Putting AI to work: Knowledge transfer as a joint task
Putting knowledge about Artificial Intelligence (AI) into practice - that is a prerequisite for the future competitiveness of Germany as a center of innovation. AI research is already well positioned in Germany. However, there is a need to catch up when it comes to transferring AI knowledge into marketable AI-based applications. In a recent white paper, experts from Plattform Lernende Systeme show how the exchange of research results and application knowledge between science and companies can succeed and identify design options. They recommend promoting the training and recruitment of AI talent, cooperation between educational institutions and industry, and suitable framework conditions in companies and universities.
The transfer of knowledge from research to application promotes AI innovations and thus strengthens Germany's resilience and competitiveness. It circumscribes a process in which research institutions and companies exchange their knowledge, findings, skills and expertise. "Knowledge transfer is not a one-way street. German companies face very different challenges when it comes to the question of how processes and products can be optimized in terms of quality and efficiency using applications of AI. That's why it's not just a matter of bringing research results and potential AI solutions to companies, but also of taking the exciting topics from the companies to the universities. This two-way exchange is the basis for successful and economically effective knowledge transfer," says Carl-Helmut Coulon, Head of Future Manufacturing Concepts at INVITE and a member of the "Technological Enablers and Data Science" working group of Plattform Lernende Systeme. He is co-author of the white paper "Bringing AI into Application - A Joint Task for Universities, Research Institutions, Companies and Government" of Plattform Lernende Systeme.
Only a few companies use AI in their operations. They lack the necessary expertise in the technology. Companies also rarely have the necessary large volumes of data of sufficient quality on their own to implement successful business models with AI. In order to drive the use of AI in companies and at the same time enable practice-relevant AI research, close cooperation between companies and universities as well as research institutions is crucial. With regional digital hubs, AI competence centers and SME centers with AI trainers, Germany already has a good infrastructure for such collaborations. The authors of the white paper suggest strengthening cooperation between the players in the talent ecosystem at these regional AI hubs. Here, for example, companies can find the right expertise for their individual problems or researchers can test their AI solutions on real data. The white paper also names career and entrepreneurship centers as good places to start networking with students. In networks between companies, SMEs can share data and infrastructure and thus create the basis for the use of AI.
"Companies must not only see themselves as a producer of an AI product, but as an AI company that invests strategically in AI projects. The development of a positive corporate mentality with regard to AI is an important prerequisite for success, especially with regard to the transfer of knowledge through cooperation with research institutions. Companies need to recognize the benefits they can gain by networking with AI experts from the research community," said white paper co-author Markus Kohler, an AI expert and manager at SAP, and a member of the Technological Enablers and Data Science working group.
High demand for AI professionals
Large companies as well as SMEs need AI talent to translate research knowledge into practical AI applications. It is true that the number of students in computer science is rising in Germany and the range of AI and data science courses on offer is one of the most extensive in Europe. Nevertheless, the demand for AI specialists has not yet been adequately met. The authors recommend strengthening the practical relevance of computer science studies and integrating exciting AI topics from the business world, for example through competitions for solving concrete problems from companies.
The white paper envisages establishing courses on AI in the so-called domain sciences in order to counter the shortage of specialists. "It is important to have a basic technical and scientific understanding of AI in order to recognize its opportunities in one's own company and to be able to turn ideas into solutions in cooperation with AI and data science experts. For this reason, AI and data science methods must also become as natural tools in physics, engineering, and economics and social sciences as, for example, statistics, systems theory, or control engineering. In turn, AI and data science experts need to build up basic knowledge in the application domains in order to help shape custom-fit AI solutions for their companies," says co-author Wolfgang Ecker, Distinguished Engineer at Infineon Technologies and Professor of Electrical Engineering at the Technical University of Munich, as well as a member of the Technological Enablers and Data Science working group.
Further training of specialists in the company on AI is also recommended, for example in certificate courses at relevant universities. At the same time, companies must offer an optimal working environment to retain domestic and foreign AI talent.
About the white paper
The white paper "Bringing AI into Application - A Joint Task for Universities, Research Institutions, Companies and Government" was written by members of the working group "Technological Enablers and Data Science" of Plattform Lernende Systeme. It is available for free download.
Linda Treugut / Birgit Obermeier
Press and Public Relations
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