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| future of data science 2030: Hello from 2030 Jan Paul Schutten, 2014-10-07 Contains predictions for 2030 that are related to health, food, technology, and more. |
| future of data science 2030: Sustainametrics - envisioning a sustainable future with data science Shutaro Takeda, Alexander Ryota Keeley, Shunsuke Managi, Thomas Gloria, 2023-03-08 |
| future of data science 2030: Data Science with Semantic Technologies Archana Patel, Narayan C. Debnath, 2023-06-20 As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This first volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic technologies. Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as: Can semantic technologies facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for data science? This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and, thus, it is a unique resource for scholars, researchers, professionals, and practitioners in this field. |
| future of data science 2030: Science Breakthroughs to Advance Food and Agricultural Research by 2030 National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Environmental Change and Society, Health and Medicine Division, Food and Nutrition Board, Division on Earth and Life Studies, Water Science and Technology Board, Board on Life Sciences, Board on Atmospheric Sciences and Climate, Board on Agriculture and Natural Resources, Committee on Science Breakthroughs 2030: A Strategy for Food and Agricultural Research, 2019-04-21 For nearly a century, scientific advances have fueled progress in U.S. agriculture to enable American producers to deliver safe and abundant food domestically and provide a trade surplus in bulk and high-value agricultural commodities and foods. Today, the U.S. food and agricultural enterprise faces formidable challenges that will test its long-term sustainability, competitiveness, and resilience. On its current path, future productivity in the U.S. agricultural system is likely to come with trade-offs. The success of agriculture is tied to natural systems, and these systems are showing signs of stress, even more so with the change in climate. More than a third of the food produced is unconsumed, an unacceptable loss of food and nutrients at a time of heightened global food demand. Increased food animal production to meet greater demand will generate more greenhouse gas emissions and excess animal waste. The U.S. food supply is generally secure, but is not immune to the costly and deadly shocks of continuing outbreaks of food-borne illness or to the constant threat of pests and pathogens to crops, livestock, and poultry. U.S. farmers and producers are at the front lines and will need more tools to manage the pressures they face. Science Breakthroughs to Advance Food and Agricultural Research by 2030 identifies innovative, emerging scientific advances for making the U.S. food and agricultural system more efficient, resilient, and sustainable. This report explores the availability of relatively new scientific developments across all disciplines that could accelerate progress toward these goals. It identifies the most promising scientific breakthroughs that could have the greatest positive impact on food and agriculture, and that are possible to achieve in the next decade (by 2030). |
| future of data science 2030: Data Science and Emerging Technologies Yap Bee Wah, Michael W. Berry, Azlinah Mohamed, Dhiya Al-Jumeily, 2023-03-31 The book presents selected papers from International Conference on Data Science and Emerging Technologies (DaSET 2022), held online at UNITAR International University, Malaysia, during December 20–21, 2022. This book aims to present current research and applications of data science and emerging technologies. The deployment of data science and emerging technology contributes to the achievement of the Sustainable Development Goals for social inclusion, environmental sustainability, and economic prosperity. Data science and emerging technologies such as artificial intelligence and blockchain are useful for various domains such as marketing, health care, finance, banking, environmental, and agriculture. An important grand challenge in data science is to determine how developments in computational and social-behavioral sciences can be combined to improve well-being, emergency response, sustainability, and civic engagement in a well-informed, data-driven society. The topics of this book include, but not limited to: artificial intelligence, big data technology, machine and deep learning, data mining, optimization algorithms, blockchain, Internet of Things (IoT), cloud computing, computer vision, cybersecurity, augmented and virtual reality, cryptography, and statistical learning. |
| future of data science 2030: Big Data Science and Analytics for Smart Sustainable Urbanism Simon Elias Bibri, 2019-05-30 We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability. |
| future of data science 2030: The Future of Nursing 2020-2030 National Academies of Sciences Engineering and Medicine, Committee on the Future of Nursing 2020-2030, 2021-09-30 The decade ahead will test the nation's nearly 4 million nurses in new and complex ways. Nurses live and work at the intersection of health, education, and communities. Nurses work in a wide array of settings and practice at a range of professional levels. They are often the first and most frequent line of contact with people of all backgrounds and experiences seeking care and they represent the largest of the health care professions. A nation cannot fully thrive until everyone - no matter who they are, where they live, or how much money they make - can live their healthiest possible life, and helping people live their healthiest life is and has always been the essential role of nurses. Nurses have a critical role to play in achieving the goal of health equity, but they need robust education, supportive work environments, and autonomy. Accordingly, at the request of the Robert Wood Johnson Foundation, on behalf of the National Academy of Medicine, an ad hoc committee under the auspices of the National Academies of Sciences, Engineering, and Medicine conducted a study aimed at envisioning and charting a path forward for the nursing profession to help reduce inequities in people's ability to achieve their full health potential. The ultimate goal is the achievement of health equity in the United States built on strengthened nursing capacity and expertise. By leveraging these attributes, nursing will help to create and contribute comprehensively to equitable public health and health care systems that are designed to work for everyone. The Future of Nursing 2020-2030: Charting a Path to Achieve Health Equity explores how nurses can work to reduce health disparities and promote equity, while keeping costs at bay, utilizing technology, and maintaining patient and family-focused care into 2030. This work builds on the foundation set out by The Future of Nursing: Leading Change, Advancing Health (2011) report. |
