The National Artificial Intelligence Research and Development Strategic Plan


Book Description

Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016, the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federally-funded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts.




Data Science Landscape


Book Description

The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains. The idea of this work germinated from the two day workshop on “Big and Open Data – Evolving Data Science Standards and Citation Attribution Practices”, an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group. In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume.




Remote Sensing Big Data


Book Description

This monograph provides comprehensive coverage of the collection, management, and use of big data obtained from remote sensing. The book begins with an introduction to the basics of big data and remote sensing, laying the groundwork for the more specialized information to follow. The volume then goes on to address a wide variety of topics related to the use and management of remote sensing big data, including hot topics such as analysis through machine learning, cyberinfrastructure, and modeling. Examples on how to use the results of big data analysis of remotely sensed data for concrete decision-making are offered as well. The closing chapters discuss geospatial big data initiatives throughout the world and future challenges and opportunities for remote sensing big data applications. The audience for this book includes researchers at the intersection of geoscience and data science, senior undergraduate and graduate students, and anyone else interested in how large datasets obtained through remote sensing can be best utilized. The book presents a culmination of 30 years of research from renowned spatial scientists Drs. Liping Di and Eugene Yu.




Big Data Shocks


Book Description

"Big data," as it has become known in business and information technology circles, has the potential to improve our knowledge about human behavior, and to help us gain insight into the ways in which we organize ourselves, our cultures, and our external and internal lives. Libraries stand at the center of the information world, both facilitating and contributing to this flood as well as helping to shape and channel it to specific purposes. But all technologies come with a price. Where the tool can serve a purpose, it can also change the user's behavior to fit the purposes of the tool. Big Data Shocks: An Introduction to Big Data for Librarians and Information Professionals examines the roots of big data, the current climate and rising stars in this world. The book explores the issues raised by big data and discusses theoretical as well as practical approaches to managing information whose scope exists beyond the human scale. What’s at stake ultimately is the privacy of the people who support and use our libraries and the temptation for us to examine their behaviors. Such tension lies deep in the heart of our great library institutions. This book addresses these issues and many of the questions that arise from them, including: What is our role as librarians within this new era of big data? What are the impacts of new powerful technologies that track and analyze our behavior? Do data aggregators know more about us and our patrons than we do? How can librarians ethically balance the need to demonstrate learning and knowledge creation and privacy? Do we become less private merely because we use a tool or is it because the tool has changed us? What's in store for us with the internet of things combining with data mining techniques? All of these questions and more are explored in this book




Modern Data Strategy


Book Description

This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.




Big Data and Human-Environment Systems


Book Description

The first comprehensive treatment of data science as a new and powerful way to understand and manage human-environment interactions.




Process Safety and Big Data


Book Description

Process Safety and Big Data discusses the principles of process safety and advanced information technologies. It explains how these principles are applied to the process industry and provides examples of applications in process safety control and decision support systems.This book helps to address problems that researchers face in industry that are the result of increased process complexity and that have an impact on safety issues. It shows ways to tackle these safety issues by implementing modern information technologies, such as big data analysis and artificial intelligence. It provides an integrated approach to modern information technologies used in control and management of process safety in industry. The book also considers indicators and criteria in effective safety decisions, and addresses the issue of how big data would provide support for improved, autonomous, data-driven decisions. - Paves the way for the digital transformation of safety science and safety management - Takes a system approach to advanced information technologies used in process safety - Applies big data technologies to process safety - Includes multiple pertinent case studies




Blockchain for Big Data


Book Description

In recent years, the fast-paced development of social information and networks has led to the explosive growth of data. A variety of big data have emerged, encouraging researchers to make business decisions by analysing this data. However, many challenges remain, especially concerning data security and privacy. Big data security and privacy threats permeate every link of the big data industry chain, such as data production, collection, processing, and sharing, and the causes of risk are complex and interwoven. Blockchain technology has been highly praised and recognised for its decentralised infrastructure, anonymity, security, and other characteristics, and it will change the way we access and share information. In this book, the author demonstrates how blockchain technology can overcome some limitations in big data technology and can promote the development of big data while also helping to overcome security and privacy challenges. The author investigates research into and the application of blockchain technology in the field of big data and assesses the attendant advantages and challenges while discussing the possible future directions of the convergence of blockchain and big data. After mastering concepts and technologies introduced in this work, readers will be able to understand the technical evolution, similarities, and differences between blockchain and big data technology, allowing them to further apply it in their development and research. Author: Shaoliang Peng is the Executive Director and Professor of the College of Computer Science and Electronic Engineering, National Supercomputing Centre of Hunan University, Changsha, China. His research interests are high-performance computing, bioinformatics, big data, AI, and blockchain.




The Big Data Agenda


Book Description

This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Annika Richterich provides a systematic contemporary overview of the field of critical data studies that reflects on practices of digital data collection and analysis. The book assesses in detail one big data research area: biomedical studies, focused on epidemiological surveillance. Specific case studies explore how big data have been used in academic work. The Big Data Agenda concludes that the use of big data in research urgently needs to be considered from the vantage point of ethics and social justice. Drawing upon discourse ethics and critical data studies, Richterich argues that entanglements between big data research and technology/ internet corporations have emerged. In consequence, more opportunities for discussing and negotiating emerging research practices and their implications for societal values are needed.




The Data Revolution


Book Description

Our world is becoming ever more data-driven, transforming how business is conducted, governance enacted, and knowledge produced. Yet, the nature of data and the scope and implications of the changes taking place are not always clear. The Data Revolution is a must read for anyone interested in why data have become so important in the contemporary era. Thoroughly updated, including ten new chapters, the book provides an accessible and comprehensive: introduction to thinking conceptually about the nature of data and the field of critical data studies overview of big data, open data and data infrastructures analysis of the utility and value of big and open data for research, business, government and civil society assessment of the concerns and risks in a data-driven world and how to prevent and mitigate them.