719 research outputs found

    Technology assessment of advanced automation for space missions

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    Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

    What is science for? The Lighthill report on artificial intelligence reinterpreted

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    This paper uses a case study of a 1970s controversy in artificial-intelligence (AI) research to explore how scientists understand the relationships between research and practical applications. It is part of a project that seeks to map such relationships in order to enable better policy recommendations to be grounded empirically through historical evidence. In 1972 the mathematician James Lighthill submitted a report, published in 1973, on the state of artificial-intelligence research under way in the United Kingdom. The criticisms made in the report have been held to be a major cause behind the dramatic slowing down (subsequently called an ‘AI winter’) of such research. This paper has two aims, one narrow and one broad. The narrow aim is to inquire into the causes, motivations and content of the Lighthill report. I argue that behind James Lighthill's criticisms of a central part of artificial intelligence was a principle he held throughout his career – that the best research was tightly coupled to practical problem solving. I also show that the Science Research Council provided a preliminary steer to the direction of this apparently independent report. The broader aim of the paper is to map some of the ways that scientists (and in Lighthill's case, a mathematician) have articulated and justified relationships between research and practical, real-world problems, an issue previously identified as central to historical analysis of modern science. The paper therefore offers some deepened historical case studies of the processes identified in Agar's ‘working-worlds’ model

    Feature Detection in Medical Images Using Deep Learning

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    This project explores the use of deep learning to predict age based on pediatric hand X-Rays. Data from the Radiological Society of North America’s pediatric bone age challenge were used to train and evaluate a convolutional neural network. The project used InceptionV3, a CNN developed by Google, that was pre-trained on ImageNet, a popular online image dataset. Our fine-tuned version of InceptionV3 yielded an average error of less than 10 months between predicted and actual age. This project shows the effectiveness of deep learning in analyzing medical images and the potential for even greater improvements in the future. In addition to the technological and potential clinical benefits of these methods, this project will serve as a useful pedagogical tool for introducing the challenges and applications of deep learning to the Bryant community

    the admissibility of AI- generated evidence

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    Durante as duas Ășltimas dĂ©cadas, a InteligĂȘncia Artificial tornou-se uma presença constante nas nossas vidas. Ao impactar setores relevantes da sociedade, tem relevando o seu carĂĄter disruptivo, sendo um dos motores impulsionadores da Quarta Revolução Industrial. A InteligĂȘncia Artificial alĂ©m dos seus presentes benefĂ­cios para a humanidade, promete soluçÔes inovadoras para os problemas que afligem a sociedade contemporĂąnea, porĂ©m a mesma comporta uma duplicidade de efeitos. Os sistemas de InteligĂȘncia Artificial pela sua capacidade de monitorizar o seu ambiente circundante, e autonomamente recolher, processar dados, aprender e agir, podem concretizar riscos para os direitos fundamentais, principalmente no contexto da justiça criminal. Esta anĂĄlise irĂĄ focar-se nas especificidades dos sistemas dotados de InteligĂȘncia Artificial, aprofundando a temĂĄtica da admissibilidade da prova gerada por InteligĂȘncia Artificial no quadro probatĂłrio do Direito Processual Penal PortuguĂȘs Ă  luz dos direitos de defesa do arguido e dos seus princĂ­pios que norteadores.During the last two decades Artificial Intelligence became ubiquitous in our lives. Revealing itself as a disruptive technology, it is already impacting important sectors of society, being a driver of the Fourth Industrial Revolution. Artificial Intelligence is benefiting humanity, and promises innovative solutions to modern-life problems, nevertheless it has a twofold effect. Artificial Intelligence as systems that are capable to monitor their surrounding environment, autonomously collect and process data, learn and act, may constitute harm to fundamental rights, mainly when deployed to criminal justice. This analysis will focus on the specificities of Artificial Intelligence systems, delving into the admissibility of AI-generated evidence in the Portuguese criminal evidentiary framework in light of the defence rights and structuring principles of Portuguese criminal procedure

    Intelligence Without Reason

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    Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation

    A virtual operator technique for enhancement of computer-to-computer interactivity

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    A traditional way to handle the problem of system compatibility uses command translation software in which the translation routines are fixed and virtually impossible for a user to add or update. Moreover, in the case where a new system is to be added, new translation software will be required;The virtual operator research proposes a way to implement an expert system with human-like capability, learning communication techniques from human experts and letting users without significant computer background communicate with different systems as desired. The virtual operator has been trained and is capable of communicating with NAS9160/WYLBUR, VAX/VMS and VAX/UNIX

    Activity Report 2021 : Automatic Control, Lund University

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    Integrated Reconfigurable Autonomous Architecture System

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    Advances in state-of-the-art architectural robotics and artificially intelligent design algorithms have the potential not only to transform how we design and build architecture, but to fundamentally change our relationship to the built environment. This system is situated within a larger body of research related to embedding autonomous agency directly into the built environment through the linkage of AI, computation, and robotics. It challenges the traditional separation between digital design and physical construction through the development of an autonomous architecture with an adaptive lifecycle. Integrated Reconfigurable Autonomous Architecture System (IRAAS) is composed of three components: 1) an interactive platform for user and environmental data input, 2) an agent-based generative space planning algorithm with deep reinforcement learning for continuous spatial adaptation, 3) a distributed robotic material system with bi-directional cyber-physical control protocols for simultaneous state alignment. The generative algorithm is a multi-agent system trained using deep reinforcement learning to learn adaptive policies for adjusting the scales, shapes, and relational organization of spatial volumes by processing changes in the environment and user requirements. The robotic material system was designed with a symbiotic relationship between active and passive modular components. Distributed robots slide their bodies on tracks built into passive blocks that enable their locomotion while utilizing a locking and unlocking system to reconfigure the assemblages they move across. The three subsystems have been developed in relation to each other to consider both the constraints of the AI-driven design algorithm and the robotic material system, enabling intelligent spatial adaptation with a continuous feedback chain
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