Augmented reality technology enables the creation of training that more closely resembles real-world environments without the cost and complexity of organizing large- scale training exercises in high-stakes domains that require recognition skills (e.g., military operations, emergency medicine). Augmented reality can be used to project virtual objects such as patients, medical equipment, colleagues, and terrain features onto any surface, transforming any space into a simulation center. Augmented reality can also be integrated into an existing simulation center. For example, a virtual patient can be mapped onto a physical manikin so learners can practice assessments skills on the highly tailorable virtual patient, and practice interventions on the physical manikin using the tools they use in their everyday work. This chapter sets the stage by describing how the author drew from their own experiences, reviewed scientific literature, and consulted with skilled instructors to articulate eleven design principles for creating augmented reality training.

Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations.

The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies.

Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.

An introduction to the concept of the book, the role it aims to play, and the intended audience. Traditionally, mathematics is often thought to progress through levels of difficulty, with category theory being very advanced and therefore only taught to advanced math students. However, in this book we take the view that mathematics is a network of interconnecting ideas, and that, while individual subject areas may progress cumulatively, there is no technical need to study any of them before category theory. So the book is aimed at a wide audience, especially including people who have not studied college-level mathematics.

This chapter introduces three cross-cutting themes that illustrate the relationship between artificial intelligence and international economic law (IEL): disruption, regulation, and reconfiguration. We explore the theme of disruption along the trifecta of AI-related technological, economic, and legal change. In this context, the impact of AI triggers political and economic pressures, as evidenced by intensive lobbying and engagement in different governance venues for and against various regulatory choices, including what will be regulated, how to regulate it, and whom should be regulated. Along these lines, we assess the extent to which IEL has already been reconfigured and examine the need for further reconfiguration. We conclude this introduction chapter by bringing the contributions we assembled in this volume into conversation with one another and identify topics that warrant further research. Taken as a whole, this book portrays the interaction between AI and IEL. We have collectively explored and evaluated the impact of AI disruption, the need for AI regulation, and directions for IEL reconfiguration.

Though worries about the impact of new technology have accompanied many inventions, AI is unusual in that some of the starkest recent warnings have come from those most knowledgeable about the field. Many of these concerns are linked to ‘general’ or ‘strong’ AI, meaning the creation of a system that is capable of performing any intellectual task that a human could – and raising complex questions about the nature of consciousness and self-awareness in a non-biological entity. The possibility that such an entity might put its own priorities above those of humans is non-trivial, but this book focuses on the more immediate challenges raised by ‘narrow’ AI – meaning systems that can apply cognitive functions to specific tasks typically undertaken by a human. The book is organized around the following sets of problems: How should we understand the challenges to regulation posed by the technologies loosely described here as ‘AI systems’? What regulatory tools exist to deal with those challenges and what are their limitations? And what more is needed – rules, institutions, actors – to enable us to reap the benefits offered by AI while minimizing avoidable harm?