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1
Bennaceur, Amel, Reiner Hähnle, and Karl Meinke, eds. Machine Learning for Dynamic Software Analysis: Potentials and Limits. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96562-8.
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2
Polyakova, Anna, Tat'yana Sergeeva, and Irina Kitaeva. The continuous formation of the stochastic culture of schoolchildren in the context of the digital transformation of general education. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1876368.
Повний текст джерелаАнотація:
The material presented in the monograph shows the possibilities of continuous teaching of mathematics at school, namely, the significant potential of modern information and communication technologies, with the help of which it is possible to form elements of stochastic culture among students. Continuity in learning is considered from two positions: procedural and educational-cognitive. In addition, a distinctive feature of the book is the presentation of the digital transformation of general education as a way to overcome the "new digital divide". Methodological features of promising digital technologies (within the framework of teaching students the elements of the probabilistic and statistical line) that contribute to overcoming the "new digital divide": artificial intelligence, the Internet of Things, additive manufacturing, machine learning, blockchain, virtual and augmented reality are described.
The solution of the main questions of probability theory and statistics in the 9th grade mathematics course is proposed to be carried out using a distance learning course built in the Moodle distance learning system. The content, structure and methodological features of the implementation of the stochastics course for students of grades 10-11 of a secondary school are based on the use of such tools in the educational process as an online calculator for plotting functions, the Wolfram Alpha service, Google Docs and Google Tables services, the Yaklass remote training, the Banktest website.<url>", interactive module "Galton Board", educational website "Mathematics at school".
It will be interesting for students, undergraduates, postgraduates, mathematics teachers, as well as specialists improving their qualifications in the field of pedagogical education.
3
Taha, Zahari, Rabiu Muazu Musa, Mohamad Razali Abdullah, and Anwar P.P.Abdul Majeed. Machine Learning in Sports: Identifying Potential Archers. Springer, 2018.
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4
Pumperla, Max, Alex Tellez, and Michal Malohlava. Mastering Machine Learning with Spark 2.x: Harness the potential of machine learning, through spark. Packt Publishing - ebooks Account, 2017.
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5
Quantum Machine Learning: Unleashing Potential in Science and Industry. Primedia eLaunch LLC, 2023.
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6
Nagel, Stefan. Machine Learning in Asset Pricing. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691218700.001.0001.
Повний текст джерелаАнотація:
Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. This book examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, the book discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. The book presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
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AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.
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8
Jaswal, Gaurav, Vivek Kanhangad, and Raghavendra Ramachandra. AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.
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9
Jaswal, Gaurav, Vivek Kanhangad, and Raghavendra Ramachandra. AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.
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10
U.S. Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models. RAND Corporation, 2022. http://dx.doi.org/10.7249/rr-a284-1.
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11
Soulava, Blanka, Victoria Ying, and Hamish Cameron. Data Rules for Machine Learning: How Europe Can Unlock the Potential While Mitigating the Risks. Atlantic Council, 2021.
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12
Robson, Sean, Maria C. Lytell, Kimberly Curry Hall, Matthew Walsh, and Kirsten M. Keller. U. S. Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models. RAND Corporation, The, 2022.
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13
Muggleton, Stephen, and Nicholas Chater, eds. Human-Like Machine Intelligence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.001.0001.
Повний текст джерелаАнотація:
In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s–2030s), and is being actively investigated in the US, Europe and China. The EPSRC’s UK network on Human-Like Computing (HLC) was one of the first internationally to initiate and support research specifically in this area. Starting activities in 2018, the network represents around sixty leading UK groups Artificial Intelligence and Cognitive Scientists involved in the development of the inter-disciplinary area of HLC. The research of network groups aims to address key unsolved problems at the interface between Psychology and Computer Science. The chapters of this book have been authored by a mixture of these UK and other international specialists based on recent workshops and discussions at the Machine Intelligence 20 and 21 workshops (2016,2019) and the Third Wave Artificial Intelligence workshop (2019). Some of the key questions addressed by the Human-Like Computing programme include how AI systems might 1) explain their decisions effectively, 2) interact with human beings in natural language, 3) learn from small numbers of examples and 4) learn with minimal supervision. Solving such fundamental problems involves new foundational research in both the Psychology of perception and interaction as well as the development of novel algorithmic approaches in Artificial Intelligence.