| future of data science 2030: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| future of data science 2030: The Broadcast 41 Carol A Stabile, 2018-10-16 How forty-one women—including Dorothy Parker, Gypsy Rose Lee, and Lena Horne—were forced out of American television and radio in the 1950s “Red Scare.” At the dawn of the Cold War era, forty-one women working in American radio and television were placed on a media blacklist and forced from their industry. The ostensible reason: so-called Communist influence. But in truth these women—among them Dorothy Parker, Lena Horne, and Gypsy Rose Lee—were, by nature of their diversity and ambition, a threat to the traditional portrayal of the American family on the airwaves. This book from Goldsmiths Press describes what American radio and television lost when these women were blacklisted, documenting their aspirations and achievements. Through original archival research and access to FBI blacklist documents, The Broadcast 41 details the blacklisted women's attempts in the 1930s and 1940s to depict America as diverse, complicated, and inclusive. The book tells a story about what happens when non-male, non-white perspectives are excluded from media industries, and it imagines what the new medium of television might have looked like had dissenting viewpoints not been eliminated at such a formative moment. The all-white, male-dominated Leave it to Beaver America about which conservative politicians wax nostalgic existed largely because of the forcible silencing of these forty-one women and others like them. For anyone concerned with the ways in which our cultural narrative is constructed, this book offers an urgent reminder of the myths we perpetuate when a select few dominate the airwaves. |
| future of data science 2030: 2030: How Today's Biggest Trends Will Collide and Reshape the Future of Everything Mauro F. Guillén, 2020-08-25 AN INTERNATIONAL BESTSELLER Wall Street Journal Bestseller A Porchlight Book Bestseller Financial Times Best Books of 2020 Yahoo Finance Favorite Business Books of 2020 JP Morgan NextList 2021 selection Bold, provocative...illuminates why we’re having fewer babies, the middle class is stagnating, unemployment is shifting, and new powers are rising.” —ADAM GRANT The world is changing drastically before our eyes—will you be prepared for what comes next? A groundbreaking analysis from one of the world's foremost experts on global trends, including analysis on how COVID-19 will amplify and accelerate each of these changes. Once upon a time, the world was neatly divided into prosperous and backward economies. Babies were plentiful, workers outnumbered retirees, and people aspiring towards the middle class yearned to own homes and cars. Companies didn't need to see any further than Europe and the United States to do well. Printed money was legal tender for all debts, public and private. We grew up learning how to play the game, and we expected the rules to remain the same as we took our first job, started a family, saw our children grow up, and went into retirement with our finances secure. That world—and those rules—are over. By 2030, a new reality will take hold, and before you know it: - There will be more grandparents than grandchildren - The middle-class in Asia and Sub-Saharan Africa will outnumber the US and Europe combined - The global economy will be driven by the non-Western consumer for the first time in modern history - There will be more global wealth owned by women than men - There will be more robots than workers - There will be more computers than human brains - There will be more currencies than countries All these trends, currently underway, will converge in the year 2030 and change everything you know about culture, the economy, and the world. According to Mauro F. Guillen, the only way to truly understand the global transformations underway—and their impacts—is to think laterally. That is, using “peripheral vision,” or approaching problems creatively and from unorthodox points of view. Rather than focusing on a single trend—climate-change or the rise of illiberal regimes, for example—Guillen encourages us to consider the dynamic inter-play between a range of forces that will converge on a single tipping point—2030—that will be, for better or worse, the point of no return. 2030 is both a remarkable guide to the coming changes and an exercise in the power of “lateral thinking,” thereby revolutionizing the way you think about cataclysmic change and its consequences. |
| future of data science 2030: Microslices John Dillard, 2015-08-13 THE WAY EXECUTIVES USE PROFESSIONAL SERVICES IS DYING. Are you ready to get the most out of what comes next? The longstanding business model of professional services is facing change unlike any other in its century-long history. Over the next 15 years, unrelenting advances in technology, data science, and corporate culture will fundamentally disrupt your “trusted advisors.” Exciting opportunities lie ahead for forward-thinking organizations, while disastrous threats await any buyer that’s unprepared to adopt a new service delivery model. MICROSLICES is a timely, eye-opening look at the changes that are already revolutionizing the professional services industry. It provides specific steps you must take as a buyer of those services to protect your organization from wasted consulting fees, outdated advice, and generic solutions. Consulting is dying. Your top adversaries will react to the future; will you? “Microslices is a great dive into understanding exactly why the boom in data sciences will completely change the way you use professional services. It’s, quite simply, a must-read.” Keith Ferrazzi author of Never Eat Alone and the #1 NY Times bestseller Who’s Got Your Back “The book provides an excellent view into the future for everyone that provides or utilizes professional services. It predicts the changes coming to the industry and how to embrace the changes in order to increase productivity and profitability.” Major General Steven W. Smith (Ret.) CEO of S.W. Smith & Associates For more information about Big Sky, visit www.bigskyassociates.com. |
