Artificial Generation: Photogenic French Literature and the Prehistory of Cinematic Modernity investigates the intersection of film theory and nineteenth-century literature, arguing that the depth of amalgamation that occurred within literary representation during this era aims to replicate an illusion of life and its sensations, in ways directly related to broader transitions into our modern cinematic age. A key part of this evolution in representation relies on the continual re-emergence of the artificial woman as longstanding expression of masculine artistic subjectivity, which, by the later nineteenth century, becomes a photographic and filmic drive. Moving through the beginning of film history, from Georges Méliès and other “silent” filmmakers in the 1890s, into more contemporary movies, including Alfred Hitchcock’s Vertigo (1958) and Blade Runner 2049 (2017), the book analyzes how films are often structured around the prior century’s mythic and literary principles, which now serve as foundation for film as medium—a phantom form for life’s re-presentation. Artificial Generation provides a crucial reassessment of the longstanding, mutual exchange between cinematic and literary reproduction, offering an innovative perspective on the proto-cinematic imperative of simulation within nineteenth-century literary symbolism.

Artificial Intelligence for Future Generation Robotics offers a vision for potential future robotics applications for AI technologies. Each chapter includes theory and mathematics to stimulate novel research directions based on the state-of-the-art in AI and smart robotics. Organized by application into ten chapters, this book offers a practical tool for researchers and engineers looking for new avenues and use-cases that combine AI with smart robotics. As we witness exponential growth in automation and the rapid advancement of underpinning technologies, such as ubiquitous computing, sensing, intelligent data processing, mobile computing and context aware applications, this book is an ideal resource for future innovation. Brings AI and smart robotics into imaginative, technically-informed dialogue Integrates fundamentals with real-world applications Presents potential applications for AI in smart robotics by use-case Gives detailed theory and mathematical calculations for each application Stimulates new thinking and research in applying AI to robotics

Distributed Artificial Intelligence (DAI) is a dynamic area of research and this book is the first comprehensive, truly integrated exposition of the discipline presenting influential contributions from leaders in the field. Commences with a solid introduction to the theoretical and practical issues of DAI, followed by a discussion of the core research topics--communication, coordination, planning--and how they are related to each other. The third section describes a number of DAI testbeds, illustrating particular strategies commissioned to provide software environments for building and experimenting with DAI systems. The final segment contains contributions which consider DAI from different perspectives.

How to raise kids who can handle the real world Today's Generation iY (teens brought up with the Internet) and Homelanders (children born after 9/11) are overexposed to information at an earlier age than ever and paradoxically are underexposed to meaningful relationships and real-life experiences. Artificial Maturity addresses the problem of what to do when parents and teachers mistake children's superficial knowledge for real maturity. The book is filled with practical steps that adults can take to furnish the experiences kids need to balance their abilities with authentic maturity. Shows how to identify the problem of artificial maturity in Generation iY and Homelanders Reveals what to do to help children balance autonomy, responsibility, and information Includes a down-to-earth model for coaching and guiding youth to true maturity Artificial Maturity gives parents, teachers, and others who work with youth a manual for understanding and practicing the leadership kids so desperately need to mature in a healthy fashion.

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions Methods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure

The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry Comprehensive resource describing how operations, outputs, and offerings of the oil and gas industry can improve via advancements in AI The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry describes the proven and promising digital technologies and tools available to empower the oil and gas industry to be future-ready. It shows how the widely reported limitations of the oil and gas industry are being nullified through the application of breakthrough digital technologies and how the convergence of digital technologies helps create new possibilities and opportunities to take this industry to its next level. The text demonstrates how scores of proven digital technologies, especially in AI, are useful in elegantly fulfilling complicated requirements such as process optimization, automation and orchestration, real-time data analytics, productivity improvement, employee safety, predictive maintenance, yield prediction, and accurate asset management for the oil and gas industry. The text differentiates and delivers sophisticated use cases for the various stakeholders, providing easy-to-understand information to accurately utilize proven technologies towards achieving real and sustainable industry transformation. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry includes information on: How various machine and deep learning (ML/DL) algorithms, the prime modules of AI, empower AI systems to deliver on their promises and potential Key use cases of computer vision (CV) and natural language processing (NLP) as they relate to the oil and gas industry Smart leverage of AI, the Industrial Internet of Things (IIoT), cyber physical systems, and 5G communication Event-driven architecture (EDA), microservices architecture (MSA), blockchain for data and device security, and digital twins Clearly expounding how the power of AI and other allied technologies can be meticulously leveraged by the oil and gas industry, The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry is an essential resource for students, scholars, IT professionals, and business leaders in many different intersecting fields.

This book analyses various models of value creation in projects and businesses by applying different forms of Artificial Intelligence in their products and services. First presenting the main concepts and ideas behind AI, Wodecki assesses different models of technology-based value creation based upon the analysis of over 400 case studies. This framework shows how AI may influence both value creation and competitive advantage (efficiency, creativity and flexibility) within a modern organization. Finally, a conceptual model is formulated to evaluate AI-supported in-company projects and new ventures and identify the key managerial and technical competencies required.