This chapter educates the reader on the main ideas that have enabled various advancements in Artificial Intelligence (AI) and Machine Learning (ML). Using various examples, and taking the reader on a journey through history, it showcases how the main ideas developed by the pioneers of AI and ML are being used in our modern era to make the world a better place. It communicates that our lives are surrounded by algorithms that work based on a few main ideas. It also discusses recent advancements in Generative AI, including the main ideas that led to the creation of Large Language Models (LLMs) such as Chat GPT. The chapter also discusses various societal considerations in AI and ML and ends with various technological advancements that could further improve our abilities in using the main ideas.

This chapter discusses the broader role and impact of analytics science in improving various aspects of society. It introduces what the book is about, and what the reader should expect to learn from reading this book. It also discusses the analytics revolution in the private and public sector, and introduces a key element of the book — insight-driven problem solving — by highlighting its vital role in addressing various societal problems.

In this chapter, the reader learns about the main ideas developed by analytics scientists in problem solving that requires decision-making over time. The reader realizes that various decision-making problems, including those made in everyday life such as which parking spot to take or which job offer to accept, can be addressed using these main ideas. The chapter also illustrates how the same ideas have been used to improve the world around us by citing various examples, including assisting policymakers to gain insights into the impact of different social mobility policies or to find better lockdown policies during a pandemic such as COVID-19.

This chapter is focused on game theory and mechanism design, presenting them as an important branch of analytics science that has impacted our world. Like all other chapters, it starts by presenting the big picture ideas, and showcasing various real-world examples in which those ideas have been impactful. It educates the reader though various familiar examples such as the simple decisions involved in cutting a cake and more critical decision-making scenarios such as what happened during the Battle of the Bismarck Sea or finding ways to reduce racial segregation in the society, and from policies that revolutionized life-saving ideas for those who are in dire need of transplantation to governments’ complex efforts in improving voting mechanisms. The chapter provides engaging stories showcasing how the main ideas in game theory and mechanism design have been impactful in a myriad of ways.

This chapter is devoted to understanding how the main ideas in graph theory and combinatorics optimization can assist insight-driven problem solving, and thereby, create public impact. The reader sees how such ideas have laid the foundation for apps such as Google Maps and how they are being used to improve our understanding of social networks, design transportation networks, create efficiency schedules for sports events, enhance cryptosystems, and improve the efficiency of supply chains. The reader also learns how analytics scientists have been able to learn from the amazing ability of nature in problem solving (swarm intelligence) and use this to develop effective insight-driven problem solving approaches that can yield powerful insights in addressing complex societal problems.

This chapter starts by communicating how various aspects of our lives involve interacting with queues. It then provides a brief history of the main inception of queueing theory and its main governing princples, and discusses how it has impacted various aspects of our lives. It educates the reader about the main ideas and principles in queueing theory and also elaborates on the psychological aspects of waiting in queues. Showcasing various examples of how the main ideas in queueing theory have enabled important improvements, ranging from what happened during Queen Elizabeth II’s memorial, to the creation of the internet and modern telephones, to our experiences in airports or on roads, the chapter presents queueing theory as a potent branch of analytics science that has enabled scholars to make the world a better place. The chapter also discusses the vital interplays between queueing theory, public policy, and technology.

This chapter is devoted to data analysis and its critical role in analytics science. The reader is introduced to the science of inference from observations and experiments and learns about the main ideas in data analysis that have been influential in addressing societal problems. Real-world examples are used throughout to convey the main ideas and illustrate why data analyses performed without sufficient care can yield wrong insights. Successful examples of insight-driven problem solving approaches in data analysis are contrasted with those that can yield wrong insights, and the reader is taken on an engaging yet educational journey that depicts how and why successful insight-driven problem solving approaches using data can have significant public impact.

This chapter introduces the reader to the big picture of what analytics science is. What is analytics science? What types does it have, and what is its scope? How can analytics science be used to improve various tasks that society needs to carry out? Is analytics science all about using data? Or can it work without data? What is the role of data versus models? How can one develop and rely on a model to answer essential questions when the model can be wrong due to its assumptions? What is ambiguity in analytics science? Is that different from risk? And how do analytics scientists address ambiguity? What is the role of simulation in analytics science? These are some of the questions that the chapter addresses. Finally, the chapter discusses the notion of "centaurs" and how a successful use of analytics science often requires combining human intuition with the power of strong analytical models.

This chapter presents mathematical programming as the science of one-shot decisions. It clarifies important ways analytics scientists implement problem solving by benefiting from tools and ideas in mathematical programming both in scenarios where the world behaves linearly and where it does not. It also introduces integer programming and inverse optimization, showcasing how the main ideas and insights obtained from mathematical programming have been applied to various impactful problems ranging from designing effective diets to allowing the military to improve the efficiency of its operations to make bike sharing systems more accessible.