Alle Publikationen
2021
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(2021): Theory and Practice of Sociosensitive Systems (working title). tba
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Bellon, Jacqueline; Gransche, Bruno; Nähr-Wagener, Sebastian (Hg.) (2021): Soziale Angemessenheit - Forschung zu Kulturtechniken des Verhaltens. Wiesbaden: Springer
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(2021) : Simondon, Language and Technology In: Guillaume, Astrid; Kurts‐Wöste, Lia (Hg.): MAKING SENSE, MAKING SCIENCE: [S.l.]: ISTE LTD, S. 35-43
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(2021) : Soziale Angemessenheit: eine Problem-Exposition aus wissensanalytischer Sicht In: Bellon, Jacqueline; Gransche, Bruno; Nähr-Wagener, Sebastian (Hg.): Soziale Angemessenheit - Forschung zu Kulturtechniken des Verhaltens: Wiesbaden: Springer
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(2021) : Soziale Normen - Die Perspektive der Spieltheorie In: Bellon, Jacqueline; Gransche, Bruno; Nähr-Wagener, Sebastian (Hg.): Soziale Angemessenheit - Forschung zu Kulturtechniken des Verhaltens: Wiesbaden: Springer
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Guillaume, Astrid; Kurts‐Wöste, Lia (Hg.) (2021): MAKING SENSE, MAKING SCIENCE. [S.l.]: ISTE LTD
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(2021): Sozialverhalten. Grundlagen, Clinical Reasoning und Intervention im Kindes- und Jugendalter. Wiesbaden: Springer, Springer Fachmedien Wiesbaden GmbH (Essentials (Springer))Keywords: Sozialverhalten
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(2021): Moral principles and social values. London: Routledge (Routledge library editions. Ethics)
Abstract: Originally published in 1987, this book discusses how matters of fact influence moral judgments and also how the judgments themselves influence facts. It demonstrates that ethics is a practical subject affecting our moral assessment of inter-personal behaviour and the conduct of public affairs. It is designed as in introduction to moral philosophy for first-year undergraduates and provides an excellent basis for further study as well as serving as a valuable background text for those whose primary interests are in law, politics, sociology, social history and education.
2020
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(2020): Repetitive Robot Behavior Impacts Perception of Intentionality and Gaze-Related Attentional Orienting. In: Frontiers in Robotics and AI 7. DOI: 10.3389/frobt.2020.565825
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(2020): On the nature and origins of cognition as a form of motivated activity. In: Adaptive Behavior 28 (2), S. 89-103. DOI: 10.1177/1059712318824325
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(2020): Dissipative systems and living bodies. In: Adaptive Behavior 28 (1), S. 47-48. DOI: 10.1177/1059712319841306
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(2020): Human-robot interaction. An introduction. Cambridge; New York, NY; Port Melbourne: Cambridge University Press
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(2020) : Ein Fallbeispiel Integrierter Forschung: Das Projekt poliTE – Soziale Angemessenheit für Assistenzsysteme In: Gransche, Bruno; Manzeschke, Arne (Hg.): Das geteilte Ganze: Horizonte Integrierter Forschung für künftige Mensch-Technik-Verhältnisse: 1st ed. 2020: Wiesbaden, Germany: Springer VS, S. 263-283
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(2020): Language Models are Few-Shot Learners. Online verfügbar unter https://arxiv.org/pdf/2005.14165
Abstract: Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.
Keywords: Computation and Language (cs.CL) -
(2020) : GPT-3 and General Intelligence . Online verfügbar unter https://dailynous.com/2020/07/30/philosophers-gpt-3/#chalmers
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(2020): Attribution of intentional agency towards robots reduces one's own sense of agency. In: Cognition 194. DOI: 10.1016/j.cognition.2019.104109
DOI: https://doi.org/10.1016/j.cognition.2019.104109 Abstract: In the presence of others, sense of agency (SoA), i.e. the perceived relationship between our own actions and external events, is reduced. The present study aimed at investigating whether the phenomenon of reduced SoA is observed in human-robot interaction, similarly to human-human interaction. To this end, we tested SoA when people interacted with a robot (Experiment 1), with a passive, non-agentic air pump (Experiment 2), or when they interacted with both a robot and a human being (Experiment 3). Participants were asked to rate the perceived control they felt on the outcome of their action while performing a diffusion of responsibility task. Results showed that the intentional agency attributed to the artificial entity differently affect the performance and the perceived SoA on the outcome of the task. Experiment 1 showed that, when participants successfully performed an action, they rated SoA over the outcome as lower in trials in which the robot was also able to act (but did not), compared to when they were performing the task alone. However, this did not occur in Experiment 2, where the artificial entity was an air pump, which had the same influence on the task as the robot, but in a passive manner and thus lacked intentional agency. Results of Experiment 3 showed that SoA was reduced similarly for the human and robot agents, threby indicating that attribution of intentional agency plays a crucial role in reduction of SoA. Together, our results suggest that interacting with robotic agents affects SoA, similarly to interacting with other humans, but differently from interacting with non-agentic mechanical devices. This has important implications for the applied of social robotics, where a subjective decrease in SoA could have negative consequences, such as in robot-assisted care in hospitals.
