neural networks have the potential to advance mathematics.

Nevertheless, there are critical voices. Experts point out that neural networks have not extensively tested all functional types and that they do not have a real mathematical cognition. Understanding how these networks actually work is revealed as a complex challenge. Langer himself notes that the scientific community is still facing the task of better understanding the mechanisms of neural networks. With her commitment, she not only wants to develop the theory, but also encourage other women to deal with mathematics without reservations and actively participate in science.

future of artificial intelligence and mathematics

The development and optimization of neural networks are therefore not only a scientific challenge, but also a social. Sophie Langer demands a stronger participation of women in mathematical disciplines and would like to overcome prejudices that often lead to the contributions of scientists not find the same recognition as those of their male colleagues. Her personal career, from studying to the doctorate to her professorship, is exemplary for the rise of a woman in an area dominated by men.

The link between mathematics and AI will continue to become more important in the future. Innovative approaches that the researchers pursue on Facebook could even lead to the automatic development of new theorems, which could change the interplay of mathematics and artificial intelligence. However, the questions of how neural networks manage these challenges and what role they will play in mathematical research remain exciting and offer a lot of space for future developments.