The Future of Copyright in the Age of Artificial Intelligence offers an extensive analysis of intellectual property and authorship theories and explores the possible impact artificial intelligence might have on those theories. The author makes compelling arguments via the exploration of authorship, ownership and artificial intelligence. First, the book advocates for a more holistic approach to authorship, arguing that there is no good reason to exclude computer-generated and artificial intelligence creations from copyright. Second, it conducts an open search for the right 'candidate' for ownership. In doing so, the book explores several possible legal frameworks, including assigning ownership to the programmer, the user, the AI itself and other alternatives such as the public domain or author-in-law approaches. Third, the book explores the concept of artificial intelligence as it has developed through the years in various fields, seeking to reframe the AI legal concept. For academic scholars and law students these pages frame the theoretical discussion about artificial intelligence creations and provide in depth discussion about artificial intelligence legal challenges.

Stochastic processes have a wide range of applications ranging from image processing, neuroscience, bioinformatics, financial management, and statistics. Mathematical, physical, and engineering systems use stochastic processes for modeling and reasoning phenomena. While comparing AI-stochastic systems with other counterpart systems, we are able to understand their significance, thereby applying new techniques to obtain new real-time results and solutions. Stochastic Processes and Their Applications in Artificial Intelligence opens doors for artificial intelligence experts to use stochastic processes as an effective tool in real-world problems in computational biology, speech recognition, natural language processing, and reinforcement learning. Covering key topics such as social media, big data, and artificial intelligence models, this reference work is ideal for mathematicians, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

New and emerging technologies are reshaping justice systems and transforming the role of judges. The impacts vary according to how structural reforms take place and how courts adapt case management processes, online dispute resolution systems and justice apps. Significant shifts are also occurring with the development of more sophisticated forms of Artificial Intelligence that can support judicial work or even replace judges. These developments, together with shifts towards online court processes are explored in Judges, Technology and Artificial Intelligence. By considering how different jurisdictions are approaching current and future technological shifts and in particular by focusing on the different approaches in the US, UK, Australia and China and elsewhere, the author draws a rich comparative exploration of justice technology trends. Judicial commentary is considered as well as the growing scholarly discourse about these trends. Ethical and user centred design options are examined in the context of how responsive judges engage with supportive, replacement and disruptive technologies in courts. This book explores current issues regarding the responsiveness of the justice system in the pandemic era. In addition, how technology can respond and shift justice processes is a growing field of research, for judges, scholars, students and justice commentators. It provides a much-needed resource on an increasingly important topic.

a) Provides basic concepts of Natural Language Processing for getting started from scratch. b) Introduces advanced concepts for scaling, deep learning and real-world issues seen in the industry. c) Provides applications of Natural Language Processing over a diverse set of 15 industry verticals. d) Shares practical implementation including Python code, tools and techniques for a variety of Natural Language Processing applications and industrial products for a hands-on experience. e) Gives readers a sense of all there is to build successful Natural Language Processing projects: the concepts, applications, opportunities and hands-on material.

Artificial Intelligence (AI) is being rapidly introduced into the workplace, creating debate around what AI means for our work and organizations. This book gives grounded counterweight to provocative newspaper headlines by using in-depth case studies of eight organizations' experiences of implementing and using AI, providing readers with a solid understanding of what is actually happening in practice. Critical yet constructive, the authors address the challenges of implementing AI: organizing for data, testing and validating, algorithmic brokering, and changing work. Using a combination of existing literature and thorough practical examples, they provide answers to questions such as: What data do I need? When is a system good enough to actually take over tasks? And how can my employees be prepared for working with AI? The book presents four recommendations for WISE management of AI, requiring work-related insights, interdisciplinary knowledge, sociotechnical change processes, and ethical awareness. Offering insight into the unique characteristics of AI in organizations, this book will be essential reading for scholars of business and management, data analytics and information systems, technology and innovation, and computer science. With practical recommendations for managing the challenges of AI, it will also provide business managers with reflections to improve their own AI development and implementation processes.

Autonomous Mobile Robots: Planning, Navigation, and Simulation presents detailed coverage of the domain of robotics in motion planning and associated topics in navigation. This book covers numerous base planning methods from diverse schools of learning, including deliberative planning methods, reactive planning methods, task planning methods, fusion of different methods, and cognitive architectures. It is a good resource for doing initial project work in robotics, providing an overview, methods and simulation software in one resource. For more advanced readers, it presents a variety of planning algorithms to choose from, presenting the tradeoffs between the algorithms to ascertain a good choice. Finally, the book presents fusion mechanisms to design hybrid algorithms.

