KI und Rhetorik: Trainingstool oder Langweiler?

Moderation: Kevin Schumacher, Schnitt: Thomas Heintz, Dauer: 1 h 5 min 7 sec, gedreht am 25.3.2025 am KIT Karlsruh

Paths and Ambient Spaces in Neural Loss Landscapes

Understanding the structure of neural network loss surfaces, particularly the emergence of low-loss tunnels, is critical for advancing neural network theory and practice. In this paper, we propose a novel approach to directly embed loss tunnels into the loss landscape of neural networks. Exploring the properties of these loss tunnels offers new insights into their length and structure and sheds light on some common misconceptions. We then apply our approach to Bayesian neural networks, where we improve subspace inference by identifying pitfalls and proposing a more natural prior that better guides the sampling procedure

Mapping UHI Risk for Climate-Resilient Street Design Using Mobile Mapping Data and AI-Based Data Analysis

Infolge des Klimawandels werden Hitzeperioden häufiger und intensiver, was insbesondere in Städten zu einer Überwärmung des Straßenraums führt. Erhöhte Gesundheitsrisiken für vulnerable Gruppen sowie eine Minderung der Aufenthalts- und Lebensqualität sind die Folgen. Für die Stadtplanung ergibt sich die Notwendigkeit, dem Urban-Heat-Island-(UHI-)Effekt durch geeignete Klimaanpassungsmaßnahmen zu begegnen. Bisherigen Ansätzen zur Lokalisierung überwärmungsgefährdeter Bereiche fehlt oft die Detailtiefe, um einen direkten Straßenbezug herzustellen, Ursachen zu analysieren und geeignete Anpassungsmaßnahmen im Straßenraum abzuleiten. In diesem Beitrag wird daher ein Ansatz vorgestellt, der die Daten eines Mobile-Mapping-Systems nutzt, um UHI-Risikobereiche im städtischen Straßennetz präzise zu kartieren und zu bewerten. Das Bewertungskonzept ist so ausgelegt, dass gezielt Maßnahmen zur Verbesserung des Mikroklimas empfohlen werden können

The phosphorus negotiation game (P-Game): first evaluation of a serious game to support science-policy decision making played in more than 20 countries worldwide

Environmental negotiations are complex, and conveying the interaction between science and policy in traditional teaching methods is challenging. To address this issue, innovative educational approaches like serious gaming and role-playing games have emerged. These methods allow students to actively explore the roles of different stakeholders in environmental decision-making and weigh for instance between sometimes conflicting UN Sustainable Development Goals or other dilemmas. In this work the phosphorus negotiation game (P-Game) is for the first time introduced. We present the initial quantitative and qualitative findings derived from engaging 788 students at various academic levels (Bachelor, Master, PhD, and Postdoc) across three continents and spanning 22 different countries. Quantitative results indicate that female participants and MSc students benefitted the most significantly from the P-Game, with their self-reported knowledge about phosphorus science and negotiation science/practice increasing by 71–93% (overall), 86–100% (females), and 73–106% (MSc students in general). Qualitative findings reveal that the P-Game can be smoothly conducted with students from diverse educational and cultural backgrounds. Moreover, students highly value their participation in the P-Game, which can be completed in just 2–3 h. This game not only encourages active engagement among participants but also provides valuable insights into the complex environmental issues associated with global phosphorus production. We strongly believe that the underlying methodology described here could also be used for other topics

Automating Customer Engagement

Mechanical and plant engineering, a key industry in the German economy, is facing challenges such as a shortage of skilled workers, international competitive pressure as well as increasing customer requirements. Small and medium-sized enterprises (SMEs) in this sector are therefore showing a growing interest in AI-supported technologies in service intending to improve customer engagement (CE) and ensure the long-term success of the company. Based on interviews with professionals and leaders from SMEs in the mechanical and plant engineering sector, this study examines their approaches to exploiting AI-based digitalization potential in after-sales service. The aim is to analyse and evaluate how these technologies promote CE and to identify the possible impact on value creation and corporate success. The focus is on questions relating to the transfer of performance factors to the customer, increasing demands on service quality, and the role of AI-based applications in after-sales service to ensure the success of the company. The findings highlight the positive impact of using AI software solutions on service quality, for example, and therefore on customer satisfaction. In addition, there is a shift of tasks from the company to the customer, relieving internal processes and changing interaction. The customer is actively involved in the service process, e.g. through self-service platforms and real-time data access. This co-creation approach creates a positive synergy, where the customer’s continuous use of platforms and access to data increases value on both sides and deepens CE. Despite the limited spread of AI solutions to date, companies are increasingly recognizing the strategic benefits of these technologies but face technological and organizational challenges that affect implementation

