The success of live comedy depends on a performer's ability to “work” an audience. Ethnographic studies suggest that this involves the co-ordinated use of subtle social signals such as body orientation, gesture, gaze by both performers and audience members. Robots provide a unique opportunity to test the effects of these signals experimentally. Using a life-size humanoid robot, programmed to perform a stand-up comedy routine, we manipulated the robot's patterns of gesture and gaze and examined their effects on the real-time responses of a live audience. The strength and type of responses were captured using SHORE™computer vision analytics. The results highlight the complex, reciprocal social dynamics of performer and audience behavior. People respond more positively when the robot looks at them, negatively when it looks away and performative gestures also contribute to different patterns of audience response. This demonstrates how the responses of individual audience members depend on the specific interaction they're having with the performer. This work provides insights into how to design more effective, more socially engaging forms of robot interaction that can be used in a variety of service contexts.
Artificial Intelligence (AI) for accessibility is a rapidly growing area, requiring datasets that are inclusive of the disabled users that assistive technology aims to serve. We offer insights from a multi-disciplinary project that constructed a dataset for teachable object recognition with people who are blind or low vision. Teachable object recognition enables users to teach a model objects that are of interest to them, e.g., their white cane or own sunglasses, by providing example images or videos of objects. In this paper, we make the following contributions: 1) a disability-first procedure to support blind and low vision data collectors to produce good quality data, using video rather than images; 2) a validation and evolution of this procedure through a series of data collection phases and 3) a set of questions to orient researchers involved in creating datasets toward reflecting on the needs of their participant community.CCS Concepts: • Human-centered computing → Accessibility; accessibility systems and tools; accessibility technologies; • Computing methodologies → Machine learning.
Critical but often overlooked research questions in artificial intelligence (AI) applied to music involve the impact of the results for music. How and to what extent does such research contribute to the domain of music? How are the resulting models useful for music practitioners? In this article, we describe how we are addressing such questions by engaging composers, musicians, and audiences with our research. We first describe two websites we have created that make our AI models accessible to a wide audience. We then describe a professionally recorded album that we released to expert reviewers to gauge the plausibility of AI-generated material. Finally, we describe the use of our AI models as tools for co-creation. Evaluating AI research and music models in these ways illuminate their impact on music making in a range of styles and practices.
Drawing as a form of analytical inscription can provide researchers with highly flexible methods for exploring embodied interaction. Graphical techniques can combine spatial layouts, trajectories of action and anatomical detail, as well as rich descriptions of movement and temporal effects. This paper introduces some of the possibilities and challenges of adapting graphical techniques from life drawing and still life for interaction research. We demonstrate how many of these techniques are used in interaction research by illustrating the postural configurations and movements of participants in a ballet class. We then discuss a prototype software tool that is being developed to support interaction analysis specifically in the context of a collaborative data analysis session.
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