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Artificial Intelligence (AI) can potentially transform many aspects of modern society in various ways, including automation of tasks, personalization of products and services, diagnosis of diseases and their treatment, transportation, safety, and security in public spaces, etc. Recently, AI technology has been transforming the financial industry, offering new ways to analyse data and automate processes, reduce costs, increase efficiency, and provide more personalized services to customers. However, it also raised important ethical and regulatory questions that need to be addressed by the industry and society as a whole. The aim of the Erasmus+ project Transversal Skills in Applied Artificial Intelligence - TSAAI (KA220-HED - Cooperation Partnerships in higher education) has been to establish a training platform that will incorporate teaching guidelines based on a curriculum covering different areas of application of AI technology. In this work, we will focus on applying AI models in the financial and insurance sectors.
Harnessing the overall benefits of the latest advancements in artificial intelligence (AI) requires the extensive collaboration of academia and industry. These collaborations promote innovation and growth while enforcing the practical usefulness of newer technologies in real life. The purpose of this article is to outline the challenges faced during cross-collaboration between academia and industry. These challenges are also inspected with the help of an ongoing project titled “Quality Assurance of Machine Learning Applications” (Q-AMeLiA), in which three universities cooperate with five industry partners to make the product risk of AI-based products visible. Further, we discuss the hurdles and the key challenges in machine learning (ML) technology transformation from academia to industry based on robustness, simplicity, and safety. These challenges are an outcome of the lack of common standards, metrics, and missing regulatory considerations when state-of-the-art (SOTA) technology is developed in academia. The use of biased datasets involves ethical concerns that might lead to unfair outcomes when the ML model is deployed in production. The advancement of AI in small and medium sized enterprises (SMEs) requires more in terms of common tandardization of concepts rather than algorithm breakthroughs. In this paper, in addition to the general challenges, we also discuss domain specific barriers for five different domains i.e., object detection, hardware benchmarking, continual learning, action recognition, and industrial process automation, and highlight the steps necessary for successfully managing the cross-sectoral collaborations between academia and industry.
Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users' expectations. Robots' appearance impacts users' behaviors and attitudes towards them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers' perceptions of SARs' roles and appearance in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. The key insight is that although Israeli and German designers share similar perceptions of visual qualities for most of the robotics roles, we found differences in the perception of the COVID-19 officer robot's role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users' perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.
As a reaction to the increasing market dynamics and complex requirements, today’s products need to be developed quickly and customized to the customer’s individual needs. In the past, CAD systems are mainly used to visualize the model that the product designer creates. Generative Design shifts the task of the CAD program by actively participating in the shaping process. This results in more design options and the complexity of the shapes and geometries increases significantly. This potential can be optimally exploited through the combination of Generative Design with Additive Manufacturing (AM). Artificial intelligence and the input of target parameters generate geometries, for example, by creating material for stressed areas, which in turn develops biomorphic shapes and thus significantly reduces the consumption of resources. This contribution aims at the evaluation of existing applications in CAD systems for generative design. Special attention is paid to the requirements in design education and easy access for students. For this purpose, three representative CAD systems are selected and analyzed with the help of a comprehensive example of mass reduction. The aim is to perform an individual result analysis in order to assess the application based on various criteria. By using different materials, the influence of the material for the generation is investigated by comparing the material distribution. By comparing the generated models, differences of the CAD systems can be identified and possible fields of application can be presented. By specifying the manufacturing parameters for the generation of the models, the feasibility of AM can be guaranteed without having to modify the results. The physical implementation of the example by means of Fused Deposition Modeling demonstrates this in an exemplary way and examines the interface of the Generative Design and AM. The results of this contribution will enable an evaluation of the different CAD systems for Generative Design according to technical, visual and economic aspects.
Should social robots become part of our society?
Embedded in an exciting crime story, the science comic by Oliver Korn and Jonas Grund conveys the current state and outlook of science on social robots.
The story takes place in the near future: In an international project, scientists are researching the requirements for social robots for the health sector. Shortly before the prototype of a nursing robot is used in a field study, it disappears without a trace. In her first major case, the young inspector Kira embarks on a journey into the world of science. In the process, she learns a lot about social robots, AI and the world of international scientific cooperation. Again and again, critical voices are heard: anti-robot activists protest against automation and a dwindling of humanity and empathy. Even in the Commissioner's family, the possible care of elderly people by robots is controversially discussed.
The overarching goal is to build knowledge across all age groups so that the advantages and disadvantages of these new technologies can be discussed controversially but knowledgeably. "Social Robots - a Science Comic" is a contribution to an informed discussion in the fields of robotics, artificial intelligence, ethics and politics and is thus also suitable as a teaching and learning tool.
The comic was conceived and designed for young people and adults. In particular, however, for those who have hardly come into contact with social robots and artificial intelligence so far. Another target group are people working in the health care sector, because the care and nursing of elderly people are considered to be one of the most important areas of application for social robots in the future.
DINA4 portrait format, hardcover thread stitching, published in German and English. Self-published by the Affective & Cognitive Institute (ACI), Offenburg University.