Universitätspublikationen
Refine
Year of publication
Document Type
- Part of a Book (107) (remove)
Language
- English (107) (remove)
Has Fulltext
- yes (107)
Is part of the Bibliography
- no (107)
Keywords
- Social Interaction (3)
- Aufsatzsammlung (2)
- Christentum (2)
- Christianity (2)
- Digitalisierung (2)
- Financial literacy (2)
- Heranwachsender (2)
- Herding (2)
- Herstellung (2)
- Indonesia (2)
Institute
- Sprach- und Kulturwissenschaften (39)
- Medizin (8)
- Informatik (7)
- Wirtschaftswissenschaften (6)
- Kulturwissenschaften (5)
- Cornelia Goethe Centrum für Frauenstudien und die Erforschung der Geschlechterverhältnisse (CGC) (3)
- Exzellenzcluster Die Herausbildung normativer Ordnungen (3)
- Gesellschaftswissenschaften (3)
- Biochemie und Chemie (2)
- Geowissenschaften (2)
The digitalisation of communication started as early as the 1980s. With the rise of the internet in the mid-90s the digitalisation process intensified; then it took on another dimension with the spread of social media and smartphones in the mid noughties. These new technologies are providing new possibilities that are unveiling, or rather, strengthening societal trends. What’s more, traditional forms of organisation are also being transformed at breakneck speed. This publication provides an overview of both developments: On the one hand we have societal developments such as the blurring of boundaries between real and digital worlds, constant connectivity, fake news, and social media outrage. On the other, we have the effects on traditional media, the workplace, schools, non-governmental organisations and sports. ...
In what follows, I will present a condensed and non-exclusive list of the five most important problem domains in the development and implementation of Artificial Intelligence (AI), each with practical recommendations.
The first problem domain to be examined is the one which, in my view, is constituted by those issues with the smallest chances of being resolved. It should therefore be approached in a multi-layered process, beginning in the European Union (EU) itself.
"Artificial Intelligence (AI) is the future. [...] Whoever leads in AI will rule the world" (Russia Today, 2018). This was the central message that President Vladimir Putin conveyed to more than one million Russian school students in September 2017. He also promised to ensure that Russian knowledge of AI would benefit the world. However, the competition in this field is already playing itself out globally. Besides Russia, the USA and China are already in the race, with China, for example, having recently published an ambitious AI strategy, namely the "New Generation Artificial Intelligence Development Plan" (Webster et al., 2018). This document predicts China’s world leadership in the AI field as soon as 2030. The EU and several other countries – among them Germany in the autumn of 2018 - have followed suit with their own AI strategies. ...
In IT security today, the usage of AI is already established in multiple domains. SPAM detection is a well-known example where support vector machines try to distinguish wanted from unwanted emails. Author attribution combines natural language forensics and machine learning. Deep learning helps in identifying illicit images and has improved malware detection as well as network intrusion detection. ...
The thought of using Artificial Intelligence (AI) and algorithmic decisionmaking (ADM) processes in our daily lives makes many of us feel insecure. Most consumers see more risks than opportunities, an attitude brought about by the black-box nature of algorithms and AI. When an organisation or public authority makes a decision supported by an algorithm, one can feel that one is at the algorithm’s mercy, finding it incomprehensible. Widespread consumer distrust of AI and ADM processes will make it difficult to improve their societal acceptance and therefore make it challenging to apply them in the business sector and in policy-making. Without trust on the consumer side, there can be no progress.
Artificial Intelligence (AI) will be one of the key technologies driving the future competitiveness of numerous industries. However, the term "AI" is defined in a variety of ways. AI could be understood as an umbrella term for technologies and systems that carry out tasks otherwise only executable with human intelligence. This requires specific skills that fall into the broad categories of "Sense", "Comprehend", "Act" and "Learn". Through machine learning, modern AI systems can be trained to adapt to changes in their environment, self-optimise and hence achieve better results than earlier versions of AI systems that were based on clearly defined, pre-programmed rules. Based on AI methods, rational and autonomous agents can be developed that collect and analyse relevant information from their environments, come to optimal conclusions based on certain performance parameters and eventually perform physical actions (e.g. robotics) or virtual actions (e.g. chat bots). Machine learning algorithms ensure that the information base of the system is continuously updated so that performance of the system is optimised in an iterative process.
Artificial intelligence (AI)1, together with big data, is the driving force behind the ever-accelerating digital revolution. AI has what it takes to call into question our fundamental concepts and processes of political, social, economic etc. order (Macron, 2018; Zuboff, 2018), and the law will not be spared. Therefore, all societal actors (inter alia from politics, the economy, legal practice and academia) must take responsibility for the crucial twin tasks of determining the right, balanced relationship between AI and the law, and even to hybridise them. ...