| future of data science 2030: Smart Sustainable Cities of the Future Simon Elias Bibri, 2018-02-24 This book is intended to help explore the field of smart sustainable cities in its complexity, heterogeneity, and breadth, the many faces of a topical subject of major importance for the future that encompasses so much of modern urban life in an increasingly computerized and urbanized world. Indeed, sustainable urban development is currently at the center of debate in light of several ICT visions becoming achievable and deployable computing paradigms, and shaping the way cities will evolve in the future and thus tackle complex challenges. This book integrates computer science, data science, complexity science, sustainability science, system thinking, and urban planning and design. As such, it contains innovative computer–based and data–analytic research on smart sustainable cities as complex and dynamic systems. It provides applied theoretical contributions fostering a better understanding of such systems and the synergistic relationships between the underlying physical and informational landscapes. It offers contributions pertaining to the ongoing development of computer–based and data science technologies for the processing, analysis, management, modeling, and simulation of big and context data and the associated applicability to urban systems that will advance different aspects of sustainability. This book seeks to explicitly bring together the smart city and sustainable city endeavors, and to focus on big data analytics and context-aware computing specifically. In doing so, it amalgamates the design concepts and planning principles of sustainable urban forms with the novel applications of ICT of ubiquitous computing to primarily advance sustainability. Its strength lies in combining big data and context–aware technologies and their novel applications for the sheer purpose of harnessing and leveraging the disruptive and synergetic effects of ICT on forms of city planning that are required for future forms of sustainable development. This is because the effects of such technologies reinforce one another as to their efforts for transforming urban life in a sustainable way by integrating data–centric and context–aware solutions for enhancing urban systems and facilitating coordination among urban domains. This timely and comprehensive book is aimed at a wide audience across science, academia industry, and policymaking. It provides the necessary material to inform relevant research communities of the state–of–the–art research and the latest development in the area of smart sustainable urban development, as well as a valuable reference for planners, designers, strategists, and ICT experts who are working towards the development and implementation of smart sustainable cities based on big data analytics and context–aware computing. |
| future of data science 2030: Big Data Analytics in Future Power Systems Ahmed F. Zobaa, Trevor J. Bihl, 2018-08-14 Power systems are increasingly collecting large amounts of data due to the expansion of the Internet of Things into power grids. In a smart grids scenario, a huge number of intelligent devices will be connected with almost no human intervention characterizing a machine-to-machine scenario, which is one of the pillars of the Internet of Things. The book characterizes and evaluates how the emerging growth of data in communications networks applied to smart grids will impact the grid efficiency and reliability. Additionally, this book discusses the various security concerns that become manifest with Big Data and expanded communications in power grids. Provide a general description and definition of big data, which has been gaining significant attention in the research community. Introduces a comprehensive overview of big data optimization methods in power system. Reviews the communication devices used in critical infrastructure, especially power systems; security methods available to vet the identity of devices; and general security threats in CI networks. Presents applications in power systems, such as power flow and protection. Reviews electricity theft concerns and the wide variety of data-driven techniques and applications developed for electricity theft detection. |
| future of data science 2030: Designing Great Data Products Jeremy Howard, Margit Zwemer, Mike Loukides, 2012-03-23 In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next step: going from recommendations to products that can produce optimal strategies for meeting concrete business objectives. We already know how to build these products: they've been in use for the past decade or so, but they're not as common as they should be. This report shows how to take the next step: to go from simple predictions and recommendations to a new generation of data products with the potential to revolutionize entire industries. |
| future of data science 2030: Proceedings of the 11th Annual Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym11) Benjamin Goldberg, 2023-07-01 Welcome to the Proceedings of the 11th Annual GIFT User Symposium! This year we are celebrating 11 years of GIFT Symposiums and have accepted 15 papers for publication. All of the presentations that occurred at GIFTSym11, and the papers in this volume show the versatility of the Generalized Intelligent Framework for Tutoring (GIFT), and the work that is being done with GIFT. GIFT is an open-source intelligent tutoring system (ITS) architecture that is freely available online at GIFTtutoring.org. There are both Cloud and Downloadable version of GIFT. GIFT has been developed with multiple goals in mind including supporting ITS research, and simplified creation of ITSs and Adaptive Instructional Systems (AISs). Our fantastic team, and our program committee did a great job supporting the development of GIFTSym11, reviewing papers, and assisting with the facilitation of the event this year. We want to recognize them for their efforts: • Benjamin Goldberg • Gregory Goodwin • Michele Myers • Alexandra Lutz • Randall Spain • Lisa N. Townsend We were very pleased to have GIFTSym11 return to being an in-person event this year! Additionally, this was our first year offering a hybrid option for attendees. We are very happy that both modalities were well attended! |
| future of data science 2030: Learning to Love Data Science Mike Barlow, 2015-10-27 Until recently, many people thought big data was a passing fad. Data science was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you’ll appreciate how data science is fundamentally altering our world, for better and for worse. Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you’ll find out how far data science reaches. With this anthology, you’ll learn how: Analysts can now get results from their data queries in near real time Indie manufacturers are blurring the lines between hardware and software Companies try to balance their desire for rapid innovation with the need to tighten data security Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center CIOs have gradually evolved from order takers to business innovators New analytics tools let businesses go beyond data analysis and straight to decision-making Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries. |
