6,076 research outputs found

    Artificial Intelligence, Robots, and Philosophy

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    This book is a collection of all the papers published in the special issue “Artificial Intelligence, Robots, and Philosophy,” Journal of Philosophy of Life, Vol.13, No.1, 2023, pp.1-146. The authors discuss a variety of topics such as science fiction and space ethics, the philosophy of artificial intelligence, the ethics of autonomous agents, and virtuous robots. Through their discussions, readers are able to think deeply about the essence of modern technology and the future of humanity. All papers were invited and completed in spring 2020, though because of the Covid-19 pandemic and other problems, the publication was delayed until this year. I apologize to the authors and potential readers for the delay. I hope that readers will enjoy these arguments on digital technology and its relationship with philosophy. *** Contents*** Introduction : Descartes and Artificial Intelligence; Masahiro Morioka*** Isaac Asimov and the Current State of Space Science Fiction : In the Light of Space Ethics; Shin-ichiro Inaba*** Artificial Intelligence and Contemporary Philosophy : Heidegger, Jonas, and Slime Mold; Masahiro Morioka*** Implications of Automating Science : The Possibility of Artificial Creativity and the Future of Science; Makoto Kureha*** Why Autonomous Agents Should Not Be Built for War; István Zoltán Zárdai*** Wheat and Pepper : Interactions Between Technology and Humans; Minao Kukita*** Clockwork Courage : A Defense of Virtuous Robots; Shimpei Okamoto*** Reconstructing Agency from Choice; Yuko Murakami*** Gushing Prose : Will Machines Ever be Able to Translate as Badly as Humans?; Rossa Ó Muireartaigh**

    Intelligent computing : the latest advances, challenges and future

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    Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

    Aerial Network Assistance Systems for Post-Disaster Scenarios : Topology Monitoring and Communication Support in Infrastructure-Independent Networks

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    Communication anytime and anywhere is necessary for our modern society to function. However, the critical network infrastructure quickly fails in the face of a disaster and leaves the affected population without means of communication. This lack can be overcome by smartphone-based emergency communication systems, based on infrastructure-independent networks like Delay-Tolerant Networks (DTNs). DTNs, however, suffer from short device-to-device link distances and, thus, require multi-hop routing or data ferries between disjunct parts of the network. In disaster scenarios, this fragmentation is particularly severe because of the highly clustered human mobility behavior. Nevertheless, aerial communication support systems can connect local network clusters by utilizing Unmanned Aerial Vehicles (UAVs) as data ferries. To facilitate situation-aware and adaptive communication support, knowledge of the network topology, the identification of missing communication links, and the constant reassessment of dynamic disasters are required. These requirements are usually neglected, despite existing approaches to aerial monitoring systems capable of detecting devices and networks. In this dissertation, we, therefore, facilitate the coexistence of aerial topology monitoring and communications support mechanisms in an autonomous Aerial Network Assistance System for infrastructure-independent networks as our first contribution. To enable system adaptations to unknown and dynamic disaster situations, our second contribution addresses the collection, processing, and utilization of topology information. For one thing, we introduce cooperative monitoring approaches to include the DTN in the monitoring process. Furthermore, we apply novel approaches for data aggregation and network cluster estimation to facilitate the continuous assessment of topology information and an appropriate system adaptation. Based on this, we introduce an adaptive topology-aware routing approach to reroute UAVs and increase the coverage of disconnected nodes outside clusters. We generalize our contributions by integrating them into a simulation framework, creating an evaluation platform for autonomous aerial systems as our third contribution. We further increase the expressiveness of our aerial system evaluation, by adding movement models for multicopter aircraft combined with power consumption models based on real-world measurements. Additionally, we improve the disaster simulation by generalizing civilian disaster mobility based on a real-world field test. With a prototypical system implementation, we extensively evaluate our contributions and show the significant benefits of cooperative monitoring and topology-aware routing, respectively. We highlight the importance of continuous and integrated topology monitoring for aerial communications support and demonstrate its necessity for an adaptive and long-term disaster deployment. In conclusion, the contributions of this dissertation enable the usage of autonomous Aerial Network Assistance Systems and their adaptability in dynamic disaster scenarios

    Policy and strategy evaluation of ridesharing autonomous vehicle operation: a london case study

