Название: EdgeAI for Algorithmic GovernmentАвтор: Rajan Gupta, Sanjana Das, Saibal Kumar Pal
Издательство: Palgrave Macmillan/Springer
Год: 2023
Страниц: 109
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB
This book titled EdgeAI for Algorithmic Government has got three significant things to offer—Introduction to Algorithmic Government and Large-Scale Decision-Making, various computing technologies around Algorithmic Government like Cloud, Fog, Edge, and EdgeAI, followed by potential use cases of EdgeAI for Algorithmic Government around the world.
Algorithmic Government or Government by Algorithm is an emerging concept introduced around the world in recent years. It involves using Data Science and Artificial Intelligence for decision-making by the Government for various services and processes. Algorithms facilitating large-scale government decision-making processes and public services must be well structured, secure, and fast, resulting in transparent and righteous governance. The cloud-centric architecture of AI is no longer suitable for the rapid calculations and analysis that must be performed on such a massive volume of data. To this end, we need to bring the AI services closer to the user devices, i.e., at the edge of the network. This complimentary relationship of Edge Computing and AI is what we call EdgeAI or Edge Intelligence, which aims to realize the potential benefits of AI at the network edge instead of the network core.
The book provides various EdgeAI concepts related to its architecture, key performance indicators, and enabling technologies after introducing algorithmic government, large-scale decision-making, and computing issues in the cloud and fog. With advancements in technology, Artificial Intelligence has permeated our personal lives and the fields of economy, socio-culture, and politics. The integration of Artificial Intelligence (AI) into decision-making for public services is changing how governments operate worldwide. This book discusses how algorithms help the government in various ways, including virtual assistants for busy civil servants, automated public services, and algorithmic decision-making processes. In such cases, the implementation of algorithms will occur on a massive scale and possibly affect the lives of entire communities. The cloud-centric architecture of Artificial Intelligence brings out challenges of latency, overhead communication, and significant privacy risks. Due to the sheer volume of data generated by IoT devices, the data analysis must be performed at the forefront of the network. This introduces the need for Edge Computing in algorithmic government. EdgeAI, the confluence of edge computing and AI, is the primary focus of this book.
The first chapter covers the background of Algorithmic Government, various concepts, motivations, and benefits, large-scale decision-making for government, and different technological solutions. The second chapter introduces the concept of Edge Computing and various types of AI techniques used for analysis purposes for computing different tasks. The third chapter focuses on EdgeAI principles, levels of Edge Intelligence, and model training/inferencing at Edge. The fourth chapter presents various Algorithmic Government use cases where EdgeAI would be applicable and beneficial. Chapter five presents a combined framework for EdgeAI applications, network integrations, resource management, coexistence of cloud and edge, reliability of Edge devices, hardware level requirements, and future scope of work.
This book will be helpful for data engineers, data scientists, cloud engineers, data management experts, public policymakers, administrators, research scholars and academicians.
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