Описание: In his bestselling work of popular science, Sir Roger Penrose takes us on a fascinating tour through the basic principles of physics, cosmology, mathematics, and philosophy to show that human thinking can never be emulated by a machine.
Автор: J.-J. Ch. Meyer, W. van der Hoek Название: Epistemic Logic for AI and Computer Science ISBN: 0521602807 ISBN-13(EAN): 9780521602808 Издательство: Cambridge Academ Рейтинг: Цена: 9186.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book, based on courses taught at universities and summer schools, provides a broad introduction to the subject; many exercises are included with their solutions.
Автор: Rao, Rajesh P. N. Название: Brain-Computer Interfacing ISBN: 0521769418 ISBN-13(EAN): 9780521769419 Издательство: Cambridge Academ Рейтинг: Цена: 12355.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This introduction to the field of brain-computer interfacing is designed as a textbook for upper-level undergraduate and first-year graduate courses in neural engineering or brain-computer interfacing for students from a wide range of disciplines. It can also be used as a reference by neuroscientists, computer scientists, engineers, and medical practitioners.
Автор: Pablo Duboue Название: The Art of Feature Engineering: Essentials for Machine Learning ISBN: 1108709389 ISBN-13(EAN): 9781108709385 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.
Описание: Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
Автор: Nie Liqiang, Liu Meng, Song Xuemeng Название: Multimodal Learning toward Micro-Video Understanding ISBN: 1681736306 ISBN-13(EAN): 9781681736303 Издательство: Mare Nostrum (Eurospan) Цена: 15523.00 р. Наличие на складе: Нет в наличии.
Описание:
Micro-videos, a new form of user-generated content, have been spreading widely across various social platforms, such as Vine, Kuaishou, and TikTok.
Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to their brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm. The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for high-order micro-video understanding.
Micro-video understanding is, however, non-trivial due to the following challenges: (1) how to represent micro-videos that only convey one or few high-level themes or concepts; (2) how to utilize the hierarchical structure of venue categories to guide micro-video analysis; (3) how to alleviate the influence of low quality caused by complex surrounding environments and camera shake; (4) how to model multimodal sequential data, i.e. textual, acoustic, visual, and social modalities to enhance micro-video understanding; and (5) how to construct large-scale benchmark datasets for analysis. These challenges have been largely unexplored to date.
In this book, we focus on addressing the challenges presented above by proposing some state-of-the-art multimodal learning theories. To demonstrate the effectiveness of these models, we apply them to three practical tasks of micro-video understanding: popularity prediction, venue category estimation, and micro-video routing. Particularly, we first build three large-scale real-world micro-video datasets for these practical tasks. We then present a multimodal transductive learning framework for micro-video popularity prediction. Furthermore, we introduce several multimodal cooperative learning approaches and a multimodal transfer learning scheme for micro-video venue category estimation. Meanwhile, we develop a multimodal sequential learning approach for micro-video recommendation. Finally, we conclude the book and figure out the future research directions in multimodal learning toward micro-video understanding.
Автор: Serap Sisman-Ugur, Gulsun Kurubacak Название: Handbook of Research on Learning in the Age of Transhumanism ISBN: 1522584315 ISBN-13(EAN): 9781522584315 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 30723.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: As a movement, transhumanism aims to upgrade the human body through science, constantly pushing back the limits of a person by using cutting-edge technologies to fix the human body and upgrade it beyond its natural abilities. Transhumanism can not only change human habits, but it can also change learning practices. By improving human learning, it improves the human organism beyond natural and biological limits. The Handbook of Research on Learning in the Age of Transhumanism is an essential research publication that discusses global values, norms, and ethics that relate to the diverse needs of learners in the digital world and addresses future priorities and needs for transhumanism. The book will identify and scrutinize the needs of learners in the age of transhumanism and examine best practices for transhumanist leaders in learning. Featuring topics such as cybernetics, pedagogy, and sociology, this book is ideal for educators, trainers, instructional designers, curriculum developers, professionals, researchers, academicians, policymakers, and librarians.
Автор: Binxing Fang, Yan Jia Название: Structure and Evolution ISBN: 3110596067 ISBN-13(EAN): 9783110596069 Издательство: Walter de Gruyter Рейтинг: Цена: 16169.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The three volume set provides a systematic overview of theories and technique on social network analysis. Volume 1 of the set mainly focuses on the structure characteristics, the modeling, and the evolution mechanism of social network analysis. Techniques and approaches for virtual community detection are discussed in detail as well. It is an essential reference for scientist and professionals in computer science.
