Titlebook: Engineering Artificially Intelligent Systems; A Systems Engineerin William F. Lawless,James Llinas,Ranjeev Mittu Book 2021 Springer Nature

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Risk Reduction for Autonomous Systems,can provide this assurance in both civil and military systems. The principle risk areas are identified, showing that organisations and individuals will have to accept new responsibilities. It will need the technical and legal professions to collaborate to understand and define acceptability both wit

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Agile Systems Engineering in Building Complex AI Systems,er serve our patients. We make use of natural language processing (NLP) in conjunction with cutting-edge machine learning (ML) and deep learning (DL) techniques to build various data products, including an understanding of consumer needs, their grievances, and how well we serve our consumers. Each s

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Platforms for Assessing Relationships: Trust with Near Ecologically-Valid Risk, and Team Interactioines to escape from a room collaboratively. PAR-TNER leverages virtual reality to stimulate risk, while PAR-TI allows researchers to explore team dynamics. While the data from the pilot test of PAR-TNER are limited, they indicate the ability to leverage the research platforms to discern trust from p

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Principles of an Accurate Decision and Sense-Making for Virtual Minds,define a decision algorithm which always terminates?.To do so, we must relax the deductive burden since we have to deal seriously with infinity causing unsolvable termination issues for proof systems when a decision is implemented as a proof. We prefer to investigate on hypercomplex representations

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William F. Lawless,James Llinas,Ranjeev MittuDescribes how AI and machine learning algorithms can efficiently adapt to technical‘challenges.Important for data and information fusion communities.Contributions by leading researchers in systems eng

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/310722.jpg

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https://doi.org/10.1007/978-3-662-59523-7y be an entirely new systems engineering paradigm for the engineering of artificially-intelligent (AI) systems. These perspectives evolve from many viewpoints related to complexities ranging from issues in data and data engineering to the challenges of realizing ethical and legal employment of auton

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,Organisationsentwicklungsmaßnahmen,ted, networked, integrated, multi-modal, and agile sensors with which to provide data for situation assessment (SA). Associated with this progression is the inexorable increase in the quantity, quality, and diversity of data which these systems provide. In order to provide a minimal uncertainty, mis