Vocal Interactivity in-and-between Humans, Animals, and Robots

Almost all animals exploit vocal signals for a range of ecologically motivated purposes: detecting predators/prey and marking territory, expressing emotions, establishing social relations, and sharing information. Whether it is a bird raising an alarm, a whale calling to potential partners, a dog responding to human commands, a parent reading a story with a child, or a business-person accessing stock prices using Siri, vocalization provides a valuable communication channel through which behavior may be coordinated and controlled, and information may be distributed and acquired. Indeed, the ubiquity of vocal interaction has led to research across an extremely diverse array of fields, from assessing animal welfare, to understanding the precursors of human language, to developing voice-based human–machine interaction. Opportunities for cross-fertilization between these fields abound; for example, using artificial cognitive agents to investigate contemporary theories of language grounding, using machine learning to analyze different habitats or adding vocal expressivity to the next generation of language-enabled autonomous social agents. However, much of the research is conducted within well-defined disciplinary boundaries, and many fundamental issues remain. This paper attempts to redress the balance by presenting a comparative review of vocal interaction within-and-between humans, animals, and artificial agents (such as robots), and it identifies a rich set of open research questions that may benefit from an interdisciplinary analysis

Chief Justice Robots

Say an AI program someday passes a Turing test, because it can con-verse in a way indistinguishable from a human. And say that its develop-ers can then teach it to converse—and even present an extended persua-sive argument—in a way indistinguishable from the sort of human we call a “lawyer.” The program could thus become an AI brief-writer, ca-pable of regularly winning brief-writing competitions against human lawyers. Once that happens (if it ever happens), this Essay argues, the same technology can be used to create AI judges, judges that we should accept as no less reliable (and more cost-effective) than human judges. If the software can create persuasive opinions, capable of regularly winning opinion-writing competitions against human judges—and if it can be adequately protected against hacking and similar attacks—we should in principle accept it as a judge, even if the opinions do not stem from human judgment

Learning in a Reggio-Inspired Reuse Center

What would children\u27s learning look like in the Child Development Center\u27s Re-Use Center if teachers, parents, and children could collaborate around the learning? This action research collaboration between a professor and a graduate student/teacher, examines the process the graduate student/teacher goes through as she documents the re-use center narratives of a small group of young children. A re-use center is likened to Reggio Emilia, Italy\u27s Remida Center as a repository for found, rescued and repurposed materials. This center finds new meanings for discarded items destined for landfills and incinerators. The findings reveal stories of language, literacy and social development as well as explore how intentional collaboration can enhance project planning. Conclusions reveal that big questions for teachers, rich dialogue in reuse contexts for learning, and flexibility with participants are essential to collaborative learning in the reuse center

On Agent Communication in Large Groups

The problem is fundamental and natural, yet deep - to simulate the simplest possible form of communication that can occur within a large multi-agent system. It would be prohibitive to try and survey all of the research on communication in general so we must restrict our focus. We will devote our efforts to synthetic communication occurring within large groups. In particular, we would like to discover a model for communication that will serve as an abstract model, a prototype, for simulating communication within large groups of biological organisms

Salsa magic: an exploratory netnographic analysis of the salsa experience

This is a paper about the promise of salsa dancing as unfolding social drama. We argue that a turn to dance offers much potential to reinvigorate ways of theorizing consumer culture, necessitating we take seriously talk around such experiences. Based on a netnographic analysis, which is inspired by the informative work of Kozinets (1997, 1998, 2001). We reveal how dance is a reflexive form of knowledge enacted in and through our bodies, where the settled and fixed becomes disturbed. Dance then makes possible shared passions, exhilarations and desires lacking from people's everyday lives granting them a space for expression

Generating Rembrandt: Artificial Intelligence, Copyright, and Accountability in the 3A Era--The Human-like Authors are Already Here- A New Model

Artificial intelligence (AI) systems are creative, unpredictable, independent, autonomous, rational, evolving, capable of data collection, communicative, efficient, accurate, and have free choice among alternatives. Similar to humans, AI systems can autonomously create and generate creative works. The use of AI systems in the production of works, either for personal or manufacturing purposes, has become common in the 3A era of automated, autonomous, and advanced technology. Despite this progress, there is a deep and common concern in modern society that AI technology will become uncontrollable. There is therefore a call for social and legal tools for controlling AI systems’ functions and outcomes. This Article addresses the questions of the copyrightability of artworks generated by AI systems: ownership and accountability. The Article debates who should enjoy the benefits of copyright protection and who should be responsible for the infringement of rights and damages caused by AI systems that independently produce creative works. Subsequently, this Article presents the AI Multi- Player paradigm, arguing against the imposition of these rights and responsibilities on the AI systems themselves or on the different stakeholders, mainly the programmers who develop such systems. Most importantly, this Article proposes the adoption of a new model of accountability for works generated by AI systems: the AI Work Made for Hire (WMFH) model, which views the AI system as a creative employee or independent contractor of the user. Under this proposed model, ownership, control, and responsibility would be imposed on the humans or legal entities that use AI systems and enjoy its benefits. This model accurately reflects the human-like features of AI systems; it is justified by the theories behind copyright protection; and it serves as a practical solution to assuage the fears behind AI systems. In addition, this model unveils the powers behind the operation of AI systems; hence, it efficiently imposes accountability on clearly identifiable persons or legal entities. Since AI systems are copyrightable algorithms, this Article reflects on the accountability for AI systems in other legal regimes, such as tort or criminal law and in various industries using these systems