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(2020): Facial expressions of emotion states and their neuronal correlates in mice. In: Science 368 (6486), S. 89-94. DOI: 10.1126/science.aaz9468
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(2020): At first sight: robots’ subtle eye movement parameters affect human attentional engagement, spontaneous attunement and perceived human-likeness. In: Paladyn, Journal of Behavioral Robotics 11 (1), S. 31-39. DOI: 10.1515/pjbr-2020-0004
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Gransche, Bruno; Manzeschke, Arne (Hg.) (2020): Das geteilte Ganze. Horizonte Integrierter Forschung für künftige Mensch-Technik-Verhältnisse. 1st ed. 2020. Wiesbaden, Germany: Springer VS
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(2020): ALANET: Adaptive Latent Attention Network forJoint Video Deblurring and Interpolation. Online verfügbar unter https://arxiv.org/pdf/2009.01005
Abstract: Existing works address the problem of generating high frame-rate sharp videos by separately learning the frame deblurring and frame interpolation modules. Most of these approaches have a strong prior assumption that all the input frames are blurry whereas in a real-world setting, the quality of frames varies. Moreover, such approaches are trained to perform either of the two tasks - deblurring or interpolation - in isolation, while many practical situations call for both. Different from these works, we address a more realistic problem of high frame-rate sharp video synthesis with no prior assumption that input is always blurry. We introduce a novel architecture, Adaptive Latent Attention Network (ALANET), which synthesizes sharp high frame-rate videos with no prior knowledge of input frames being blurry or not, thereby performing the task of both deblurring and interpolation. We hypothesize that information from the latent representation of the consecutive frames can be utilized to generate optimized representations for both frame deblurring and frame interpolation. Specifically, we employ combination of self-attention and cross-attention module between consecutive frames in the latent space to generate optimized representation for each frame. The optimized representation learnt using these attention modules help the model to generate and interpolate sharp frames. Extensive experiments on standard datasets demonstrate that our method performs favorably against various state-of-the-art approaches, even though we tackle a much more difficult problem.
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(2020) : Identifying a Facial Expression of Flirtation and Its Effect on Men , zuletzt geprüft am 04.09.2020
Abstract: (2020). Identifying a Facial Expression of Flirtation and Its Effect on Men. The Journal of Sex Research. Ahead of Print.
Keywords: facial expression recognition -
(2020): Text-to-Image Generation Grounded by Fine-Grained User Attention. Online verfügbar unter https://arxiv.org/pdf/2011.03775
Abstract: Localized Narratives is a dataset with detailed natural language descriptions of images paired with mouse traces that provide a sparse, fine-grained visual grounding for phrases. We propose TReCS, a sequential model that exploits this grounding to generate images. TReCS uses descriptions to retrieve segmentation masks and predict object labels aligned with mouse traces. These alignments are used to select and position masks to generate a fully covered segmentation canvas; the final image is produced by a segmentation-to-image generator using this canvas. This multi-step, retrieval-based approach outperforms existing direct text-to-image generation models on both automatic metrics and human evaluations: overall, its generated images are more photo-realistic and better match descriptions.
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(2020) : Specification gaming: the flip side of AI ingenuity . Online verfügbar unter https://deepmind.com/blog/article/Specification-gaming-the-flip-side-of-AI-ingenuity
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(2020): A social robot learning to facilitate an assistive group-based activity from non-expert caregivers. In: International Journal of Social Robotics, S. 1159-1176. DOI: 10.1007/s12369-020-00621-4
DOI: https://doi.org/10.1007/s12369-020-00621-4 Abstract: AbstractSocially assistive robots are a promising technology for supporting residential care facilities to provide stimulating recreational activities to residents in group settings. In order for caregivers to teach robots customized recreational activities for residents in their facilities, these robots need to be able to learn such activities from non-experts. In this work, we present a novel learning from demonstration system that allows socially assistive robots to learn customized group recreational activities from caregivers and facilitate these activities with users. We validate the usability and effectiveness of the proposed system by conducting a robot teaching study with caregivers and the Tangy robot at a local residential care facility. The caregivers found the learning system easy to use, experienced moderately low perceived workload, and were able to successfully teach Tangy the game of Bingo. Once Tangy learned the game, it autonomously facilitated Bingo games with elderly residents. The residents found the robot behaviors, personalized by the caregivers, both helpful and entertaining. Furthermore, they enjoyed playing Bingo with Tangy and would participate in future games. (PsycINFO Database Record (c) 2020 APA, all rights reserved)
Keywords: Usability & (Social) Robot(ics) -
(2020): The Creation and Detection of Deepfakes: A Survey. In: ACM Computing Surveys 1 (1)