Chapter 1 aims to correct popular misinformation and summarizes how intelligence is defined and measured for scientific research. Some of the validity data will surprise you. For example, childhood IQ scores predict adult mortality.

This chapter is about semantic annotation, discussed from formal and computational perspectives. Annotation is viewed as a scholarly technique or methodology. This chapter explains what annotation was in general and is now, how annotation has gradually developed and become applicable to the automatic building of larger data in language, and what applications semantic annotation aims at and what principles govern the modeling of semantic annotation schemes. There are two governing principles discussed: the partiality and situatedness of information to be annotated. I also mention the use of machine learning techniques for the automatic annotation of language data, which is represented either in a textual form or in a graphic form.

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.

This new book on mathematical logic by Jeremy Avigad gives a thorough introduction to the fundamental results and methods of the subject from the syntactic point of view, emphasizing logic as the study of formal languages and systems and their proper use. Topics include proof theory, model theory, the theory of computability, and axiomatic foundations, with special emphasis given to aspects of mathematical logic that are fundamental to computer science, including deductive systems, constructive logic, the simply typed lambda calculus, and type-theoretic foundations.

Clear and engaging, with plentiful examples and exercises, it is an excellent introduction to the subject for graduate students and advanced undergraduates who are interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for any practicing logician’s bookshelf.

The author spells out the different key features of AI systems, introducing inter alia the notions of machine learning and deep learning as well as the use of AI systems as part of robotics.

This chapter first defines data science, its primary objectives, and several related terms. It continues by describing the evolution of data science from the fields of statistics, operations research, and computing. The chapter concludes with historical notes on the emergence of data science and related topics.

An overview of what the point of category theory is, without formality, and an overview of the contents of the book. We will present category theory as “the mathematics of mathematics”, so first we explain what aspects of mathematics we are focusing on. We present mathematics as starting from abstraction, as a way of elucidating analogies between situations, finding connections between them, and unifying them. Category theory is then a rigorous framework for making analogies and finding connections between different parts of mathematics. It focuses on relationships between things, rather than on intrinsic characteristics, and uses those relationships to put objects in context rather than treat them in isolation. Once the framework has been set up, we have, among other things, a way to express more nuanced notions of “sameness”, and a way to characterize things by the role they play in that context. Category theory also works at many different levels, so we can zoom in and out and study details close up, or broad contexts with more of an overview.

This introductory chapter briefly outlines the main theme of this volume, namely, to review the new opportunities and risks of digital healthcare from various disciplinary perspectives. These perspectives include law, public policy, organisational studies, and applied ethics. Based on this interdisciplinary approach, we hope that effective strategies may arise to ensure that benefits of this ongoing revolution are deployed in a responsible and sustainable manner. The second part of the chapter comprises a brief review of the four parts and fourteen substantive chapters that make up this volume.

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.

The rise of artificial intelligence is mainly associated with software-based robotic systems such as mobile robots, unmanned aerial vehicles, and increasingly, semi-autonomous cars. However, the large gap between the algorithmic and physical worlds leaves existing systems still far from the vision of intelligent and human-friendly robots capable of interacting with and manipulating our human-centered world. The emerging discipline of machine intelligence (MI), unifying robotics and artificial intelligence, aims for trustworthy, embodiment-aware artificial intelligence that is conscious both of itself and its surroundings, adapting its systems to the interactive body it is controlling. The integration of AI and robotics with control, perception and machine-learning systems is crucial if these truly autonomous intelligent systems are to become a reality in our daily lives. Following a review of the history of machine intelligence dating back to its origins in the twelfth century, this chapter discusses the current state of robotics and AI, reviews key systems and modern research directions, outlines remaining challenges and envisages a future of man and machine that is yet to be built.