9,038 research outputs found

    Remarkable Objects: Supporting Collaboration in a Creative Environment

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    In this paper, we report the results of a field trial of a Ubicomp system called CAM that is aimed at supporting and enhancing collaboration in a design studio environment. CAM uses a mobile-tagging application which allows designers to collaboratively store relevant information onto their physical design objects in the form of messages, annotations and external web links. The purpose of our field trial was to explore the role of augmented objects in supporting and enhancing creative work. Our results show that CAM was used not only used to support participants’ mutual awareness and coordination but also to facilitate designers in appropriating their augmented design objects to be explorative, extendable and playful supporting creative aspects of design work. In general, our results show how CAM transformed static design objects into ‘remarkable’ objects that made the creative and playful side of cooperative design visible

    Chance and Improbability

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    ‘Chance and Improbability’ is an article featuring in a peer-reviewed online journal, Flusser Studies. It focuses on the role of the improbable or unexpected in relation to art practice and discusses the impact of these concepts, drawing on the work of philosopher Vilém Flusser. Digital code and the visual representations it enables are now ubiquitous and the article attempts to excavate some of Flusser’s thinking in this respect and relate it to current practices in the field of art. The article discusses Flusser’s notion of the ‘technical image’ and that one’s role as an artist or cultural producer is to work against the tendency of machines to standardise and homogenise, to strive for the improbable as opposed to the probable. Drawing on the developing interest in Flusser’s work, it provides a resource for artist/scholars and features quotations from an unpublished Flusser manuscript, ‘Between the probable and the impossible’, from the Flusser Archive at Universität der Künste in Berlin (previously at Kunsthochschule für Medien, Cologne). The article also includes reflections on O’Riley’s bookwork, Accidental Journey

    Flusser Studies (Chance and Improbability)

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    This text discusses Flusser’s thinking regarding the ‘technical image’ in relation to a recent artist’s book, Accidental Journey. The book is nominally about the moon and astronomy, and contains images, factual and fictional texts, documents of my own and others’ research, travels, illustrations, scientific diagrams, and so on. Also presented is an excerpt from the book together with a selected quotation from an unpublished work by Flusser, ‘Between the probable and the impossible’, (Vilém Flusser Archiv, number 2723). Often with art it is not clear what goes into formulating and making a work. The research that feeds into the development of the work remains crucial but is often undisclosed. Acting as a repository for the unexpected, the book in this sense was an attempt to enable these things to see the light of day. Flusser’s thinking is important not only towards developing a critical understanding and formulation of theory and a reflection on the practical processes at work, but also in terms of the nature of research itself: that is, the potentialities that proliferate through looking and searching, which hint at a realm of the possible as opposed to the probable. The text is not intended to situate Flusser’s writing other than in terms of my own thinking and the processes that lead to making work, in whatever form that may take

    Current Challenges and Visions in Music Recommender Systems Research

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    Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

    Implementing feedback in creative systems : a workshop approach

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    One particular challenge in AI is the computational modelling and simulation of creativity. Feedback and learning from experience are key aspects of the creative process. Here we investigate how we could implement feedback in creative systems using a social model. From the field of creative writing we borrow the concept of a Writers Workshop as a model for learning through feedback. The Writers Workshop encourages examination, discussion and debates of a piece of creative work using a prescribed format of activities. We propose a computational model of the Writers Workshop as a roadmap for incorporation of feedback in artificial creativity systems. We argue that the Writers Workshop setting describes the anatomy of the creative process. We support our claim with a case study that describes how to implement the Writers Workshop model in a computational creativity system. We present this work using patterns other people can follow to implement similar designs in their own systems. We conclude by discussing the broader relevance of this model to other aspects of AI
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