"The authors of Machines Like Us explore what it would take to endow computers with the kind of common sense that humans depend on every day--critically needed for AI systems to be successful in the world and to become trustworthy"-- Provided by publisher
Book — 1 online resource (180 pages) Sound: digital.
Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
(source: Nielsen Book Data)
In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed. (source: Nielsen Book Data)
Second edition. - Boca Raton, FL : CRC Press, 
Book — 1 online resource (xii, 598 pages) : illustrations
Introduction. Computer Graphics and Visualization. Discrete Data Representation in (Scientific) Visualization Applications. Visualization Pipeline. Fundamental Techniques for Scalar Visualization. Vector Visualization Techniques. Tensor Visualization Techniques. Domain Modeling Techniques. Scientific Visualization and Signal/Image Processing. Scalar Visualization. Information Visualization (Infovis) Techniques. The Value and Price of Visualization. Challenges and Prospects of the Visualization Field. Appendix.
(source: Nielsen Book Data)
Designing a complete visualization system involves many subtle decisions. When designing a complex, real-world visualization system, such decisions involve many types of constraints, such as performance, platform (in)dependence, available programming languages and styles, user-interface toolkits, input/output data format constraints, integration with third-party code, and more. Focusing on those techniques and methods with the broadest applicability across fields, the second edition of Data Visualization: Principles and Practice provides a streamlined introduction to various visualization techniques. The book illustrates a wide variety of applications of data visualizations, illustrating the range of problems that can be tackled by such methods, and emphasizes the strong connections between visualization and related disciplines such as imaging and computer graphics. It covers a wide range of sub-topics in data visualization: data representation; visualization of scalar, vector, tensor, and volumetric data; image processing and domain modeling techniques; and information visualization. See What's New in the Second Edition: Additional visualization algorithms and techniques New examples of combined techniques for diffusion tensor imaging (DTI) visualization, illustrative fiber track rendering, and fiber bundling techniques Additional techniques for point-cloud reconstruction Additional advanced image segmentation algorithms Several important software systems and libraries Algorithmic and software design issues are illustrated throughout by (pseudo)code fragments written in the C++ programming language. Exercises covering the topics discussed in the book, as well as datasets and source code, are also provided as additional online resources. (source: Nielsen Book Data)