Machine Learning For Societal Improvement Modernization And Progress

Learning has been fundamental to the growth and evolution of humanity and civilization. The same concepts of learning, applied to the tasks that machines can perform, are having a similar effect now. Machine learning is evolving computation and its applications like never before. It is now widely recognized that machine learning is playing a similar role to electricity in the late 19th and early 20th centuries in modernizing the world. From simple high school science projects to large-scale radio astronomy, machine learning has revolutionized it all—however, a few of the applications clearly stand out as transforming the world and opening up a new era. Machine Learning for Societal Improvement, Modernization, and Progress showcases the path-breaking applications of machine learning that are leading to the next generation of computing and living standards. The focus of the book is machine learning and its application to specific domains, which is resulting in substantial civilizational progress. Covering topics such as lifespan prediction, smart transportation networks, and socio-economic data, this premier reference source is a dynamic resource for data scientists, industry leaders, practitioners, students and faculty of higher education, sociologists, researchers, and academicians.

This book brings together the work of historians and sociologists with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMind’s AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents’ capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of “learning” does machine learning truly represent? We empirically address these questions and more to provide a baseline for future research. Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Frugal innovation is considered a new source of innovation, mainly to meet the needs of low-income customers. Hence, frugal innovation has primarily been explored emphasizing affordability. The concept of frugal and social innovation is a new idea and requires perspectives from academicians, researchers, and organizations to reach its full potential. Frugal Innovation and Social Transitions in the Digital Era considers the social value of innovation, frugal innovation, and social innovation in society at local, national, and international levels and calls the attention of scholars and researchers around the globe to focus on the social perspectives and social patterns of human life and society. Covering key topics such as emerging technologies, entrepreneurship, and social change, this reference work is ideal for computer scientists, business owners, managers, policymakers, researchers, scholars, practitioners, instructors, and students.

The world is undergoing a transformation as technology enters every ecosystem. Subsequently, there is a need to develop higher-order digital skills to ensure one's employability as professionals need to build digital competencies to remain competitive in the current work environment. Additionally, businesses must also continue to update their digital practices in order to remain relevant. Multidisciplinary Perspectives Towards Building a Digitally Competent Society explores multidisciplinary perspectives towards building a more digitally competent society, considers new business models and the need for organizations and individuals to develop the right mindset to embrace digitalization, and discusses how social capital can become a key driver in crafting a whole new digitally competent social fabric. Covering topics such as technological transformation, social media, and corporate social responsibility, this reference work is ideal for corporate practitioners, business owners, policymakers, scholars, researchers, practitioners, instructors, and students.

Research on digital reality has been extensive in recent years, covering a wide range of topics and leading to new ways to approach and deal with complex situations. Within the Society 5.0 paradigm, people and machines establish a positive relationship to find solutions for social aspects and problems. This perspective establishes a strong interconnection between physical and virtual space, making the user an active player for better life and society. In these terms, digital systems and virtual and augmented reality technologies enable multi-dimensional scenarios and additional levels of interdisciplinary collaboration to create a highly inclusive communication network and social framework. The Handbook of Research on Implementing Digital Reality and Interactive Technologies to Achieve Society 5.0 provides an overview of methods, processes, and tools adopted to achieve super-smart society needs by exploiting digital reality and interactive technologies. It includes case studies that illustrate applications that place people’s quality of life at the center of the digitalization process, accessing and managing different information and data domains. Covering topics such as cultural heritage, interactive learning, and virtual participation, this major reference work is a comprehensive resource for business executives and managers, IT managers, government officials, community leaders, arts and performance organizers, healthcare administrators and professionals, faculty and administrators of both K-12 and higher education, students of higher education, researchers, and academicians.

Individuals, businesses, organizations, and countries all benefit from having access to data. People who generate data do it voluntarily, forming their habits, patterns, and behaviors in the process. Their psychological characteristics will be better understood as a result of the data that they generate, allowing them to make intelligent decisions. Organizations are motivated by the desire to collect and analyze as much data as possible from the general public or future customers in order to better understand their psychological features and influence them to purchase their products or services. As a result, there has been a great deal of debate concerning the use of data from the perspectives of individuals, organizations, the public, and the government. Digital Psychology’s Impact on Business and Society considers the phenomena of digital psychology and society in general and evaluates individual strategies and those of businesses, organizations, and even nations. Covering topics such as big data, marketing, social media, and social computing, this reference work is ideal for policymakers, psychologists, business owners, managers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.