According to a survey by the Institute for Management and Economic Research (manager seminars, September 2018), 41% or almost half of those respondents over 60, considered it unlikely that they would be affected by Artificial Intelligence (AI) in the workplace. On the other hand, younger respondents more realistically estimated that significant AI-related changes would occur in their workplace within the next five years, not only in production and data analysis, but also in customer service and office practices across the board. ...
Policy research deals with the policy cycle encompassing problem definition, and policy development, implementation, enforcement and evaluation for different policy domains. On the one hand, this research field empirically describes and analyses how the phases of the policy cycle are processed by relevant social actors in interaction with industry, the media and civil society. On the other hand, policy research is concerned with the issue itself, i.e. the reasons for success and failure of policies that have or will run through the process. Here, the research field offers scientific policy advice including exante evaluation and assessments of potential futures, options, developments, and scenarios for policy domains to inform political debates and decisions. It is this latter function AI already has and will further influence policy research. ...
In April 2018 the European Commission announced its holistic approach to Artificial Intelligence (AI) based on the following three pillars: first, to boost financial support and encourage uptake by the public and private sectors in order to reach investments in AI-related research and innovation by at least 20 billion Euros by the end of 2020. The second pillar aims at preparing for socio-economic changes in terms of the upcoming transformation of the labour market. Finally, the European Commission will ensure an appropriate ethical and legal framework by developing AI ethics guidelines and providing guidance on the interpretation of the Product Liability directive. ...
Editorial : economic competence and financial literacy of young adults – status and challenges
(2016)
In modern society, the ability to deal with financial and economic matters is becoming increasingly important. This is true for both professionals – e.g., in the investment and banking sectors – and for individuals responsible for managing their financial and economic affairs in everyday life (Aprea et al., in press). This ability is generally described as economic competence, economic literacy or financial literacy. Despite the importance of these constructs, there is still a lack of clarity regarding the exact definitions, and specifically, which components they cover in detail. Furthermore, the terms economic competence and financial literacy are only loosely coupled. Economic competence is usually considered to be more comprehensive than financial literacy. However, recent research on financial literacy has followed a broader approach as well. ...
The necessity for well-founded teacher education in economics – findings from curriculum analyses
(2016)
Everybody has to make daily decisions requiring a good understanding of political and economic systems to manage and design our life but also to react on changes in these systems. Already in the early stages of adulthood, individuals need to decide on which job to choose, which party to vote for or on what money to spend on. For all these activities economical knowledge is necessary, which usually derives from economic education taught in schools in several subjects. ...
We propose and create a new data model for learning specific environments and learning analytics applications. This is motivated from the experience in the Fiber Bundle Data Model used for large - time and space dependent - data. Our proposed data model integrates file or stream-based data structures from capturing devices more easily. Learning analytics algorithms are added directly to the data, and formulation of queries and analytics is done in Python. It is designed to improve collaboration in the field of learning analytics. We leverage a hierarchical data structure, where varying data is located near the leaves. Abstract data types are identified in four distinct pathways, which allow storing most diverse data sources. We compare different implementations regarding its memory footprint and performance. Our tests indicate that LeAn Bundles can be smaller than a naïve xAPI export. The benchmarks show that the performance is comparable to a MongoDB, while having the benefit of being portable and extensible.
Students of computer science studies enter university education with very different competencies, experience and knowledge. 145 datasets collected of freshmen computer science students by learning management systems in relation to exam outcomes and learning dispositions data (e. g. student dispositions, previous experiences and attitudes measured through self-reported surveys) has been exploited to identify indicators as predictors of academic success and hence make effective interventions to deal with an extremely heterogeneous group of students.
This volume contains the papers presented at the First International Workshop on Rewriting Techniques for Program Transformations and Evaluation (WPTE 2014) which was held on July 13, 2014 in Vienna, Austria during the Vienna Summer of Logic 2014 (VSL 2014) as a workshop of the Sixth Federated Logic Conference (FLoC 2014). WPTE 2014 was affiliated with the 25th International Conference on Rewriting Techniques and Applications joined with the 12th International Conference on Typed Lambda Calculi and Applications (RTA/TLCA 2014).
In recent years, interest in the environmental occurrence and effects of microplastics (MPs) has shifted towards our inland waters, and in this chapter we provide an overview of the issues that may be of concern for freshwater environments. The term ‘contaminant of emerging concern’ does not only apply to chemical pollutants but to MPs as well because it has been detected ubiquitously in freshwater systems. The environmental release of MPs will occur from a wide variety of sources, including emissions from wastewater treatment plants and from the degradation of larger plastic debris items. Due to the chemical makeup of plastic materials, receiving environments are potentially exposed to a mixture of micro- and nano-sized particles, leached additives, and subsequent degradation products, which will become bioavailable for a range of biota. The ingestion of MPs by aquatic organisms has been demonstrated, but the long-term effects of continuous exposures are less well understood. Technological developments and changes in demographics will influence the types of MPs and environmental concentrations in the future, and it will be important to develop approaches to mitigate the input of synthetic polymers to freshwater ecosystems.