| future of data science 2030: Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science Inam Ullah, Inam Ullah Khan, Mariya Ouaissa, Mariyam Ouaissa, Salma El Hajjami, 2024-06-14 Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science mainly focuses on the techniques of artificial intelligence (AI), Internet of Things (IoT) and data science for future communications systems. The goal of AI, IoT and data science for future communications systems is to create a venue for industry and academics to collaborate on the development of network and system solutions based on data science, AI and IoT. Recent breakthroughs in IoT, mobile and fixed communications and computation have paved the way for a data‐centric society of the future. New applications are increasingly reliant on machine‐to‐machine connections, resulting in unusual workloads and the need for more efficient and dependable infrastructures. Such a wide range of traffic workloads and applications will necessitate dynamic and highly adaptive network environments capable of self‐optimization for the task at hand while ensuring high dependability and ultra‐low latency. Networking devices, sensors, agents, meters and smart vehicles/systems generate massive amounts of data, necessitating new levels of security, performance and dependability. Such complications necessitate the development of new tools and approaches for providing successful services, management and operation. Predictive network analytics will play a critical role in insight generation, process automation required for adapting and scaling to new demands, resolving issues before they impact operational performance (e.g., preventing network failures and anticipating capacity requirements) and overall network decision‐making. To increase user experience and service quality, data mining and analytic techniques for inferring quality of experience (QoE) signals are required. AI, IoT, machine learning, reinforcement learning and network data analytics innovations open new possibilities in areas such as channel modeling and estimation, cognitive communications, interference alignment, mobility management, resource allocation, network control and management, network tomography, multi‐agent systems and network ultra‐broadband deployment prioritization. These new analytic platforms will aid in the transformation of our networks and user experience. Future networks will enable unparalleled automation and optimization by intelligently gathering, analyzing, learning and controlling huge volumes of information. |
| future of data science 2030: Artificial Intelligence and Software Engineering Derek Partridge, 2013-04-11 Managers, business owners, computer literate individuals, software developers, students, and researchers--all are looking for an understanding of artificial intelligence (AI) and what might be in the future. In this literate yet easy-to-read discussion, Derek Partridge explains what artificial intelligence can and cannot do, and what it holds for applications such as banking, financial services, and expert systems of all kinds. Topics include: the strengths and weaknesses of software development and engineering; machine learning and its promises and problems; expert systems and success stories; and practical software through artificial intelligence. |
| future of data science 2030: The Fourth Industrial Revolution Klaus Schwab, 2017-01-03 World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress. |
| future of data science 2030: Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing Valentina Emilia Balas, Aboul Ella Hassanien, Satyajit Chakrabarti, Lopa Mandal, 2021 This book includes selected papers presented at International Conference on Computational Intelligence, Data Science and Cloud Computing (IEM-ICDC) 2020, organized by the Department of Information Technology, Institute of Engineering & Management, Kolkata, India, during 25-27 September 2020. It presents substantial new research findings about AI and robotics, image processing and NLP, cloud computing and big data analytics as well as in cyber security, blockchain and IoT, and various allied fields. The book serves as a reference resource for researchers and practitioners in academia and industry. |
| future of data science 2030: Preparing for the Future of Artificial Intelligence Committee on Technology National Science and Technology Council, Committee on Technology, 2016-10-30 Advances in Artificial Intelligence (AI) technology have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, and the environment. In recent years, machines have surpassed humans in the performance of certain specific tasks, such as some aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue. Although it is very unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will reach and exceed human performance on more and more tasks. As a contribution toward preparing the United States for a future in which AI plays a growing role, this report surveys the current state of AI, its existing and potential applications, and the questions that are raised for society and public policy by progress in AI. The report also makes recommendations for specific further actions by Federal agencies and other actors. |
| future of data science 2030: Role of Science and Technology for Sustainable Future Ranbir Chander Sobti, |
| future of data science 2030: Overtourism Martha Honey, Kelsey Frenkiel, 2021-05-27 COVID-19 put a temporary stop to the crisis of overtourism. Yet there is no question that travel will resume; the only question is, when it does, what will it look like? Overtourism: Lessons for a Better Future charts a path toward tourism that is truly sustainable, focusing on the triple bottom line of people, planet, and prosperity. This practical book examines the causes and effects of overtourism before turning to emerging management strategies. Visitor education, traffic planning, and redirection to lesser known sites are among the measures that can protect the economic benefit of tourism without overwhelming local communities. As tourism revives around the world, these innovations will guide government agencies, parks officials, site managers, civic groups, environmental NGOs, tourism operators, and others with a stake in protecting our most iconic places. |