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Mungoli, Neelesh. Breaking Barriers with AI : Empowering Latin America Through Machine Learning: Unleashing the Potential of Artificial Intelligence to Transform Latin America's Economy, Society, and Future. Absolute Author Publishing House, 2023.
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15
Vallor, Shannon, and George A. Bekey. Artificial Intelligence and the Ethics of Self-Learning Robots. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190652951.003.0022.
Повний текст джерелаАнотація:
The convergence of robotics technology with the science of artificial intelligence is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors. Recent advances in machine learning techniques have produced artificial agents that can acquire highly complex skills formerly thought to be the exclusive province of human intelligence. These developments raise a host of new ethical concerns about the responsible design, manufacture, and use of robots enabled with artificial intelligence—particularly those equipped with self-learning capacities. While the potential benefits of self-learning robots are immense, their potential dangers are equally serious. While some warn of a future where AI escapes the control of its human creators or even turns against us, this chapter focuses on other, far less cinematic risks of AI that are much nearer to hand, requiring immediate study and action by technologists, lawmakers, and other stakeholders.
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Machine Learning for Dynamic Software Analysis : Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, ... Papers. Springer, 2018.
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17
Dimick, William. Python : 3 Books in 1: Beginner's Guide, Data Science and Machine Learning. the Easiest Guide to Get Started in Python Programming. Unlock Your Programmer Potential and Develop Your Project in Just 30 Days. Phormictopus Publishing, 2020.
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18
Dimick, William. Python : 3 Books in 1: Beginner's Guide, Data Science and Machine Learning. the Easiest Guide to Get Started in Python Programming. Unlock Your Programmer Potential and Develop Your Project in Just 30 Days. Phormictopus Publishing, 2020.
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19
Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.
Повний текст джерелаАнотація:
Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ
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Barker, Richard. Achieving future impact. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198737780.003.0007.
Повний текст джерелаАнотація:
To propel change forward we need not just a good sense of direction but also a sense of the prize, for patients and the health system, if we are successful. A wide range of new technologies, from technologies now coming into our hands, from gene editing to machine learning, have the potential to empower precision medicine to overcome some of mankind’s most intractable challenges: cancer, inherited diseases, aging, dementia—among many others. Taken together, the changes we propose to the innovation process could bring at least an order of magnitude greater net patient benefit over the lifetime of products, as a result of faster development, better targeting, more consistent reimbursement, swifter adoption, and better utilization.
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Villez, Kris, Daniel Aguado, Janelcy Alferes, Queralt Plana, Maria Victoria Ruano, and Oscar Samuelsson, eds. Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems. IWA Publishing, 2024. http://dx.doi.org/10.2166/9781789061154.
Повний текст джерелаАнотація:
In recent years, the wastewater treatment field has undergone an instrumentation revolution. Thanks to increased efficiency of communication networks and extreme reductions in data storage costs, wastewater plants have entered the era of big data. Meanwhile, artificial intelligence and machine learning tools have enabled the extraction of valuable information from large-scale datasets. Despite this potential, the successful deployment of AI and automation depends on the quality of the data produced and the ability to analyze it usefully in large quantities. Metadata, including a quantification of the data quality, is often missing, so vast amounts of collected data quickly become useless. Ultimately, data-dependent decisions supported by machine learning and AI will not be possible without data readiness skills accounting for all the Vs of big data: volume, velocity, variety, and veracity. Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems provides recommendations to handle these challenges, and aims to clarify metadata concepts and provide advice on their practical implementation in water resource recovery facilities. This includes guidance on the best practices to collect, organize, and assess data and metadata, based on existing standards and state-of-the-art algorithmic tools. This Scientific and Technical Report offers a great starting point for improved data management and decision making, and will be of interest to a wide audience, including sensor technicians, operational staff, data management specialists, and plant managers. ISBN: 9781789061147 (Paperback) ISBN: 9781789061154 (eBook) ISBN: 9781789061161 (ePub)
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Bi, Xiaojun, Andrew Howes, Per Ola Kristensson, Antti Oulasvirta, and John Williamson. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0001.
