Refine
Document Type
- Article (6)
- Part of a Book (4)
- Book (1)
- Conference Proceeding (1)
- Report (1)
Has Fulltext
- yes (13)
Keywords
- Künstliche Intelligenz (13) (remove)
Publicationstate
- Veröffentlichungsversion (7)
- Zweitveröffentlichung (2)
- Postprint (1)
Reviewstate
- Peer-Review (5)
- (Verlags)-Lektorat (4)
Publisher
- Narr (3)
- Leibniz-Institut für Deutsche Sprache (IDS) (2)
- Sage (1)
- Schmidt-Römhild (1)
- Springer (1)
- Springer Nature (1)
- Syddansk Universitet (1)
- Technische Informationsbibliothek (1)
- University of Oulu (1)
- de Gruyter (1)
Computational language models (LMs), most notably exemplified by the widespread success of OpenAI's ChatGPT chatbot, show impressive performance on a wide range of linguistic tasks, thus providing cognitive science and linguistics with a computational working model to empirically study different aspects of human language. Here, we use LMs to test the hypothesis that languages with more speakers tend to be easier to learn. In two experiments, we train several LMs—ranging from very simple n-gram models to state-of-the-art deep neural networks—on written cross-linguistic corpus data covering 1293 different languages and statistically estimate learning difficulty. Using a variety of quantitative methods and machine learning techniques to account for phylogenetic relatedness and geographical proximity of languages, we show that there is robust evidence for a relationship between learning difficulty and speaker population size. However, contrary to expectations derived from previous research, our results suggest that languages with more speakers tend to be harder to learn.
The proposed contribution will shed light on current and future challenges on legal and ethical questions in research data infrastructures. The authors of the proposal will present the work of NFDI’s section on Ethical, Legal and Social Aspects (hereinafter: ELSA), whose aim is to facilitate cross-disciplinary cooperation between the NFDI consortia in the relevant areas of management and re-use of research data.
We are witnessing an emerging digital revolution. For the past 25–30 years, at an increasing pace, digital technologies—especially the internet, mobile phones and smartphones—have transformed the everyday lives of human beings. The pace of change will increase, and new digital technologies will become even more tightly entangled in human everyday lives. Artificial intelligence (AI), the Internet of Things (IoT), 6G wireless solutions, virtual reality (VR), augmented reality (AR), mixed reality (XR), robots and various platforms for remote and hybrid communication will become embedded in our lives at home, work and school.
Digitalisation has been identified as a megatrend, for example, by the OECD (2016; 2019). While digitalisation processes permeate all aspects of life, special attention has been paid to its impact on the ageing population, everyday communication practices, education and learning and working life. For example, it has been argued that digital solutions and technologies have the potential to improve quality of life, speed up processes and increase efficiency. At the same time, digitalisation is likely to bring with it unexpected trends and challenges. For example, AI and robots will doubtlessly speed up or take over many routine-based work tasks from humans, leading to the disappearance of certain occupations and the need for re-education. This, in turn, will lead to an increased demand for skills that are unique to humans and that technologies are not able to master. Thus, developing human competences in the emerging digital era will require not only the mastering of new technical skills, but also the advancement of interpersonal, emotional, literacy and problem-solving skills.
It is important to identify and describe the digitalisation phenomena—pertaining to individuals and societies—and seek human-centric answers and solutions that advance the benefits of and mitigate the possible adverse effects of digitalisation (e.g. inequality, divisions, vulnerability and unemployment). This requires directing the focus on strengthening the human skills and competences that will be needed for a sustainable digital future. Digital technologies should be seen as possibilities, not as necessities.
There is a need to call attention to the co-evolutionary processes between humans and emerging digital technologies—that is, the ways in which humans grow up with and live their lives alongside digital technologies. It is imperative to gain in-depth knowledge about the natural ways in which digital technologies are embedded in human everyday lives—for example, how people learn, interact and communicate in remote and hybrid settings or with artificial intelligence; how new digital technologies could be used to support continuous learning and understand learning processes better and how health and well-being can be promoted with the help of new digital solutions.
Another significant consideration revolves around the co-creation of our digital futures. Important questions to be asked are as follows: Who are the ones to co-create digital solutions for the future? How can humans and human sciences better contribute to digitalisation and define how emerging technologies shape society and the future? Although academic and business actors have recently fostered inclusion and diversity in their co-creation processes, more must be done. The empowerment of ordinary people to start acting as active makers and shapers of our digital futures is required, as is giving voice to those who have traditionally been silenced or marginalised in the development of digital technology. In the emerging co-creation processes, emphasis should be placed on social sustainability and contextual sensitivity. Such processes are always value-laden and political and intimately intertwined with ethical issues.