| future of data science 2030: Handbook of Sustainability Science in the Future Walter Leal Filho, Anabela Marisa Azul, Federica Doni, Amanda Lange Salvia, 2023-08-14 Humanity will have to cope with many problems in the coming decades: for instance, the world population is likely grow to to 8,8 billion people by 2035. Also, changing climate conditions are negatively affecting the livelihoods of millions of people. In particular, environmental disasters are causing substantial damages to properties. From a social perspective, the inequalities between rich and poor nations are becoming even deeper, and in many countries, conflicts between national and international interest groups are intensifying.The above state of affairs suggest that a broader understanding of the trends which may lead to a more sustainable world is needed, especially those which may pave the way for future developments. In other words, we need to pave the way for sustainable futures.Consistent with this reality, the proposed Encyclopedia of Sustainability Futures aims to identify, document and disseminate ideas, experiences and visions from scientists, member of nongovernmental organisations, decision-makers industry representatives and citizens, on themes and issues which will be important in pursuing sustainable future scenarios. In particular, the publication will focus on scientific aspects, as well as on social and economic ones, also considering matters related to financing and infra-structures, which are important in pursuing a sustainable future.The Encyclopedia of Sustainability Futures will involve the contributing authors in line with theprinciple of co-generation, from across a wide range of disciplines, e.g. education and social sciences, natural sciences, engineering, the arts, languages etc, with papers adopting a long-term sustainability perspective, with a time horizon until 2050. The focus will be on themes which are felt as important in the future, and the chapters are expected to interest and motivate a world audience.This book is part of the 100 papers to accelerate the implementation of the UN Sustainable Development Goals initiative! |
| future of data science 2030: Data Science of Renewable Energy Integration Yuichi Ikeda, |
| future of data science 2030: AI 2041 Kai-Fu Lee, Chen Qiufan, 2024-03-05 How will AI change our world within twenty years? A pioneering technologist and acclaimed writer team up for a “dazzling” (The New York Times) look at the future that “brims with intriguing insights” (Financial Times). This edition includes a new foreword by Kai-Fu Lee. A BEST BOOK OF THE YEAR: The Wall Street Journal, The Washington Post, Financial Times Long before the advent of ChatGPT, Kai-Fu Lee and Chen Qiufan understood the enormous potential of artificial intelligence to transform our daily lives. But even as the world wakes up to the power of AI, many of us still fail to grasp the big picture. Chatbots and large language models are only the beginning. In this “inspired collaboration” (The Wall Street Journal), Lee and Chen join forces to imagine our world in 2041 and how it will be shaped by AI. In ten gripping, globe-spanning short stories and accompanying commentary, their book introduces readers to an array of eye-opening settings and characters grappling with the new abundance and potential harms of AI technologies like deep learning, mixed reality, robotics, artificial general intelligence, and autonomous weapons. |
| future of data science 2030: The 4IR and the Humanities in South Africa Bhaso Ndzendze , Asheel Singh , Suzall Timm , 2024-05-28 The world is at a crossroads because of industrial change, compounded by a global pandemic. Humanities and social science education is grappling with the meaning of this change, to the effect that there have been some anxieties and misguided perceptions about the irrelevance of the humanities in this emerging new world. With the emergence of new technologies, this book highlights the indispensable centrality of humanity and the humanities going forward. The book will provide a reference point for new and innovative approaches to the humanities in the 4IR in South Africa and Africa. Its diverse content means that it will be useful across the humanities and social science spectrum. |
| future of data science 2030: Bold Peter H. Diamandis, Steven Kotler, 2016-02-23 Bold is a radical how-to guide for using exponential technologies, moonshot thinking, and crowd-powered tools to create extraordinary wealth while also positively impacting the lives of billions. A follow-up to the authors' Abundance (2012). |
| future of data science 2030: Data Science and Social Research II Paolo Mariani, Mariangela Zenga, 2020-11-25 The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019. |
| future of data science 2030: Future Smart James Canton, 2015-01-27 From the Chairman of the Institute for Global Futures, a forecast of game-changing trendsÑand how to manage and profit from them to better your life |
| future of data science 2030: AI and the Future of the Public Sector Tony Boobier, 2022-09-07 Discover how data, analytics, and AI will transform public services for the better In AI and the Future of the Public Sector: The Creation of Public Sector 4.0, renowned executive and consultant Tony Boobier delivers a comprehensive reference of the most relevant and central issues regarding the adoption and implementation of AI in the public sector. In the book, you’ll find out why data and analytics are the solution to significant and ongoing problems in the public service relating to its ability to effectively provide services in an environment of reduced funding. You’ll also discover the likely impact of future technological developments, like 5G and quantum computing, as well as explore the future of healthcare and the effective digitalization of the healthcare industry. The book also offers: Discussions of policing 4.0 and how data and analytics will transform public safety Explorations of the future of education and how ai can dramatically enhance educational standards while reducing costs Treatments of the internationalization of public services and its impact on agencies and departments everywhere A can’t-miss resource for public sector employees at the managerial and professional levels, AI and the Future of the Public Sector is an insightful and timely blueprint to the effective use of artificial intelligence that belongs in the bookshelves of policy makers, academics, and public servants around the world. |
| future of data science 2030: The Ultimate Modern Guide to Artificial Intelligence Enamul Haque, 2020-07-21 The era of artificial intelligence has arrived. You, who only felt far from artificial intelligence, and the growing dream trees, are now inseparable from artificial intelligence. What does AI have to do with me? Isn't it a distant future that has nothing to do with me, not a scientist, a technician, or a computer programmer? Well, Artificial intelligence is not a story of someone who has nothing to do with it, but the fact is, it is now everyone's story. AI is already deeply infiltrating everyone's life. The question is no longer whether we use technology or not; it's about working together in a better way. Surrounding technologies like Siri, Alexa, or Cortana are seamlessly integrated into our interactions. We walk into the room, turn on the lights, play songs, change the room temperature, keep track of shopping lists, book a ride at the airport, or remind ourselves to take the proper medication on time. It is now necessary to look at artificial intelligence from a broader and larger