Search results for "Generative AI Engineering"
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Engineering the Metaverse
The metaverse offers a new way of interacting with the world via immersive and interactive technologies. It has the potential to change the way we learn, work and play. 3D modelling, artificial intelligence, virtual, augmented, extended and mixed reality (VR/AR/XR/MR), the Internet of Things (IoT), Web 3.0, 5G and 6G communication, digital twins and simulation technologies, edge and cloud computing, blockchain and cybersecurity are seen as the key enabling and supporting technologies and tools for establishing the next generation metaverse environments. Industrial verticals are keen to embrace the flexible paradigm of the metaverse to be more resilient and relevant to their customers and consumers. This book focuses on the contributing and enabling technologies and use cases for the metaverse including engineering techniques, deployment environments and integrated platforms for designing and developing metaverse applications. It is aimed at industry and academic researchers, scientists, engineers, architects and programmers working in the fields of information and communication technologies, 5G and 6G communication, AI, data science, IoT, edge and cloud computing, cybersecurity and automation with a focus on immersive technologies. It will also be a useful reference for lecturers and advanced students, and product and project managers as well as developers in the field of the metaverse.
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AI Business
What is the bottom line on Artificial Intelligence? The AI Business offers a comprehensive summary of the commercial picture, present and future, for Artificial Intelligence in the computer industry, medicine, the oil industry, and electronic design. AI's brightest and best-financiers, researchers, and users-analyze current projects, speculate on trends in factory automation, compare research in Japan and the U.S., and note the pros and cons of investment opportunities. ContentsExpert Systems * Amplifying Expertise with Expert Systems, Randall Davis (MIT) * XCON: An Expert Configuration System at Digital Equipment Corporation, Arnold Kraft (DEC) * DIPMETER ADVISOR: An Expert Logo Analysis System at Schlumberger, James D. Baker (Schlumberger) * CADUCEUS: An Experimental Expert System for Medical Diagnosis, Harry E. Pople, Jr. (University of Pittsburgh) * The Low Road, the Middle Road, and the High Road, John Seely Brown (Xerox) * Work and Play. Inventing the Future, Alan Kay (Atari) * The Engineer's Apprentice, Aryeh Finegold (Daisy Systems Corporation) * The Programmer's Apprentice, Charles Rich (MIT) * Intelligent Advisory Systems, Roger Schank (Cognitive Systems, Inc. and Yale University) * Natural Language Front Ends, Larry R. Harris (Artificial Intelligence Corporation) * Robotics. Intelligent Robots: Connecting Perception to Action, Michael Brady (MIT) * Intelligent Robots: Moving toward Megassembly, Philippe Villers (Automatix, Inc.) * Intelligent Robots: Myth or Reality, Paul M. Russo (GE) * Today and Tomorrow. The Problems and the Promise, Marvin Minsky (MIT) * An Investment Opportunity? Frederick R. Adler (Adler & Company) * Financing the Future, William H. Janeway (F. Eberstadt & Co., Inc.) * From the Blocks World to the Business World, Patrick H. Winston and Karen A. Prendergast (MIT) * How to Learn More
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Hardware Architectures for Deep Learning
This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.
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