Повний текст джерелаАнотація:
This chapter introduces the field of computational interaction, and explains its long tradition of research on human interaction with technology that applies to human factors engineering, cognitive modelling, artificial intelligence and machine learning, design optimization, formal methods, and control theory. It discusses how the book as a whole is part of an argument that, embedded in an iterative design process, computational interaction design has the potential to complement human strengths and provide a means to generate inspiring and elegant designs without refuting the part played by the complicated, and uncertain behaviour of humans. The chapters in this book manifest intellectual progress in the study of computational principles of interaction, demonstrated in diverse and challenging applications areas such as input methods, interaction techniques, graphical user interfaces, information retrieval, information visualization, and graphic design.
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Rolls, Edmund T. Brain Computations. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198871101.001.0001.
Повний текст джерелаАнотація:
The subject of this book is how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed. The book will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics.
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Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.
Повний текст джерелаАнотація:
Diagnostic radiology is a medical specialty that is primarily devoted to the diagnostic process, centered on the interpretation of medical images. This book reviews the high level of uncertainty inherent to radiological interpretation and the overlap that exists between the uncertainty of the process and what might be considered “error.” There is also a great deal of variability inherent in the physical and technological aspects of the imaging process itself. The information in diagnostic images is subtly encoded, with a broad range of “normal” that usually overlaps the even broader range of “abnormal.” Image interpretation thus blends technology, medical science, and human intuition. To develop their skillset, radiologists train intensively for years, and most develop a remarkable level of expertise. But radiology itself remains a fallible human endeavor, one involving complex neurophysiological and cognitive processes employed under a range of conditions and generally performed under time pressure. This book highlights the human experience of error. A taxonomy of error is presented, along with a theoretical classification of error types based on the underlying causes and an extensive discussion of potential error-reduction strategies. The relevant perceptual science, cognitive science, and imaging science are reviewed. A chapter addresses the issue of accountability for error, including peer review, regulatory oversight/accreditation, and malpractice litigation. The potential impact of artificial intelligence, including the use of machine learning and deep-learning algorithms, to reduce human error and improve radiologists’ efficiency is also explored.
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Zhai, Xiaoming, and Joseph Krajcik, eds. Uses of Artificial Intelligence in STEM Education. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198882077.001.0001.
Повний текст джерелаАнотація:
Abstract In the age of rapid technological advancements, the integration of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in science, technology, engineering, and mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. This book, comprising twenty-six chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools. As the chapters unfold, readers learn about innovative AI applications, from automated scoring systems in biology, chemistry, physics, mathematics, and engineering to intelligent tutors and adaptive learning. The book also touches upon the nuances of AI in supporting diverse learners, including students with learning disabilities, and the ethical considerations surrounding AI's growing influence in educational settings. It showcases the transformative potential of AI in reshaping STEM education, emphasizing the need for adaptive pedagogical strategies that cater to diverse learning needs in an AI-centric world. The chapters further delve into the practical applications of AI, from scoring teacher observations and analyzing classroom videos using neural networks to the broader implications of AI for STEM assessment practices. Concluding with reflections on the new paradigm of AI-based STEM education, this book serves as a comprehensive guide for educators, researchers, and policymakers, offering insights into the future of STEM education in an AI-driven world.
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Plecháč, Petr. Versification and Authorship Attribution. Karolinum Press, 2021. http://dx.doi.org/10.14712/9788024648903.
Повний текст джерелаАнотація:
The technique known as contemporary stylometry uses different methods, including machine learning, to discover a poem’s author based on features like the frequencies of words and character n-grams. However, there is one potential textual fingerprint stylometry tends to ignore: versification, or the very making of language into verse. Using poetic texts in three different languages (Czech, German, and Spanish), Petr Plecháč asks whether versification features like rhythm patterns and types of rhyme can help determine authorship. He then tests its findings on two unsolved literary mysteries. In the first, Plecháč distinguishes the parts of the Elizabethan verse play The Two Noble Kinsmen written by William Shakespeare from those written by his coauthor, John Fletcher. In the second, he seeks to solve a case of suspected forgery: how authentic was a group of poems first published as the work of the nineteenth-century Russian author Gavriil Stepanovich Batenkov? This book of poetic investigation should appeal to literary sleuths the world over.
