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dc.contributor.authorDekker, Lauraen_US
dc.contributor.editorBerio, Daniel and Cruz, Pedro and Echevarria, Joseen_US
dc.date.accessioned2019-05-20T09:53:19Z
dc.date.available2019-05-20T09:53:19Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-084-0
dc.identifier.urihttps://doi.org/10.2312/exp.20191090
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/exp20191090
dc.description.abstractThe ''Expressive Machine'' is a series of interactive artworks which explore a machine's-eye view of the world. The machine- an assemblage of hardware and software-provokes sensual interaction with viewer-participants, playing with transduction across multiple modes: from touch to sound, to word, to vision, to taste, to uniquely machinic states with no particular human analogue. These stimuli are processed in various interpretations, elaborations, in a relatively unstructured ''data soup''. Asynchronous processes consume data from the soup. When trigger conditions for a particular expressive process are satisfied, the machine produces externalised outputs in various forms: sound, shift of attention, fragments of narrative, and so on. What can be considered as creativity arises as an emergent property-a serendipitous by-product of the machine working through its experiences, rather than an explicit creative process. To make this conceptual exploration possible, an adaptable, extensible, decentralised system is presented: its requirements, architecture and some implementations as interactive artworks. Each new site and context gives rise to a unique instantiation of the machine, as it explores and expresses its experiences.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectApplied computing
dc.subjectArts and humanities
dc.subjectComputing methodologies
dc.subjectNatural language processing
dc.subjectMachine learning
dc.subjectHardware
dc.subjectSensors and actuators
dc.titleEmergence in the Expressive Machineen_US
dc.description.seriesinformationACM/EG Expressive Symposium - Posters, Demos, and Artworks
dc.description.sectionheadersModeling Phenomena
dc.identifier.doi10.2312/exp.20191090
dc.identifier.pages13-16


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