There is now a plethora of internet of things (IoT) devices on the market that can connect to the internet and the desired environment to produce sufficient and reliable data that is required by the government administration for a variety of purposes. Additionally, the potential benefits of incorporating artificial intelligence (AI) and machine learning into governance are numerous. Governments can use AI and machine learning to enforce the law, detect fraud, and monitor urban areas by identifying problems before they occur. The government can also use AI to easily automate processes and replace mundane and repetitive tasks. AI, IoT, and Blockchain Breakthroughs in E-Governance defines and emphasizes various AI algorithms as well as new internet of things and blockchain breakthroughs in the field of e-governance. Covering key topics such as machine learning, government, and artificial intelligence, this premier reference source is ideal for government officials, policymakers, researchers, academicians, practitioners, scholars, instructors, and students.

Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up!

In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks.

Build a Large Language Model (from Scratch) teaches you how to:

• Plan and code all the parts of an LLM
• Prepare a dataset suitable for LLM training
• Fine-tune LLMs for text classification and with your own data
• Use human feedback to ensure your LLM follows instructions
• Load pretrained weights into an LLM

Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant.

The advancement in FinTech especially artificial intelligence (AI) and machine learning (ML), has significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision making, macroeconomic analysis, and credit evaluation are getting more complex in the field of finance. ML is used in many financial companies which are making a significant impact on financial services. With the increasing complexity of financial transaction processes, ML can reduce operational costs through process automation which can automate repetitive tasks and increase productivity. Among others, ML can analyze large volumes of historical data and make better trading decisions to increase revenue. This book provides an exhaustive overview of the roles of AI and ML algorithms in financial sectors with special reference to complex financial applications such as financial risk management in a big data environment. In addition, it provides a collection of high-quality research works that address broad challenges in both theoretical and application aspects of AI in the field of finance.

Diagnostic Biomedical Signal and Image Processing Applications: With Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges, which are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.

Zeroing Neural Networks Describes the theoretical and practical aspects of finite-time ZNN methods for solving an array of computational problems Zeroing Neural Networks (ZNN) have become essential tools for solving discretized sensor-driven time-varying matrix problems in engineering, control theory, and on-chip applications for robots. Building on the original ZNN model, finite-time zeroing neural networks (FTZNN) enable efficient, accurate, and predictive real-time computations. Setting up discretized FTZNN algorithms for different time-varying matrix problems requires distinct steps. Zeroing Neural Networks provides in-depth information on the finite-time convergence of ZNN models in solving computational problems. Divided into eight parts, this comprehensive resource covers modeling methods, theoretical analysis, computer simulations, nonlinear activation functions, and more. Each part focuses on a specific type of time-varying computational problem, such as the application of FTZNN to the Lyapunov equation, linear matrix equation, and matrix inversion. Throughout the book, tables explain the performance of different models, while numerous illustrative examples clarify the advantages of each FTZNN method. In addition, the book: Describes how to design, analyze, and apply FTZNN models for solving computational problems Presents multiple FTZNN models for solving time-varying computational problems Details the noise-tolerance of FTZNN models to maximize the adaptability of FTZNN models to complex environments Includes an introduction, problem description, design scheme, theoretical analysis, illustrative verification, application, and summary in every chapter Zeroing Neural Networks: Finite-time Convergence Design, Analysis and Applications is an essential resource for scientists, researchers, academic lecturers, and postgraduates in the field, as well as a valuable reference for engineers and other practitioners working in neurocomputing and intelligent control.

Robotics for Cell Manipulation and Characterization provides fundamental principles underpinning robotic cell manipulation and characterization, state-of-the-art technical advances in micro/nano robotics, new discoveries of cell biology enabled by robotic systems, and their applications in clinical diagnosis and treatment. This book covers several areas, including robotics, control, computer vision, biomedical engineering and life sciences using understandable figures and tables to enhance readers' comprehension and pinpoint challenges and opportunities for biological and biomedical research.

Reachable Sets of Dynamic Systems: Uncertainty, Sensitivity, and Complex Dynamics introduces differential inclusions, providing an overview as well as multiple examples of its interdisciplinary applications. The design of dynamic systems of any type is an important issue as is the influence of uncertainty in model parameters and model sensitivity. The possibility of calculating the reachable sets may be a powerful additional tool in such tasks. This book can help graduate students, researchers, and engineers working in the field of computer simulation and model building, in the calculation of reachable sets of dynamic models.