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Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.
Повний текст джерелаАнотація:
This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design more expressive, efficient and versatile interaction.
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Dean, Roger T., and Alex McLean, eds. The Oxford Handbook of Algorithmic Music. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.001.0001.
Повний текст джерелаАнотація:
Algorithmic music appears to be at a turning point in its history, with many new systems and communities of practice developing together, as vibrant musical culture. This handbook brings together dozens of leading researchers and practitioners in the field, blending technical, artistic, cultural and scientific viewpoints into a whole that considers the making of algorithmic music as a rich, and essentially human activity. The book is organised into four sections, the first grounding the topic in the history, philosophy and psychology of algorithmic music. The second section asks 'what can algorithms in music do?', finding answers in computer science, mathematics, machine learning, bio-inspired computation, manipulation of pattern, computational creativity, and live coding. The third section focuses on the music maker, and the role of algorithms in supporting network music, sonification, music interface design, music in computer games, and spatialisation. The final section opens out to culture at large, and considers algorithmic music in terms of its audience reception, sociology, education, politics and the potential for mass consumption. Perhaps just as importantly, these sections are interleaved with reflective pieces from leading practitioners in the field, allowing us to to grasp the pragmatics of making music with algorithms. Combined, these diverse standpoints provide an absorbing, authoritative survey of research and practice from across the algorithmic music field.
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Briggs, Andrew, and Michael J. Reiss. Human Flourishing. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198850267.001.0001.
Повний текст джерелаАнотація:
Possibly since humans were first capable of asking the question, certainly since the dawn of history, humans have asked why we are here and what good life consists of. Much of humanity still finds the ultimate answers to meaning and purpose in religion. But in countries across the globe, secular views are widely held. Whether religious or secular, individuals, communities and governments still have to make decisions about what people want from life. We examine what is meant by human flourishing and see what it has to offer for those seeking after truth, meaning and purpose. We argue that the concept of human flourishing provides a valuable framework within which to consider the importance of satisfying people’s yearnings for material goods, successful relationships and the hope that we can achieve and experience things that give us a sense of something greater than ourselves—the transcendent. The transcendent is not discerned only within religion; for many, the arts, nature, wilderness and a consideration of our place in the Universe are all instances of routes towards an appreciation of something beyond. The analysis we offer is of value in considering how humanity should regard developments in new technologies. Advances in artificial intelligence, machine learning and gene technologies are likely radically within a generation or two to affect employment, healthcare, education, communications, transport and many other areas of life. How is humanity to evaluate the potential of such developments and steer them so as to promote human flourishing?
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Volpi, Elena, Jong Suk Kim, Shaleen Jain, and Sangam Shrestha, eds. Artificial Intelligence in Hydrology. IWA Publishing, 2024. http://dx.doi.org/10.2166/9781789064865.
Повний текст джерелаАнотація:
Abstract Nowadays, hydrological systems are becoming increasingly complex owing to the growing interaction between nature and humans at the local scale of river sections, lakes, reservoirs, catchments, etc., to the global scale. There is great demand for the development of models to evaluate, predict, and optimize the performance of complex hydrological systems whose behaviour is characterized by a strong nonlinearity. However, traditional approaches can hardly handle this nonlinear behaviour; moreover, the analysis of hydrological systems at large or even global scale, requires dealing with large-volume and real-time data. In recent years, artificial intelligence (AI), especially deep learning, has shown great potential to process massive data and solve large-scale nonlinear problems. AI has been successfully applied to computer vision, machine translation, bioinformatics, drug design, and climate science. AI models have produced results comparable to and even better than expert human performance. It is expected that AI can significantly contribute to hydrology research as well as development. This book presents some of the latest advances in the field of AI in hydrology. Both theoretical and experimental chapters are included, covering new and emerging AI methods and models from various challenging problems in hydrology. In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.