Suchergebnisse

Abstract
This article focuses on copyright issues pertaining to generative artificial intelligence (AI) systems, with particular emphasis on the ChatGPT case study as a primary exemplar. In order to generate high-quality outcomes, generative AI systems require substantial quantities of training data, which may frequently comprise copyright-protected information. This prompts inquiries into the legal principles of fair use, the creation of derivative works and the lawfulness of data gathering and utilisation. The utilisation of input data for the purpose of training and enhancing AI models presents significant concerns regarding potential violations of copyright. This paper offers suggestions for safeguarding the interests of copyright holders and competitors, while simultaneously addressing legal challenges and expediting the advancement of AI technologies. This study analyses the ChatGPT platform as a case example to explore the necessary modifications that copyright regulations must undergo to adequately tackle the intricacies of authorship and ownership in the realm of AI-generated creative content.

With the rise of artificial intelligence (AI), higher education faces a significant challenge in learning assessment. The emergence of tools like ChatGPT raises concerns regarding the potential for cheating and the reliability of assessment outcomes. This paper aims to address these concerns by proposing a methodology for framing questions that effectively measures learning outcomes while reducing the risk of AI-enabled cheating. To achieve this objective, we employ a methodological approach that involves getting responses from ChatGPT 3.5 to various question prompts across different domains. These responses are then evaluated by faculty members specializing in management education. Through this process, we aim to identify question-framing strategies that effectively assess learning outcomes while minimizing susceptibility to AI Cheating. Our analysis reveals several key findings. Certain question Types (Decision Making, Recent Events, and Experiential Learning) demonstrate greater resilience against AI-generated responses, indicating their potential effectiveness in assessing student learning. This study offers original insights into the challenges and opportunities associated with learning assessment in the context of AI integration. The paper tries to provide valuable guidance for Policymakers, educators & students seeking to enhance the integrity and reliability of their assessment practices. Doi: 10.28991/ESJ-2024-08-03-09 Full Text: PDF

La Ciencia de la Información se considera una ciencia interdisciplinaria, lo que significa que contribuye y recibe contribuciones de diversas áreas, siendo una de ellas la Ciencia de la Computación. Dentro de los campos de estudio de la Ciencia de la Computación, la Inteligencia Artificial está destacada actualmente, dado que el interés en estudios relacionados con la inteligencia de las máquinas ha aumentado significativamente, especialmente con la aparición de herramientas generativas como ChatGPT. Teniendo en cuenta que estas herramientas son capaces de imitar algunas funciones cognitivas de los seres humanos, se pueden explorar sus aplicaciones en la organización de la información. El objetivo de esta investigación es reflexionar sobre las posibilidades de organización de la información derivadas del acceso a ChatGPT, explorando la configuración del texto y cómo el chatbot compone los objetos informativos en sus respuestas. Dado lo expuesto, la inteligencia artificial tiene un gran potencial para ser una herramienta de apoyo en la organización de la información; sin embargo, ChatGPT tiene algunas limitaciones que deben ser estudiadas y desarrolladas para ofrecer mejores posibilidades de aplicación.

Der amerikanische Techriese erprobt seit April den KI-Chatbot Med-PaLML 2 mit medizinischen Kunden. Darunter sei mit der Mayo-Klinik auch die renommierteste Klinik der USA, wie das "Wallstreetjournal" (WSJ) berichtet.