Examining the modern origins of artificial intelligence, this book explores issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries.
This introduction to computational geometry focuses on algorithms. Motivation is provided from the application areas as all techniques are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement.
Haikonen envisions autonomous robots that perceive and understand the world directly, acting in it in a natural human-like way without the need of programs and numerical representation of information. By developing higher-level cognitive functions through the power of artificial associative neuron architectures, the author approaches the issues of machine consciousness. Robot Brains expertly outlines a complete system approach to cognitive machines, offering practical design guidelines for the creation of non-numeric autonomous creative machines. It details topics such as component parts and realization principles, so that different pieces may be implemented in hardware or software. Real-world examples for designers and researchers are provided, including circuit and systems examples that few books on this topic give. In novel technical and practical detail, this book also considers: * the limitations and remedies of traditional neural associators in creating true machine cognition; * basic circuit assemblies cognitive neural architectures; * how motors can be interfaced with the associative neural system in order for fluent motion to be achieved without numeric computations; * memorization, imagination, planning and reasoning in the machine; * the concept of machine emotions for motivation and value systems; * an approach towards the use and understanding of natural language in robots. The methods presented in this book have important implications for computer vision, signal processing, speech recognition and other information technology fields. Systematic and thoroughly logical, it will appeal to practising engineers involved in the development and design of robots and cognitive machines, also researchers in Artificial Intelligence. Postgraduate students in computational neuroscience and robotics, and neuromorphic engineers will find it an exciting source of information.
In recent years, due to the rapid development of computer technology, computer vision techniques have been successfully implemented to solve practical problems that face engineers and scientists. Although many methods and applications are still in the fundamental research phase, a large number of them are found to be used in commercial products, which are a very important part of much more complex systems that solve a given practical problem. The scope of computer vision is very diverse. This is a robotics that uses computer vision as a video sensor that provides input information to find the position of a robot during its mechanical movement in space; and other artificial intelligence systems that use computer vision for pattern recognition for use in computer learning methods. As a result, computer vision is often viewed as part of the field of artificial intelligence or the field of computer science in general.
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.