Intelligence At The Edge
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Artificial Intelligence for Cloud and Edge Computing
This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.
Mobile Edge Artificial Intelligence
Front Cover -- Mobile Edge Artificial Intelligence -- Copyright -- Contents -- List of figures -- Biography -- Yuanming Shi -- Kai Yang -- Zhanpeng Yang -- Yong Zhou -- Preface -- Acknowledgments -- Part 1 Introduction and overview -- 1 Motivations and organization -- 1.1 Motivations -- 1.2 Organization -- References -- 2 Primer on artificial intelligence -- 2.1 Basics of machine learning -- 2.1.1 Supervised learning -- 2.1.1.1 Logistic regression -- 2.1.1.2 Support vector machine -- 2.1.1.3 Decision tree -- 2.1.1.4 k-Nearest neighbors method -- 2.1.1.5 Neural network -- 2.1.2 Unsupervised learning -- 2.1.2.1 k-Means algorithm -- 2.1.2.2 Principal component analysis -- 2.1.2.3 Autoencoder -- 2.1.3 Reinforcement learning -- 2.1.3.1 Q-learning -- 2.1.3.2 Policy gradient -- 2.2 Models of deep learning -- 2.2.1 Convolutional neural network -- 2.2.2 Recurrent neural network -- 2.2.3 Graph neural network -- 2.2.4 Generative adversarial network -- 2.3 Summary -- References -- 3 Convex optimization -- 3.1 First-order methods -- 3.1.1 Gradient method for unconstrained problems -- 3.1.2 Gradient method for constrained problems -- 3.1.3 Subgradient descent method -- 3.1.4 Mirror descent method -- 3.1.5 Proximal gradient method -- 3.1.6 Accelerated gradient method -- 3.1.7 Smoothing for nonsmooth optimization -- 3.1.8 Dual and primal-dual methods -- 3.1.9 Alternating direction method of multipliers -- 3.1.10 Stochastic gradient method -- 3.2 Second-order methods -- 3.2.1 Newton's method -- 3.2.2 Quasi-Newton method -- 3.2.3 Gauss-Newton method -- 3.2.4 Natural gradient method -- 3.3 Summary -- References -- 4 Mobile edge AI -- 4.1 Overview -- 4.2 Edge inference -- 4.2.1 On-device inference -- 4.2.2 Edge inference via computation offloading -- 4.2.2.1 Server-based edge inference -- 4.2.2.2 Device-edge joint inference -- 4.3 Edge training.
Edge AI
As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.