The Global Sanctions Data Base—Release 4: The Heterogeneous Effects of the Sanctions on Russia

This paper introduces the fourth release of the Global Sanctions Data Base (GSDB‐R4). Covering the period 1950–2023, it contains 1547 sanctions cases, including the recent ones against Russia. The GSDB‐R4 comes in two versions, a case‐specific and a dyadic version, both freely available upon request. To highlight one of the new features of the GSDB‐R4, we combine it with trade data until 2023 to study the effects of the sanctions on Russia's trade within an econometric gravity model. We find that, on average, the effects on trade between Russia and the sanctioning countries are negative and statistically significant, but relatively small. We also find that the effects are very heterogeneous across senders, including the EU members. Finally, our estimates identify the presence of a reduction in the direct bilateral trade costs in Russia's bilateral trade with India, China and Turkey, even after controlling for all possible general equilibrium effects. The implication is that such trade cost decreases may offset the effects of Western sanctions and even lead to net benefits for Russia

Challenges in calculating the AHI to diagnose sleep apnoea using deep learning and portable monitors

Automatic detection of the apnoea–hypopnoea index (AHI) using a portable monitor (PM) with artificial intelligence (AI) represents a significant challenge. The objective of this study was to examine factors that affect the performance of an AI algorithm that had been previously trained in calculating the AHI with polysomnography (PSG) data using signals collected by a PM

On the effectiveness of the sanctions on Russia

There has been an unprecedented increase in the number of sanctions imposed in world over the past 70 years, raising questions over their effectiveness. This column uses the fourth release of the Global Sanctions Database to quantify the impact of the 2022 sanctions on Russia on the country’s trade. The authors find that the sanctions have decreased Russia’s trade with sanctioning states but with very heterogeneous effects, especially across the EU. More importantly, however, they find evidence of significant trade liberalisation between Russia and third countries that have mitigated and may even eliminate the negative primary trade effects of the sanctions

Verhaltensökonomische Biases im Risikomanagement auf Mega-Infrastrukturprojekten

Die vorliegende Bachelorarbeit beschäftigt sich mit dem Einfluss verhaltensökonomischer Verzerrungen auf das Risikomanagement in Mega-Infrastrukturprojekten. Während klassische Risikoprozesse im Bauwesen stark technisch geprägt sind, zeigen zahlreiche empirische Studien, dass kognitive und strategische Denkfehler einen erheblichen Einfluss auf die Einschätzung und Steuerung von Risiken haben. Anhand theoretischer Grundlagen wie dem Zwei-Systeme-Modell nach Kahneman, der Prospect Theory sowie typischer Biases wie Overconfidence, Misrepresentation oder der Verfügbarkeitsheuristik wird aufgezeigt, wie menschliche Wahrnehmung und Entscheidungsfindung das Risikomanagement verzerren können. Auf dieser Basis wurde ein bias-sensibler Risikomanagementprozess entwickelt, der klassische Methoden wie die FMEA mit einem metakognitiven Kalibrierungswert kombiniert. Dieser erlaubt eine quantitative Bewertung der individuellen Einschätzungssicherheit und ermöglicht eine systematische Korrektur von Über- oder Unterschätzungen. Der Ansatz wurde im Rahmen einer Fallstudie auf das Projekt „City Link Anneberg–Skanstull Tunnel“ in Stockholm angewendet. Dabei wurden Risiken im Bereich der Spritzbetonarbeiten identifiziert, von 13 Projektbeteiligten bewertet und mithilfe des MKW kalibriert. Die Ergebnisse zeigen, dass die ursprünglichen Risikoeinschätzungen tendenziell vorsichtig waren und durch die Berücksichtigung von Unsicherheiten veranschaulicht werden konnten. Die Anwendung des entwickelten Prozesses führte zu einem realistischeren Risikoprofil und einem plausibleren Fertigstellungstermin. Die Ergebnisse unterstreichen die Bedeutung psychologischer Einflussfaktoren im Bauprojektmanagement und zeigen, wie bias-sensible Verfahren die Entscheidungsqualität in der Praxis verbessern können

Surrogate model using a u-net-convlstm architecture for a macroscopic pedestrian flow model

The aim of this paper is to demonstrate whether a deep neural network can replace a FEM simulation of a complex system and capture its non-linear dependencies on geometry. The system we are interested in is the macroscopic pedestrian flow in evacuation scenarios, modelled by two coupled partially differential equations (PDE) on 2D domains, possibly with holes, with mixed boundary conditions. Based on a literature review, we narrow down promising network architectures and study them with varying input variables. We develop a surrogate model based on a U-Net-ConvLSTM network. Our tests show the basic ability of such models to mimic the simulation, although further tests with larger training sets are needed