Constant and accelerating change characterises contemporary human systems, our everyday lives and the environment. Resilience thinking has become one of the major conceptual tools for understanding and dealing with change. It is a multi-scalar idea referring to the capacity of individuals and human systems to absorb disturbances and reorganise their functionality while undergoing a change. Based on the evolving new digital technologies, there is a pressing need to understand how these technologies could be utilised for human well-being, sustainable lifestyles and a better environment. This calls for analysing different scales and types of resilience in order to develop better technology-based solutions for human-centred development in the new digital era.
This white paper is a collaborative effort by researchers from six faculties and groups working on questions related to digitalisation at the University of Oulu, Finland. We have identified questions and challenges related to the emerging digital era and suggest directions that will make possible a human-centric digital future and strengthen the competences of humans and humanity in this era.
The paper presents research results emerging from the analysis of Intelligent Personal Assistants (IPA) log data. Based on the assump-tion that media and data, as part of practice, are produced and used cooperatively, the paper discusses how IPA log data can be used to analyze (1) how the IPA systems operate through their connection to platforms and infrastructures, (2) how the dialog systems are de-signed today and (3) how users integrate them into their everyday social interaction. It also asks in which everyday practical contexts the IPA are placed on the system side and on the user side, and how privacy issues in particular are negotiated. It is argued that, in order to be able to investigate these questions, the technical-institutional and the cultural-theoretical perspective on media, which is common in German media linguistics, has to be complemented by a more fun-damental, i.e. social-theoretical and interactionist perspective.
Der Mythos „Künstliche Intelligenz“ wird besonders von der sogenannten „transhumanistischen“ Community im Silicon Valley propagiert, deren Vertreter wie der Physiker Ray Kurzweil davon ausgehen, dass wir in spätestens 30 Jahren mit KIs kommunizieren könnten, wie mit einem Menschen (Kurzweil 2005). Saudi Arabien hat 2017 bereits dem anthropomorphen Roboter mit Sprachinterface Sophia die Staatsbürgerschaft zugesprochen (Arab News 2017). Künstliche Intelligenzen wie Apples Assistenzsystem Siri oder Amazons Alexa halten derzeit Einzug in unseren Alltag. Chatbots und Social-Bots wie der Twitter-Bot Tay nehmen Einfluss auf öffentliche Diskurse und interaktives Spielzeug mit Dialogfunktion führt bereits unsere Jüngsten an die Interaktion mit dem artifiziellen Gegenüber heran. Hier entsteht eine völlig neue Form der Dialogizität, die wir aus linguistischer Perspektive noch kaum verstehen. Unabhängige Studien zur Mensch-Maschine-Interaktion stellen also ein großes Desiderat dar.
Situiertheit
(1993)
We taught a humanoid robot a number of different actions involving a number of different objects (e.g., touching a green object, moving a red object etc.) alongside a number of simplified linguistic labels for these behaviours (e.g., ‘touch-green’, ‘move-red’ etc.). The robot managed to learn the associations between the behaviours and their linguistic labels, and it succeeded in recognising the compositional structure of the behaviours and their associated linguistic descriptions (ACTION/VERB+OBJECT/NOUN). Moreover, it was able to generalise the learned instructions to novel, previously untrained action+object-combinations (e.g., touch-red). This corresponds to the task of learning and decomposing so-called ‘holophrases’ in early child language acquisition.
Co-development of action, conceptualization and social interaction mutually scaffold and support each other within a virtuous feedback cycle in the development of human language in children. Within this framework, the purpose of this article is to bring together diverse but complementary accounts of research methods that jointly contribute to our understanding of cognitive development and in particular, language acquisition in robots. Thus, we include research pertaining to developmental robotics, cognitive science, psychology, linguistics and neuroscience, as well as practical computer science and engineering. The different studies are not at this stage all connected into a cohesive whole; rather, they are presented to illuminate the need for multiple different approaches that complement each other in the pursuit of understanding cognitive development in robots. Extensive experiments involving the humanoid robot iCub are reported, while human learning relevant to developmental robotics has also contributed useful results.
Disparate approaches are brought together via common underlying design principles. Without claiming to model human language acquisition directly, we are nonetheless inspired by analogous development in humans and consequently, our investigations include the parallel co-development of action, conceptualization and social interaction. Though these different approaches need to ultimately be integrated into a coherent, unified body of knowledge, progress is currently also being made by pursuing individual methods.