Portrayals and perceptions of AI and why they matter

How researchers, communicators, policymakers, and publics talk about technology matters. Shared understandings about the nature, promise and risks of new technologies develop through the explicit or implicit stories that different groups tell about technology and its place in our lives. The AI narratives project – a joint endeavour by the Leverhulme Centre for the Future of Intelligence and the Royal Society – has been examining which narratives currently influence public debates about AI, and how these portrayals might shape public perceptions of the capabilities, risks, and benefits of AI technologies. Many of the current ideas about AI technologies that are pervasive in public consciousness – typically that AI is an embodied, super-human intelligence – are shaped by hundreds of years of stories that people have told about humans and machines, and our places in the world. This cultural hinterland shapes how AI is portrayed in media, culture, and everyday discussion; it influences what societies find concerning – or exciting – about technological developments; and it affects how different publics relate to AI technologies. Building a well-founded public dialogue about AI technologies will be key to continued public confidence in the systems that deploy AI technologies, and to realising the benefits they promise across sectors. Since the launch of the machine learning project, the Royal Society has been creating spaces for public discussion about AI technologies, and their implications for society. In a series of four workshops, the Royal Society and Leverhulme Centre for the Future of Intelligence explored: which narratives around intelligent machines are most prevalent, and their historical roots; what can be learned from how the narrative around other complex, new technologies developed, and the impact of these; how narratives are shaping the development of AI, and the role of arts and media in this process; and the implications of current AI narratives for researchers and communicators. The report brings together the conclusions of these workshops, and is for anyone interested in how AI is portrayed and perceived.Drs Cave, Dihal, and Dillon are funded by a Leverhulme Trust Research Centre Grant awarded to the Leverhulme Centre for the Future of Intelligence. Dr Singler was funded by a Templeton World Charitable Foundation grant during the course of the AI narratives project, awarded to the Faraday Institute for Science and Religion, St Edmund's College, Cambridge

Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed “the wall” that excludes any possi-bility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Tu-ring test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify con-cepts as well as participate in social practices

Towards a synthetic tutor assistant: The EASEL project and its architecture

Robots are gradually but steadily being introduced in our daily lives. A paramount application is that of education, where robots can assume the role of a tutor, a peer or simply a tool to help learners in a specific knowledge domain. Such endeavor posits specific challenges: affective social behavior, proper modelling of the learner’s progress, discrimination of the learner’s utterances, expressions and mental states, which, in turn, require an integrated architecture combining perception, cognition and action. In this paper we present an attempt to improve the current state of robots in the educational domain by introducing the EASEL EU project. Specifically, we introduce the EASEL’s unified robot architecture, an innovative Synthetic Tutor Assistant (STA) whose goal is to interactively guide learners in a science-based learning paradigm, allowing us to achieve such rich multimodal interactions

A Review on Current and Potential Applications of Robotics In Mental Health Care

Robotics technology is most commonly associated with robots, that are physically embodied systems capable of causing physical change in the world. Robots execute this transformation via effectors that either move the robot itself (locomotion) or move items in the environment (manipulation), and they frequently make judgments based on data from sensors. Robot autonomy can range from totally teleoperated to fully autonomous (the robot is entirely independent). The word robotics technology also encompasses related technologies, such as sensor systems, data processing algorithms, and so forth.  While in recent years this has evolved outward, with an emphasis on difficulties connected to dealing with actual people in the real world. This transition has been referred to as human-centered robotics in the literature, and a developing topic in the last decade focused on difficulties in this arena is known as human robot interaction (HRI). The application of robotics technology in mental health treatment is still in its early stages, but it offers a potentially beneficial tool in the professional's arsenal

Service robotics: do you know your new companion? Framing an interdisciplinary technology assessment

Service-Robotic—mainly defined as “non-industrial robotics”—is identified as the next economical success story to be expected after robots have been ubiquitously implemented into industrial production lines. Under the heading of service-robotic, we found a widespread area of applications reaching from robotics in agriculture and in the public transportation system to service robots applied in private homes. We propose for our interdisciplinary perspective of technology assessment to take the human user/worker as common focus. In some cases, the user/worker is the effective subject acting by means of and in cooperation with a service robot; in other cases, the user/worker might become a pure object of the respective robotic system, for example, as a patient in a hospital. In this paper, we present a comprehensive interdisciplinary framework, which allows us to scrutinize some of the most relevant applications of service robotics; we propose to combine technical, economical, legal, philosophical/ethical, and psychological perspectives in order to design a thorough and comprehensive expert-based technology assessment. This allows us to understand the potentials as well as the limits and even the threats connected with the ongoing and the planned implementation of service robots into human lifeworld—particularly of those technical systems displaying increasing grades of autonomy

Large Language Models for Robotics: A Survey

The human ability to learn, generalize, and control complex manipulation tasks through multi-modality feedback suggests a unique capability, which we refer to as dexterity intelligence. Understanding and assessing this intelligence is a complex task. Amidst the swift progress and extensive proliferation of large language models (LLMs), their applications in the field of robotics have garnered increasing attention. LLMs possess the ability to process and generate natural language, facilitating efficient interaction and collaboration with robots. Researchers and engineers in the field of robotics have recognized the immense potential of LLMs in enhancing robot intelligence, human-robot interaction, and autonomy. Therefore, this comprehensive review aims to summarize the applications of LLMs in robotics, delving into their impact and contributions to key areas such as robot control, perception, decision-making, and path planning. We first provide an overview of the background and development of LLMs for robotics, followed by a description of the benefits of LLMs for robotics and recent advancements in robotics models based on LLMs. We then delve into the various techniques used in the model, including those employed in perception, decision-making, control, and interaction. Finally, we explore the applications of LLMs in robotics and some potential challenges they may face in the near future. Embodied intelligence is the future of intelligent science, and LLMs-based robotics is one of the promising but challenging paths to achieve this.Comment: Preprint. 4 figures, 3 table

Artificial Intelligence: Robots, Avatars, and the Demise of the Human Mediator

Published in cooperation with the American Bar Association Section of Dispute Resolutio