Editorial 1 Tribologie + Schmierungstechnik · volume 71 · issue 4/ 2024 DOI 10.24053/ TuS-2024-0017 The Nobel Prize in Chemistry 2024 will be awarded with one half to David Baker from the University of Washington, Seattle, WA, USA and the Howard Hughes Medical Institute, USA “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper from Google DeepMind, London, UK “for protein structure prediction”. The press release states that “Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors” and ”In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.” The 2024 Nobel Prize for Chemistry is a very good example for a scientific use of Information Technology. The limits of perfection in humanities subjects lies in terms of adapting to individual contexts. In contrast, the social perception among many students, teachers, parents and even politicians is that such programs create “perfect” texts that could be used directly. However, AI always only elevates the average to the norm. AI-produced texts must be understood and checked for accuracy. In science we generate and use referenced data. Having experienced the change to a well-known company operating software myself led to the conclusion that if unreferenced data go in, no one can expect that rock solid data come out. In my first job in the industry, I developed a little computer program that could predict nicotine and tar levels from cigarettes for a fixed tobacco blend by varying physical properties of components. It was a design tool to reduce laboratory work, but at the end the taste of the smoke with its over 3000 chemicals could not be predicted. Reducing laboratory work was my next challenge when entering the lubricant industry. At the end of the ‘80ties then called expert systems should be used to develop lubricant formulations, a challenge that decades later has not been solved. In the chemical industry formulating robots are used, however I have not heard of their use for blending lubricants. Terms like machine learning, simulating rubbing surfaces and using finite element analysis for material fatigue are researched since years and will be used in the future. Thus, AI will find its way to us as well and remember Tribology is everywhere. Your editor in chief Manfred Jungk Artificial Intelligence wins Nobel Prize!