Intelligence

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This book focuses on the emerging topic of embedded artificial intelligence and provides a systematic summary of its principles, platforms, and practices. In the section on principles, it analyzes three main approaches for implementing embedded artificial intelligence: cloud computing mode, local mode, and local-cloud collaborative mode. The book identifies five essential components for implementing embedded artificial intelligence: embedded AI accelerator chips, lightweight neural network algorithms, model compression techniques, compiler optimization techniques, and multi-level cascaded application frameworks. The platform section introduces mainstream embedded AI accelerator chips and software frameworks currently used in the industry. The practical part outlines the development process of embedded artificial intelligence and showcases real-world application examples with accompanying code.

This book presents a comprehensive framework for analyzing, evaluating, and guiding AI for Sciences (AI4Sci) research, offering a unified approach that facilitates analysis across various academic fields through a shared set of dimensions and indicators. It provides a systematic overview of recent AI4Sci advances in various disciplines and offers insights into the latest issues in and prospects of AI4Sci. The book is based on the theory of Parallel Intelligence (PI), which forms the foundation for the general AI4Sci framework. By analyzing multiple cases in various academic fields, this framework integrates key elements of AI4Sci, such as real scientific problems, datasets, virtual systems, AI methods, human roles, and organizational mechanisms, from a multidimensional perspective. It also assesses and summarizes the limitations of AI4Sci, incorporating the latest advances in AI for fundamental models. Lastly, it explores the impact of DeSci and DAO, as well as TAO, on AI4Sci ecosystem development and prospects. Through its balanced approach, the book offers readers a goal-oriented perspective, focusing on a concise presentation of the core ideas and reducing detailed descriptions of specific AI4Sci cases to a minimum.

Artificial Intelligence and Edge Computing for Sustainable Ocean Health explores the transformative role of AI and edge computing in preserving and enhancing ocean health. The growing influence of Artificial Intelligence (AI), along with the Internet of Things (IoT) in generating wide coverage of sensor networks, and Edge Computing (EC) has paved the way for investigation of underwater as well as massive marine data, thereby generating huge potential for credible research opportunities for these domains. This book’s journey begins with a broad overview of Artificial Intelligence for Sustainable Ocean Health, setting the foundation for understanding AI's potential in marine conservation. The subsequent chapter, Role of Artificial Intelligence and Technologies in Improving Ocean Health in Promoting Tourism, illustrates the synergy between technological advancements and sustainable tourism practices, demonstrating how AI can enhance the attractiveness and preservation of marine destinations. The identification, restoration, and monitoring of marine resources along with the utilization of technology continues in Utilization of Underwater Wireless Sensor Network through Supervising a Random Network Environment in the Ocean Environment has been extensively dealt with. The technical challenges of underwater imaging, essential for accurate data collection and analysis has been discussed.

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New York Times bestselling author Deepak Chopra delivers a visionary and unprecedented exploration of how artificial intelligence can revolutionize well-being and open new horizons for personal development.

This book covers all the emerging trends in artificial intelligence (AI) and the Internet of Things (IoT). The Internet of Things is a term that has been introduced in recent years to define devices that are able to connect and transfer data to other devices via the Internet. While IoT and sensors have the ability to harness large volumes of data, AI can learn patterns in the data and quickly extract insights in order to automate tasks for a variety of business benefits.