583 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Food - Media - Senses: Interdisciplinary Approaches

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    Food is more than just nutrition. Its preparation, presentation and consumption is a multifold communicative practice which includes the meal's design and its whole field of experience. How is food represented in cookbooks, product packaging or in paintings? How is dining semantically charged? How is the sensuality of eating treated in different cultural contexts? In order to acknowledge the material and media-related aspects of eating as a cultural praxis, experts from media studies, art history, literary studies, philosophy, experimental psychology, anthropology, food studies, cultural studies and design studies share their specific approaches

    Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

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    Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems

    Scaling energy management in buildings with artificial intelligence

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    20th Annual Symposium of the School of Science, Engineering and Health

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    We in the School of Science, Engineering and Health at Messiah University welcome you to our 20th Annual Symposium. Please celebrate with our students, staff and faculty as you hear about and see professional presentations that showcase our students’ basic and applied research in science and health fields. The outcomes of scientific research expand intellectual understanding and have tremendous impact on quality of life, environmental health, and human flourishing. We warmly welcome you as guests for the day. Angela Hare Dean School of Science, Engineering and Healt

    Humans in the Loop

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    From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human-in-the-loop” systems. We make four contributions to the discourse. First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into a decisionmaking process. Regardless of whether the law governing these systems is old or new, inadvertent or intentional, it rarely accounts for the fact that human-machine systems are more than the sum of their parts: they raise their own problems and require their own distinct regulatory interventions. But how to regulate for success? Our third contribution is to highlight the panoply of roles humans might be expected to play, to assist regulators in understanding and choosing among the options. For our fourth contribution, we draw on legal case studies and synthesize lessons from human factors engineering to suggest regulatory alternatives to the MABA-MABA approach. Namely, rather than carelessly placing a human in the loop, policymakers should regulate the human-in-the-loop system

    Humans in the Loop

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    From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human-in-the-loop” systems. We make four contributions to the discourse. First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into a decisionmaking process. Regardless of whether the law governing these systems is old or new, inadvertent or intentional, it rarely accounts for the fact that human-machine systems are more than the sum of their parts: they raise their own problems and require their own distinct regulatory interventions. But how to regulate for success? Our third contribution is to highlight the panoply of roles humans might be expected to play, to assist regulators in understanding and choosing among the options. For our fourth contribution, we draw on legal case studies and synthesize lessons from human factors engineering to suggest regulatory alternatives to the MABA-MABA approach. Namely, rather than carelessly placing a human in the loop, policymakers should regulate the human-in-the-loop system

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Analytics and Intuition in the Process of Selecting Talent

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    In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
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