This chapter provides a high-level overview of the complete development and deployment process for generative AI (GenAI) language models, such as those that power systems like ChatGPT and Gemini. It begins by clarifying the distinction between a GenAI model (the core algorithm and code) and a GenAI system (the model plus all necessary infrastructure, user interface, and data management). The discussion then demystifies the technology. Large language models (LLMs) operate by calculating the statistical probability of the next word in a sequence – making them sophisticated, yet fundamentally logic-based, “next-token predictor machines” that lack true comprehension or inherent powers of judgment.

This preface chapter transitions from planning to learning. The chapter distinguishes RL from supervised learning: While supervised learning uses fixed i.i.d. datasets, RL involves an agent interacting online with an environment. Three main learning paradigms are identified: situated agent setting, offline learning and simulation-based learning. The exploration–exploitation tradeoff is emphasized as fundamental. Success metrics including sample complexity bounds, regret minimization and convergence guarantees are discussed.

This chapter dissects the intense, seemingly irrational boom in generative AI (GenAI), moving beyond superficial hype to reveal the complex, structural incentives driving billions of dollars into an industry with uncertain returns. It argues that the current boom is propelled not purely by altruistic motives or technological inevitability, but by ten powerful forces. These forces – namely, shareholder primacy, financial imperatives, the pursuit of first-mover advantages, insatiable data-collection needs, self-serving safety justifications, fear of losing relevance, the pursuit of prestige, fear of missing out (FOMO), a belief in “manifest destiny,” and regulatory entrepreneurship – collectively prioritize speed, growth, and market capture over caution, sustainability, and societal benefit. By examining these systemic pressures, the chapter provides the framework for understanding the breakneck pace of AI development and for shaping its ultimate deployment.

This chapter discusses the contentious world of web scraping and its essential, yet problematic, role as the lifeblood of generative AI (GenAI) model training. It highlights the vast scale of data required and the subsequent reliance on the internet’s reservoirs: the public, deep, and dark webs. The central tension explored is the conflict between technological capability and creator consent, revolving around legal and ethical dilemmas.

The chapter scrutinizes the ethical implications of this “finders keepers” approach, the use of torrenting to acquire copyrighted material, and the economic imbalance created when AI models compete with the original content creators. Finally, the chapter examines the complex debate surrounding content opt-out mechanisms and concludes by stressing the impending challenge of diminishing data, which will likely force a greater reliance on synthetic data. Ultimately, the chapter argues that the current approach has created a crisis of legitimacy that requires a reevaluation of the terms under which the internet should be scraped.

This chapter delves into the narrative aspect of emergency medicine, highlighting the importance of storytelling in patient care and the oral board examination process. It discusses the structured nature of the oral examination, which combines elements of an Observed Structured Clinical Encounter (OSCE) and simulation to evaluate candidates’ clinical skills. The chapter provides insights into the virtual platform used for the exam, the unfolding of cases, and the examiner’s role in guiding the narrative. Strategies for approaching cases, organizing information, and interacting with examiners are detailed, emphasizing the need for efficient data collection and clear communication. Practical tips for exam preparation, including technology setup, time management, and professional conduct, are outlined to help candidates navigate the challenging oral board experience. The structured interview component is also explored, focusing on critical thinking and decision-making processes. Differences between the American Board of Emergency Medicine (ABEM) and the American Osteopathic Board of Emergency Medicine (AOBEM) exams are discussed, with a glimpse into the future transition to an in-person certifying exam by ABEM in 2026.

Exploring the use of our methods has highlighted the importance of defining the boundaries of the system being studied and attending to features of underlying systems that afford causal processes. We note that our limited application of our methods cannot confirm that all the implementation actions were necessary, though our examination of L shows how this might be shown by broader study. We note that, while our methods may seem daunting, they’re no more detailed than statistical methods used in quantitative research and have practical advantages in supplying more detailed qualitative evaluations. They may also be used for ex ante appraisal. We stress the importance of understanding mechanisms in practical research and note that actual research involves back-and-forth between evidence and theories of change, even though we detail the former first in our evaluation. We note the value of such evaluations in combatting problems such as, relevantly, costly failed reforms.

Machine-readable humanity is an evocative idea, and it is this idea which Hanley et al. spell out and critically discuss in their contribution. They are interested in exploring the technological as well as the moral side of the meaning of machine-readability. They start by differentiating between various ways to collect (and read) data and to develop classification schemes. They argue that traditional top-down data collection (first the pegs and then the collection according to the pegs) is less efficient than more recent machine readability, which is dynamic, because of the successive advances of data and predictive analytics (“big data”), machine learning, deep learning, and AI. Discussing the advantages as well as the dangers of this new way to read humans, they conclude that we should be especially cautious vis-à-vis the growing field of digital biomarkers since in the end they could not only endanger privacy and entrench biases, but also obliterate our autonomy. Seen in this light, apps (like AdNauseam) that restrict data collection as a form of protest against behavioral profiling also constitute resistance to the inexorable transformation of humanity into a standing reserve: humans on standby, to be immediately at hand for consumption by digital machines.

Chapter 4 considers ethical issues in healthcare communication research through two case studies. The first case study looks at a relatively straightforward situation involving a study of the Pain Concern online forum. Data from the forum was provided by Health Unlocked, a company that runs a large number of online communities related to health. One advantage of using their service was that Health Unlocked took care of relevant legal requirements concerning ethics and only shared data from contributors to the forum who had agreed for their posts to be used for research purposes. The second case study relates to the study of dementia and brings into focus the difficulties of working with multiple datasets and a range of stakeholders. The data collection for this project involved public health communication in terms of news media and external communications from support services, including social media. As such, it presents scenarios that are common to studies of health communication and thereby offers instruction in how to navigate related ethical concerns.

This chapter explores the rich landscape of mixed methods research, examining its origins, foundational principles, and essential steps for implementation. You will gain insights into the philosophical underpinnings that shape this approach and learn about the different stages involved in conducting mixed methods research, from planning and data collection to analysis and integration of findings. The chapter discusses various mixed methods research designs, outlining their unique characteristics and applications so that you can discern when and how to utilize each effectively. Additionally, it tackles the integration of data from diverse sources and explores strategies for overcoming the challenges that arise in this process. A critical focus will be on the issue of validity in mixed methods research, where we will highlight essential strategies for ensuring the reliability and credibility of your findings. By the end of this chapter, you will be prepared to address the complexities of mixed methods research and use this versatile approach in your applied linguistics studies.

How do you collect data? This chapter will outline tried-and-true data collection techniques for conducting fieldwork in the speech community.