Автор: Rajiv Joshi, Matt Ziegler, Arvind Kumar, Eduard Alarcon Название: From Artificial Intelligence to Brain Intelligence: AI Compute Symposium 2018 ISBN: 8770221235 ISBN-13(EAN): 9788770221238 Издательство: Taylor&Francis Рейтинг: Цена: 14851.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The field of AI is not new to researchers, as its foundations were established in the 1950s. After many decades of inattention, there has been a dramatic resurgence of interest in AI, fueled by a confluence of several factors. The benefits of decades of Dennard scaling and Moore’s law miniaturization, coupled with the rise of highly distributed processing, have led to massively parallel systems well suited for handling big data. The widespread availability of big data, necessary for training AI algorithms, is another important factor. Finally, the greatly increased compute power and memory bandwidths have enabled deeper networks and new algorithms capable of accuracy rivaling that of human perception.
Already AI has shown success in many diverse areas, including finance (portfolio management, investment strategies), marketing, health care, transportation, gaming, defense, robotics, computer vision, education, search engines, online assistants, image/facial recognition, anomaly detection, spam filtering, online customer service, biometric sensors, and predictive maintenance, to name a few. Despite these remarkable advances, the human brain is still superior in many ways – including, notably, energy efficiency and one-shot learning – giving researchers new areas to explore. In summary, AI research and applications will continue with vigor in software, algorithms, and hardware accelerators. These exciting developments have also brought new questions of ethics and privacy, areas which must be studied in tandem with technological advances.
To continue the success story of AI, the AI Compute symposium was launched with the sponsorship of IBM, IEEE CAS and EDS for the first time. The aim of this publication is to compile all the materials presented by the renowned speakers in the symposium into a book format, serving as a learning tool for the audience.
This book contains two broad topics: general AI advances (chapters 1-5) and neuromorphic computing directions (chapters 6-9). Technical topics discussed in the book include:
1. Research Directions in AI algorithms and systems
2. An ARM perspective on hardware requirements and challenges for AI
3. The new Era of AI hardware
4. AI and the Opportunity for Unconventional Computing Platforms
5. Thermodynamic Computing
6. Brain-like cognitive engineering system
7. BRAINWAY and Nano - Abacus architecture: Brain-inspired Cognitive Computing using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design
8. Applying Lessons from Nature for Today’s Computing Challenges
9. Emerging Memories - RRAM Fabric for Neuromorphic Computing Applications
Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Автор: Alex Lui, Anna Farzinder, Mingboo Gong Название: Transforming Healthcare with Big Data and AI ISBN: 1641138971 ISBN-13(EAN): 9781641138970 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 7069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Healthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field.
This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.
Автор: Kwang-Cheng Chen Название: Artificial Intelligence in Wireless Robotics ISBN: 8770221189 ISBN-13(EAN): 9788770221184 Издательство: Taylor&Francis Рейтинг: Цена: 14851.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Robots, autonomous vehicles, unmanned aerial vehicles, and smart factory, will significantly change human living style in digital society. Artificial Intelligence in Wireless Robotics introduces how wireless communications and networking technology enhances facilitation of artificial intelligence in robotics, which bridges basic multi-disciplinary knowledge among artificial intelligence, wireless communications, computing, and control in robotics. A unique aspect of the book is to introduce applying communication and signal processing techniques to enhance traditional artificial intelligence in robotics and multi-agent systems.
The technical contents of this book include fundamental knowledge in robotics, cyber-physical systems, artificial intelligence, statistical decision and Markov decision process, reinforcement learning, state estimation, localization, computer vision and multi-modal data fusion, robot planning, multi-agent systems, networked multi-agent systems, security and robustness of networked robots, and ultra-reliable and low-latency machine-to-machine networking. Examples and exercises are provided for easy and effective comprehension.
Engineers wishing to extend knowledge in the robotics, AI, and wireless communications, would be benefited from this book. In the meantime, the book is ready as a textbook for senior undergraduate students or first-year graduate students in electrical engineering, computer engineering, computer science, and general engineering students. The readers of this book shall have basic knowledge in undergraduate probability and linear algebra, and basic programming capability, in order to enjoy deep reading.
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