Equity confirmed it was supporting a Scottish actor who believes her image was used in the creation of Tilly Norwood (above), an AI-generated'actor'. Equity confirmed it was supporting a Scottish actor who believes her image was used in the creation of Tilly Norwood (above), an AI-generated'actor'. Equity threatens mass direct action over use of actors' images in AI content The performing arts union Equity has threatened mass direct action over tech and entertainment companies' use of its members' likenesses, images and voices in AI content without permission. Its general secretary, Paul W Fleming, said it planned to coordinate data requests en masse to companies to force them to disclose whether they used members' data in AI-generated material without consent. Last week the union confirmed its was supporting a Scottish actor who believes her image was used in the creation of the "AI actor" Tilly Norwood, which has been widely condemned by the film industry.

Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational question-answering datasets with natural topic transitions. By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method. Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts. Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks.

This paper presents an empirical investigation of the extent to which spoken Humanoid Embodied Conversational Agents (HECAs) can foster usability in mobile serious game (MSG) applications. The aim of the research is to assess the impact of multiple agents and illusion of humanness on the quality of the interaction. The experiment investigates two styles of agent presentation: an agent of high human-likeness (HECA) and an agent of low human-likeness (text). The purpose of the experiment is to assess whether and how agents of high humanlikeness can evoke the illusion of humanness and affect usability. Agents of high human-likeness were designed by following the ECA design model that is a proposed guide for ECA development. The results of the experiment with 90 participants show that users prefer to interact with the HECAs. The difference between the two versions is statistically significant with a large effect size (d=1.01), with many of the participants justifying their choice by saying that the human-like characteristics of the HECA made the version more appealing. This research provides key information on the potential effect of HECAs on serious games, which can provide insight into the design of future mobile serious games.

Decision Point AI are working with the Paisley YMCA, Scotland on a secure application that standardises both the SHANARRI social framework processes and its data. SHANARRI is an acronym for the eight wellbeing indicators in the Curriculum for Excellence (CfE) in Health and Wellbeing. It stands for Safe, Healthy, Achieving, Nurtured, Active, Respected, Responsible, Included. The indicators are used to structure the information recorded about a child or young person and to monitor their progress across social services. Since starting Decision Point AI, we have had to work hard to demystify AI and focus on real world problems with potential clients.

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