Suchergebnisse

Manufacturing companies are facing an increasingly turbulent market - a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.

"International Relations, Big Data, and Artificial Intelligence" published on by Oxford University Press.

Einleitend beschreibt der Autor drei grundsätzliche Probleme der Human- und Sozialwissenschaften in der Theoriebildung und Computermodellierung: 1. Sozialwissenschaftliches Wissen existiert oft nur in qualitativer, nichtnumerischer Form. 2. In herkömmlichen Computermodellen versteckt sich das theoretische Wissen häufig in einem nichttheoretischen Code. 3. Schlüsse, die mittels dieser Computermodelle gezogen wurden, erweisen sich nur auf mathematischen Feld durchschau- und kritisierbar. Eine Lösung der Dilemmata erhofft man sich durch das Modellieren auf der Basis Künstlicher Intelligenz (KI). Der Autor beschreibt diese Basis und geht auf den Schlüsselbegriff der Wissensrepräsentation ein. Er unterscheidet zwei Wege, wie die KI-Modellierung für die Theoriebildung genutzt werden kann:die sogenannte theoriegeleitete Perspektive und die datenorientierte Perspektive. Anschließend stellt er einen gemischten Ansatz anhand von Gruppenprozessen vor. Der Autor kommt zu dem Schluss, dass KI-Techniken zur Verbesserung und Generierung neuer Theorien aus empirischen Daten genutzt werden können, auch wenn KI keine Revolution darstellt. (FR)

Artificial intelligence is an important innovation in the rapid development of modern Internet. In the 21st century, human beings have been continuously researching and exploring Internet information technology. All kinds of application forms of Internet informatization begin to appear in our life. The rapid change of technology brings a high upgrade rate of internet products. This marks the technological innovation of some traditional concepts and thinking methods. The development mode of artificial intelligence plus education is an important innovation after the deep development of artificial intelligence technology and the achievement of cross-industry application practice. Robots will be the brains of the future education process. This paper aims to clarify the development trend of the application of artificial intelligence in modern education by analyzing the innovation progress of the combination of artificial intelligence technology and contemporary education. This is of great significance for better use of the advantages of artificial intelligence to build a future-oriented high-tech education system.

This paper asks what skills migrants need to be able to deal with artificial intelligence (AI) technologies in a self-determined way in their everyday lives. We propose a conceptual framework to empirically identify migrant's awareness and perceptions of possible discrimination through AI. Following Bucher (2017, 40), we argue that by experiencing AI systems in their digital environments, people develop AI imaginaries that shape their attitudes, interactions, and practices with AI. We assume that experiences of discrimination evoke affects, feelings, and emotions that at first glance are not associated with AI technologies. The paper provides relevant research questions that address AI imaginaries. In addition to studying knowledge about and perceptions of AI, research should increasingly focus on users' attitudes towards AI, their evaluations of AI, and their feelings, emotions, and affects related to AI. Subsequently, we elaborate on dimensions of digital literacy based on these AI imaginaries. Finally, we will describe the digital skills that are necessary to confidently cope with discrimination by AI technologies.