Artificial intelligence (AI) is a tool that is used in numerous fields such as research, commercial enterprises, and everyday life yet the digital revolution is happening faster than the education and training professions can keep pace with and many teaching professionals have no clue about AI. AI technologies are not part of the curriculum in most fields of study. An interdisciplinary team led by Prof. Steffen Becker, Prof. Felix Fritzen, Prof. Steffen Staab, Prof. Stefan Wagner, and Jun.-Prof. Maria Wirzberger have established the Artificial Intelligence Software Academy (AISA) at the University of Stuttgart to address this issue. "What we're aiming to do," explains Wirzberger, head of the Teaching and Learning with Intelligent Systems Department and spokesperson for the Interchange Forum for Reflecting on Intelligent Systems, "is to address a gap that hasn't previously been bridged by providing bespoke AI expertise to people with no expertise in the field.”
” Wirzberger heads up the Academy's training department, which provides students with the opportunity to acquire additional qualifications or certificates in addition to their main degree program, whereby the focus is always on AI skills and software engineering (SE), which, in this case means designing and creating the requisite AI programs. The students then link this to applications in their field of expertise, explains Wirzberger.
Funding for the ASIA form the Baden-Württemberg ministry of science
The second pillar of the academy, which is coordinated by Prof. Steffen Staab, Head of the Analytic Computing Department at the Institute for Parallel and Distributed Systems (IPVS), is research. Prof. Stefan Wagner, Head of the Department of Empirical Software Engineering at the Institute of Software Engineering (ISTE), is responsible for the overall coordination of the AISA.
The Baden-Württemberg Ministry of Science, Research and the Arts has been funding the AISA since August 2021 and for an initial period through to the end of 2023 with 2.75 million euros. Among other things, the team are using the funding to create their own computer clusters and finance eleven doctoral studentships, whereby each doctoral researcher is supervised by several experts from different disciplines. As Wirzberger explains: "It is precisely through these special combinations that we are able to research interface topics", i.e. the connection between AI and SE as well as their technical application. Marijana Palalić's doctoral thesis, "Artificial Intelligence for Hybrid Manufacturing of the Future," is focusing on one such interface topic.
Her work is being supervised by an entire team consisting of Prof. Hans-Christian Möhring, head of the Institute for Machine Tools (IfW), an expert in additive and subtractive manufacturing technologies; Prof. Steffen Becker, head of the Software Quality and Architecture (SQA) Department, whose expertise is in model-driven software engineering and software architectures, and Jun.-Prof. Andreas Wortmann of the Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), who researches model-based development in production automation.
What we're aiming to do is to address a gap that hasn't previously been bridged by providing bespoke AI expertise to people with no expertise in the field.Jun. Prof. Dr. Maria Wirzberger
Doctoral degree studies in interface topics
"We are working on laser cladding with powdered metal," Palalić' explains. In this process, the heat source is a high-power laser, which "melts specific areas of the workpiece while simultaneously adding an inert gas mixed with fine metal powder, which also melts and bonds with the surface of the component, a process that is applied layer by layer." This process can be used to combine different materials in a single component or even to repair certain areas or parts. It also makes it possible to create geometries that it would be impossible to produce using traditional metalworking methods, whereby barely any waste is produced. Yet, there are some downsides: "It is almost always necessary to rework any component that has been manufactured using additive manufacturing by removing material, specifically through machining processes such as milling, drilling, and grinding," Palalić explains.
This hybrid production method places high demands on the process. In order to ensure that the components manufactured for such applications as aerospace or medical technology do what is expected of them, the individual steps in the process chain have to be precisely coordinated. However, as the researcher explains, there is still a great deal of uncertainty about the mechanical properties of these components due to the fact that the process of applying and removing material is extremely complex. "There is a huge number of correlating parameters and effects that still need to be studied and whose interactions cannot be described in analytical terms. This is why it is appropriate to use machine learning in this case, and not just to obtain information about what happened during a given process, but also to enable us to make predictions about the quality of the component that were previously impossible."
Palalić's ultimate goal is to develop a virtual model, or a kind of digital twin, of both the component and the manufacturing process, with a view to facilitating the optimization and monitoring of the process, e.g. by identifying errors. Her first step is to install sensors in the machine after which she uses SE to create an efficient software program. As Palalić' explains: "the machine learning algorithms need to be fed with processed sensor data." The third thing she has to do is to correctly select, parameterize, and then apply the algorithms for the particular machine learning AI method and then generate a digital model
AI literacy as a basic skill
Wirzberger emphasizes the need to qualify students from non-informatics disciplines in the fields of AI and SE and explains that: "If our goal is to properly equip the skilled workforce of the future, we need to incorporate AI into study programs across the board." Another special feature of the academy, he adds, is that doctoral students such as Palalić co-supervise the AISA seminars and advise students on AI or SE issues. "For example," says Wirzberger, "if a student would like to use an AI-powered algorithm in the context of a master's thesis and has questions about it, we're the ones who can provide assistance and feedback.” Increasingly, AI literacy is becoming a basic skill that everyone needs to master. I need to understand what drives these systems; how much trust I can place in them, and at what point would I need to apply particularly critical thought?"
It also enables us to make predicitions about the quality oft he component that were previously impossible.Marijana Palalic
In her doctoral thesis, which Wirzberger and Becker are jointly supervising, Nadine Koch wants to find out how this competence could be taught more effectively. "It's about developing AI didactics for people with no expertise in AI," Wirzberger explains. "How can I systematically and clearly communicate to non-AI professionals how to select a particular algorithm, recognize its advantages and disadvantages, and how it can be used and evaluated? Can I do it by using well-established forms from teaching/learning studies or computer science didactics? How should I adapt them?" Because once we have answers to these questions, even more skilled workers will soon be able to start their professional lives being AI-competent.
Author: Daniel Völpel