perspective. You should not just hang on to complex deep learning algorithms and think only through science and technology but through the eyes of emotions and humanities. These days, elementary school students learn English and coding at school. Tomorrow's elementary school students will learn AI. Of course, not everyone needs to be an AI expert. But if you don't understand AI, you will be left out of the trend of changing times. AI comes before English and coding. This is because artificial intelligence is the language and tool of the future. This book opens your door to the most critical understanding needed of AI and other relevant disruptive technologies. Artificial intelligence will significantly change societal structures and the operations of companies. The next generation of employees needs to be trained as a workforce before entering the job market, and the existing workforce is regularly recharged and skilled. There is plenty on this for reskilling too. This is the most definitive compendium of AI, The Internet of Things, Machine Learning, Deep Learning, Data Science, Big Data, Cloud Computing, Neural networks, Robotics, the future of work and the future of intelligent industries. |
| future of data science 2030: Geospatial Data Science in Healthcare for Society 5.0 Pradeep Kumar Garg, Nitin K. Tripathi, Martin Kappas, Loveleen Gaur, 2022-03-10 The book introduces a variety of latest techniques designed to represent, enhance, and empower multi-disciplinary approaches of geographic information system (GIS), artificial intelligence (AI), deep learning (DL), machine learning, and cloud computing research in healthcare. It provides a unique compendium of the current and emerging use of geospatial data for healthcare and reflects the diversity, complexity, and depth and breadth of this multi-disciplinary area. This book addresses various aspects of how smart healthcare devices can be used to detect and analyze diseases. Further, it describes various tools and techniques to evaluate the efficacy, suitability, and efficiency of geospatial data for health-related applications. It features illustrative case studies, including future applications and healthcare challenges. This book is beneficial for computer science and engineering students and researchers, medical professionals, and anyone interested in using geospatial data in healthcare. It is also intended for experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious to learn about this important subject. The book presents an effort to draw how we can build health-related applications using geospatial big data and their subsequent analysis. |
| future of data science 2030: Mastering Marketing Data Science Iain Brown, 2024-04-29 Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable. |
| future of data science 2030: Practical Data Analytics for Innovation in Medicine Gary D. Miner, Linda A. Miner, Scott Burk, Mitchell Goldstein, Robert Nisbet, Nephi Walton, Thomas Hill, 2023-02-08 Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies, Second Edition discusses the needs of healthcare and medicine in the 21st century, explaining how data analytics play an important and revolutionary role. With healthcare effectiveness and economics facing growing challenges, there is a rapidly emerging movement to fortify medical treatment and administration by tapping the predictive power of big data, such as predictive analytics, which can bolster patient care, reduce costs, and deliver greater efficiencies across a wide range of operational functions. Sections bring a historical perspective, highlight the importance of using predictive analytics to help solve health crisis such as the COVID-19 pandemic, provide access to practical step-by-step tutorials and case studies online, and use exercises based on real-world examples of successful predictive and prescriptive tools and systems. The final part of the book focuses on specific technical operations related to quality, cost-effective medical and nursing care delivery and administration brought by practical predictive analytics. Brings a historical perspective in medical care to discuss both the current status of health care delivery worldwide and the importance of using modern predictive analytics to help solve the health care crisis Provides online tutorials on several predictive analytics systems to help readers apply their knowledge on today’s medical issues and basic research Teaches how to develop effective predictive analytic research and to create decisioning/prescriptive analytic systems to make medical decisions quicker and more accurate |
| future of data science 2030: Opportunities from the Integration of Simulation Science and Data Science National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Committee on Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science in 2017-2020, 2018-07-31 Convergence has been a key topic of discussion about the future of cyberinfrastructure for science and engineering research. Convergence refers both to the combined use of simulation and data-centric techniques in science and engineering research and the possibilities for a single type of cyberinfrastructure to support both techniques. The National Academies of Science, Engineering, and Medicine convened a Workshop on Converging Simulation and Data-Driven Science on May 10, 2018, in Washington, D.C. The workshop featured speakers from universities, national laboratories, technology companies, and federal agencies who addressed the potential benefits and limitations of convergence as they relate to scientific needs, technological capabilities, funding structures, and system design requirements. This publication summarizes the presentations and discussions from the workshop. |
| future of data science 2030: NANO-CHIPS 2030 Boris Murmann, Bernd Hoefflinger, 2020-06-08 In this book, a global team of experts from academia, research institutes and industry presents their vision on how new nano-chip architectures will enable the performance and energy efficiency needed for AI-driven advancements in autonomous mobility, healthcare, and man-machine cooperation. Recent reviews of the status quo, as presented in CHIPS 2020 (Springer), have prompted the need for an urgent reassessment of opportunities in nanoelectronic information technology. As such, this book explores the foundations of a new era in nanoelectronics that will drive progress in intelligent chip systems for energy-efficient information technology, on-chip deep learning for data analytics, and quantum computing. Given its scope, this book provides a timely compendium that hopes to inspire and shape the future of nanoelectronics in the decades to come. |