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    To understand the dynamics of an autonomous ridesharing transport mode from the perspectives of different stakeholders, a single model of such a system is essential, because this will enable policymakers and companies involved in the manufacture and operation of shared autonomous vehicles (SAVs) to develop user-centered strategies. The model needs to be based on real data, network, and traffic information and applied to real cities and situations, particularly those with complex public transportation systems. In this paper, we propose a new agent-based model for SAV deployment that enables the parametric assessment of key performance indicators from the perspective of potential SAV users, vehicle manufacturers, operators, and local authorities. This has been applied to a case study of three regions in London: central, inner, and outer. The results show there is no linear correlation between an increased ridesharing acceptance level and average trip duration. Without a fleet rebalancing algorithm, over 80% of SAVs’ energy expenditure is on picking up customers. By reducing pickup distance, SAVs could be a contender for a nonpersonal transportation system based on trip energy comparisons. The results provide a picture of future SAV systems for potential users and offer suggestions as to how operators can devise an optimal transportation strategy beyond the question of fleet size and how policymakers can improve the overall transport network and reduce its environmental impact based on energy consumption. As a result of its flexibility and parametric capability, the model can be utilized to inform any local authority how SAV services could be deployed in any city

    Cognition and hypocognition: discursive and simulation-supported decision-making within complex systems

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    Homo sapiens is currently believed to have evolved in the African savannah several hundreds of thousands of years ago. Since then, human societies have become, through technological innovation and application, powerful influencers of the planet's ecological, hydrological and meteorological systems – for good and ill. They have experimented with many different systems of governance, in order to manage their societies and the environments they inhabit – using computer simulations as a tool to help make decisions concerning highly complex systems, is only the most recent of these. In questioning whether, when and how computer simulations should play a role in determining decision-making in these systems of governance, it is also worth reflecting on whether, when and how humans, or groups of humans, have the capability to make such decisions without the aid of such technology. This paper looks at and compares the characteristics of natural language-based and simulation-based decision-making. We argue that computational tools for decision-making can and should be complementary to natural language discourse approaches, but that this requires that both systems are used with their limitations in mind. All tools and approaches – physical, social and mental – have dangers when used inappropriately, but it seems unlikely humankind can survive without them. The challenge is how to do so

    Science and corporeal religion: a feminist materialist reconsideration of gender/sex diversity in religiosity

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    This dissertation develops a feminist materialist interpretation of the role the neuroendocrine system plays in the development of gender/sex differences in religion. Data emerging from psychology, sociology, and cognitive science have continually indicated that women are more religious than men, in various senses of those contested terms, but the factors contributing to these findings are little understood and disciplinary perspectives are often unhelpfully siloed. Previous scholarship has tended to highlight socio-cultural factors while ignoring biological factors or to focus on biological factors while relying on problematic and unsubstantiated gender stereotypes. Addressing gender/sex difference is vital for understanding religion and how we study it. This dissertation interprets this difference by means of a multidisciplinary theoretical and methodological approach. This approach builds upon insights from the cognitive and evolutionary science of religion, affect theory and affective neuroscience, and social neuroendocrinology, and it is rooted in the foundational insights of feminist materialism, including that cultural and micro-sociological forces are inseparable from biological materiality. The dissertation shows how a better way of understanding gender/sex differences in religion emerges through focusing on the co-construction of biological materiality and cultural meanings. This includes deploying a gene-culture co-evolutionary explanation of ultrasociality and an understanding of the biology of performativity to argue that religious behavior and temperaments emerge from the enactment and hormonal underpinnings of six affective adaptive desires: the desires for (1) bonding and attachment, (2) communal mythos, (3) deliverance from suffering, (4) purpose, (5) understanding, and (6) reliable leadership. By hypothesizing the patterns of hormonal release and activation associated with ritualized affects—primarily considering oxytocin, testosterone, vasopressin, estrogen, dopamine, and serotonin—the dissertation theorizes four dimensions of religious temperament: (1) nurturant religiosity, (2) ecstatic religiosity, (3) protective/hierarchical religiosity, and (4) antagonistic religiosity. This dissertation conceptualizes hormones as chemical messengers that enable the diversity emerging from the imbrication of physical materiality and socio-cultural forces. In doing so, it demonstrates how hormonal aspects of gender/sex and culturally constructed aspects of gender/sex are always already intertwined in their influence on religiosity. This theoretical framework sheds light on both the diversity and the noticeable patterns observed in gender/sex differences in religious behaviors and affects. This problematizes the terms of the “women are more religious than men” while putting in place a more adequate framework for interpreting the variety of ways it appears in human lives

    CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

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    This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
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