Data Ethics of Power takes a reflective and fresh look at the ethical implications of transforming everyday life and the world through the effortless, costless, and seamless accumulation of extra layers of data. By shedding light on the constant tensions that exist between ethical principles and the interests invested in this socio-technical transformation, the book bridges the theory and practice divide in the study of the power dynamics that underpin these processes of the digitalization of the world. Gry Hasselbalch expertly draws on nearly two decades of experience in the field, and key literature, to advance a better understanding of the challenges faced by big data and AI developers. She provides an innovative ethical framework for studying and governing Big-Data and Artificial Intelligence. Offering both a historical account and a theoretical analysis of power dynamics and their ethical implications, as well as incisive ideas to guide future research and governance practices, the book makes a significant contribution to the establishment of an emerging data and AI ethics discipline. This timely book is a must-read for scholars studying AI, data, and technology ethics. Policymakers in the regulatory, governance, public administration, and management sectors will find the practical proposals for a human-centric approach to big data and AI to be a valuable resource for revising and developing future policies.

This incisive book provides a much-needed examination of the legal issues arising from the data economy, particularly in the light of the expanding role of algorithms and artificial intelligence in business and industry. In doing so, it discusses the pressing question of how to strike a balance in the law between the interests of a variety of stakeholders, such as AI industry, businesses and consumers. Investigating issues at the intersection of trade secrets and personal data as well as the potential legal conflicts to which this can give rise, Gintare Surblyte-Namaviciene examines what kinds of changes to the legal framework the growing data economy may require. Through an analysis of the way in which EU competition law may tackle algorithm-related problems the book also identifies a regulatory gap in the case of algorithmic manipulation in the business-to-consumer relationship. The book further argues that control by public bodies over terms and conditions often used in the data economy may be necessary for the sake of consumer protection. Scholars in competition law and regulatory governance, particularly those with an interest in the impacts of technology, will find this to be critical reading. It will also be beneficial to practitioners and policy makers working at the intersections of regulation and technology.

Artificial Intelligence for Sustainable Value Creation provides a detailed and insightful exploration of both the possibilities and the challenges that accompany widespread Artificial Intelligence. Providing a cutting edge analysis of the impact of AI in business and society, the editors offer an opportunity to assess what is known about managing other forms of information systems, strategy, and marketing, and to re-examine this knowledge in situations involving AI. This comprehensive book explores how human- centric AI systems create value inside organizations, distinguishing three main components: ethical value, societal value, and business value. Using a multidisciplinary perspective, this discerning book addresses the interests of a wide spectrum of practitioners, students, and researchers alike who are interested in identifying the value generated by AI systems in management.

Studies on integer optimization in emergency management have attracted engineers and scientists from various disciplines such as management, mathematics, computer science, and other fields. Although there are a large number of literature reports on integer planning and emergency events, few books systematically explain the combination of the two. Researchers need a clear and thorough presentation of the theory and application of integer programming methods for emergency management. Integer Optimization and its Computation in Emergency Management investigates the computation theory of integer optimization, developing integer programming methods for emergency management and explores related practical applications. Pursuing a holistic approach, this book establishes a fundamental framework for this topic, intended for graduate students who are interested in operations research and optimization, researchers investigating emergency management, and algorithm design engineers working on integer programming or other optimization applications.

Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues. Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.

Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach - putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

Artificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in various sectors. Owing to the multidisciplinary nature, this book primarily focuses on the concepts of AIML and its methodologies such as evolutionary techniques, neural networks, machine learning, deep learning, block chain technology, big data analytics, and image processing in the context of smart cities. The text also discusses possible solutions to different challenges posed by smart cities by presenting cutting edge AIML techniques using different methodologies, as well as future directions for those same techniques.

Integrated Human-Machine Intelligence: Beyond Artificial Intelligence focuses on deep situational awareness in human-computer integration, covering the interaction and integration mechanisms of human intelligence, machine intelligence and environmental systems. The book also details the cognitive, philosophical, social, scientific and technological, and military theories and methods of human-computer division, cooperation and collaborative decision-making to provide basic theoretical support for a development strategy in the field of national intelligence. Sections focus on describing a new form of intelligence produced by the interaction of human, machine and environmental systems which will become the next generation of AI. From the perspective of deep situational awareness in human-computer integration, the book studies the interaction and integration mechanisms of human intelligence, machine intelligence and environmental systems. In addition, it details the cognitive, philosophical, social, scientific and technological, and military theories and methods of human-computer division, cooperation and collaborative decision-making, so as to provide basic theoretical support for a development strategy in the field of national intelligence.