| future of data science 2030: Past, Present and Future of Computing Education Research Mikko Apiola, Sonsoles López-Pernas, Mohammed Saqr, 2023-04-17 This book presents a collection of meta-studies, reviews, and scientometric analyses that together reveal a fresh picture about the past, present, and future of computing education research (CER) as a field of science. The book begins with three chapters that discuss and summarise meta-research about the foundations of CER, its disciplinary identity, and use of research methodologies and theories. Based on this, the book proceeds with several scientometric analyses, which explore authors and their collaboration networks, dissemination practices, international collaboration, and shifts in research focus over the years. Analyses of dissemination are deepened in two chapters that focus on some of the most influential publication venues of CER. The book also contains a series of country-, or region-level analyses, including chapters that focus on the evolution of CER in the Baltic Region, Finland, Australasia, Israel, and in the UK & Ireland. Two chapters present case studies of influential CER initiatives in Sweden and Namibia. This book also includes chapters that focus on CER conducted at school level, and cover crucially important issues such as technology ethics, algorithmic bias, and their implications for CER.In all, this book contributes to building an understanding of the past, present and future of CER. This book also contributes new practical guidelines, highlights topical areas of research, shows who to connect with, where to publish, and gives ideas of innovative research niches. The book takes a unique methodological approach by presenting a combination of meta-studies, scientometric analyses of publication metadata, and large-scale studies about the evolution of CER in different geographical regions. This book is intended for educational practitioners, researchers, students, and anyone interested in CER. This book was written in collaboration with some of the leading experts of the field. |
| future of data science 2030: Handbook of Dynamic Data Driven Applications Systems Erik Blasch, Sai Ravela, Alex Aved, 2018-11-13 The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies. Beginning with general concepts and history of the paradigm, the text provides 32 chapters by leading experts in10 application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal: Earth and Space Data Assimilation Aircraft Systems Processing Structures Health Monitoring Biological Data Assessment Object and Activity Tracking Embedded Control and Coordination Energy-Aware Optimization Image and Video Computing Security and Policy Coding Systems Design The authors explain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination. |
| future of data science 2030: Digital Work in the Planetary Market Mark Graham, Fabian Ferrari, 2022-06-14 Understanding the embedded and disembedded, material and immaterial, territorialized and deterritorialized natures of digital work. Many jobs today can be done from anywhere. Digital technology and widespread internet connectivity allow almost anyone, anywhere, to connect to anyone else to communicate and exchange files, data, video, and audio. In other words, work can be deterritorialized at a planetary scale. This book examines the implications for both work and workers when work is commodified and traded beyond local labor markets. Going beyond the usual “world is flat” globalization discourse, contributors look at both the transformation of work itself and the wider systems, networks, and processes that enable digital work in a planetary market, offering both empirical and theoretical perspectives. The contributors—leading scholars and experts from a range of disciplines—touch on a variety of issues, including content moderation, autonomous vehicles, and voice assistants. They first look at the new experience of work, finding that, despite its planetary connections, labor remains geographically sticky and embedded in distinct contexts. They go on to consider how planetary networks of work can be mapped and problematized, discuss the productive multiplicity and interdisciplinarity of thinking about digital work and its networks, and, finally, imagine how planetary work could be regulated. Contributors Sana Ahmad, Payal Arora, Janine Berg, Antonio A. Casilli, Julie Chen, Christina Colclough, Fabian Ferrari, Mark Graham, Andreas Hackl, Matthew Hockenberry, Hannah Johnston, Martin Krzywdzinski, Johan Lindquist, Joana Moll, Brett Neilson, Usha Raman, Jara Rocha, Jathan Sadowski, Florian A. Schmidt, Cheryll Ruth Soriano, Nick Srnicek, James Steinhoff, Jara Rocha, JS Tan, Paola Tubaro, Moira Weigel, Lin Zhang |
Over 100 Data, Analytics and AI Predictions Through 2030
Jun 19, 2024 · Over 100 Data, Analytics and AI redictions Through 2030 Our annual predictions highlight the continued impact and influence of data, analytics and increasingly AI
Thinking and Doing with Data - Designing 2030
• What must be done now and anticipated in the future in order to make data experiences both powerful and enduring? • What knowledge, scaffolds, and trajectories are necessary to equip and
Insight Report Data Science in the New Economy - World …
Jul 1, 2019 · his new source of value in the global economy. This Report focuses on data science, among the most competitive skills of the Fourth Industrial Revolution, in collaboration with …
ARTIFICIAL INTELLIGENCE AND LIFE IN 2030
Artificial Intelligence (AI) is a science and a set of computational technologies that are inspired by—but typically operate quite differently from—the ways people use their nervous systems …
DATA SCIENCE TODAY AND TOMORROW: AN OVERVIEW!
In this paper we review the research evidences on the benefits of the data science in the future 2024-2030. In this paper we give flash on the points: What is Data Science? What are benefits …
ASA Statement on The Role of Statistics in Data Science and …
Data science and AI rely heavily on statistics, mathematics, and computer science to gain knowledge from data. These fields produce tools to interact with data, provide effective and …
Future Of Data Science 2030 (PDF) - mira.fortuitous.com
Future Of Data Science 2030: Data Science with Semantic Technologies Archana Patel,Narayan C. Debnath,2023-06-20 As data is an important asset for any organization it is essential to …
IMPACT 2020–2030 Looking to the next decade of data and …
data has become perhaps the most valuable natural resource in today’s increasingly A.I.-driven economy, and a major value-generator for the biggest players in big tech. Companies around …
WPP OPEN DATA 2030
Apr 7, 2020 · We are moving from the digital revolution to a data revolution; creating a data universe in which data infuses our everyday lives, the decisions we make and the way we …
NIH STRATEGIC PLAN FOR DATA SCIENCE 2023-2028
Strategic Plan for Data Science articulates the NIH’s strategic views, goals, and objectives to advance data science in the next five years. By addressing these challenges, NIH will pioneer …
Informatics and data science perspective on Future of Nursing …
Jan 3, 2023 · Data science Introduction The Future of Nursing 2020 to 2030 report (National Academies of Sciences, Engineering, and Medicine, 2021) explicitly recommends integration of …
The future of data - KPMG
While data and AI can enable new business models, products, revenue streams and efficiencies, they are also a source of new risk. The challenge for boards is to actively transform their …
Data Center 2030 - Huawei
paper systematically describes the key technologies that will power future data centers, and proposes for the first time that future data centers will be computers that feature high degrees …
BIG DATA AND THE 2030 AGENDA FOR SUSTAINABLE …
Data science ‐ The gleaning of knowledge from data as a discipline that includes elements of programming, mathematics, modeling, engineering and visualization. Data silos ‐ Fixed or …
NIH Strategic Plan For Data Science 2025
collection and sharing to actively fostering effective and ethical data use. The 2025 - 2030 NIH Strategic Plan for Data Science provides a bold vision for the future of data science at the NIH. …
Artificial-intelligence-Trends and Predictions for 2030 - Qulix
This year Stanford University published a Report Artificial Intelligence and Life in 2030. The research includes forecasts and expectations of the experts from AI and further relevant areas …
Towards technology- and data-driven life science - SciLifeLab
In this roadmap document, we describe how SciLifeLab will leverage its national infrastructure for the benefit of Swedish research, recruitment, training, translation, innovation and utilization of …
INTERIM REPORT Data Driven Healthcare in 2030: …
Realising the data-driven healthcare, digital transformation and technology-supported organisational change ambitions of the NHS requires a workforce with the right job roles, and …
Are Synthetic Data a Real Concern? Substantive Predictions …
emphasizing the preference for real data, the review highlights synthetic data’s potential in overcoming access gaps, fostering research, and informing evidence-based policymaking, …
Over 100 Data, Analytics and AI Predictions Through 2030
Jun 19, 2024 · Over 100 Data, Analytics and AI redictions Through 2030 Our annual predictions highlight the continued impact and influence of data, analytics and increasingly AI
The Future of Data Science - Harvard Data Science Review
Sep 30, 2020 · Data science has emerged as a term to capture the broad range of concepts, methods, and tools involved in this transformation. We warmly commend Xuming He and …
Thinking and Doing with Data - Designing 2030
• What must be done now and anticipated in the future in order to make data experiences both powerful and enduring? • What knowledge, scaffolds, and trajectories are necessary to equip and
Insight Report Data Science in the New Economy - World …
Jul 1, 2019 · his new source of value in the global economy. This Report focuses on data science, among the most competitive skills of the Fourth Industrial Revolution, in collaboration with …
ARTIFICIAL INTELLIGENCE AND LIFE IN 2030
Artificial Intelligence (AI) is a science and a set of computational technologies that are inspired by—but typically operate quite differently from—the ways people use their nervous systems …
DATA SCIENCE TODAY AND TOMORROW: AN OVERVIEW!
In this paper we review the research evidences on the benefits of the data science in the future 2024-2030. In this paper we give flash on the points: What is Data Science? What are benefits …
ASA Statement on The Role of Statistics in Data Science and …
Data science and AI rely heavily on statistics, mathematics, and computer science to gain knowledge from data. These fields produce tools to interact with data, provide effective and …
Future Of Data Science 2030 (PDF) - mira.fortuitous.com
Future Of Data Science 2030: Data Science with Semantic Technologies Archana Patel,Narayan C. Debnath,2023-06-20 As data is an important asset for any organization it is essential to …
IMPACT 2020–2030 Looking to the next decade of data and …
data has become perhaps the most valuable natural resource in today’s increasingly A.I.-driven economy, and a major value-generator for the biggest players in big tech. Companies around …
WPP OPEN DATA 2030
Apr 7, 2020 · We are moving from the digital revolution to a data revolution; creating a data universe in which data infuses our everyday lives, the decisions we make and the way we …
NIH STRATEGIC PLAN FOR DATA SCIENCE 2023-2028
Strategic Plan for Data Science articulates the NIH’s strategic views, goals, and objectives to advance data science in the next five years. By addressing these challenges, NIH will pioneer …
Informatics and data science perspective on Future of …
Jan 3, 2023 · Data science Introduction The Future of Nursing 2020 to 2030 report (National Academies of Sciences, Engineering, and Medicine, 2021) explicitly recommends integration …
The future of data - KPMG
While data and AI can enable new business models, products, revenue streams and efficiencies, they are also a source of new risk. The challenge for boards is to actively transform their …
Data Center 2030 - Huawei
paper systematically describes the key technologies that will power future data centers, and proposes for the first time that future data centers will be computers that feature high degrees …
BIG DATA AND THE 2030 AGENDA FOR SUSTAINABLE …
Data science ‐ The gleaning of knowledge from data as a discipline that includes elements of programming, mathematics, modeling, engineering and visualization. Data silos ‐ Fixed or …
NIH Strategic Plan For Data Science 2025
collection and sharing to actively fostering effective and ethical data use. The 2025 - 2030 NIH Strategic Plan for Data Science provides a bold vision for the future of data science at the NIH. …
Artificial-intelligence-Trends and Predictions for 2030 - Qulix
This year Stanford University published a Report Artificial Intelligence and Life in 2030. The research includes forecasts and expectations of the experts from AI and further relevant areas …
Towards technology- and data-driven life science
In this roadmap document, we describe how SciLifeLab will leverage its national infrastructure for the benefit of Swedish research, recruitment, training, translation, innovation and utilization of …
INTERIM REPORT Data Driven Healthcare in 2030: …
Realising the data-driven healthcare, digital transformation and technology-supported organisational change ambitions of the NHS requires a workforce with the right job roles, and …
Are Synthetic Data a Real Concern? Substantive Predictions …
emphasizing the preference for real data, the review highlights synthetic data’s potential in overcoming access gaps, fostering research, and informing evidence-based policymaking, …
