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This gentle introduction to the most important techniques in natural language processing uses a unified mathematical and algorithmic framework and gradually increases in complexity. Topics covered range from n-gram language models to large language models (LLMs), from perceptron to deep learning, from text classification to structured prediction (e.g., sequence labelling, segmentation, and parsing) and generation, and from discrete representation to neural representation of linguistics structures. This book provides a comprehensive overview of NLP, making it ideal for upper undergraduate and graduate students in computer science and a valuable reference for researchers and engineers. Exercises of varying difficulty are provided as well as teaching slides and tutorial videos. The new edition features three new chapters on pre-trained language models and large language models as well as a new preliminary chapter overviewing data and model as a framework for NLP methods.
Spanning the full AI Ph.D. journey, this practical guide offers clear, realistic, and action oriented advice for success. Designed as an end-to-end guide, the book leads readers from finding a research problem to experimentation, writing, conferences, and the final defense. Readers learn how to partner effectively with advisors, run productive meetings, and navigate review and rejection with confidence. The book provides guidance on responsible AI research and using LLMs effectively while safeguarding scientific integrity. The final stages of the PhD receive explicit focus, with advice on shaping publications into a coherent dissertation, preparing for the defense, and responding to examiners. This guide extends beyond graduation, exploring career paths in academia, industry, and the public sector while emphasizing transferable skills and strategic decisions. Ideal for Ph.D. students in AI and machine learning, as well as those considering starting a PhD, this book provides actionable advice that clarifies next steps and accelerates progress.
Artificial intelligence is reshaping decisions that affect people, institutions, and societies. Understanding how to design, deploy, and govern AI systems that can be trusted is now essential in many disciplines. This book offers a clear, concise introduction to trustworthy AI, treating AI not just as a technical artifact but as a socio-technical system embedded in human contexts. Developed from an internationally applicable educational framework, the book is designed for teaching and learning in computer science, data science, law, policy, business, and related fields. It equips students and professionals with the concepts and judgment needed to engage critically and responsibly with AI in practice. Combining ethics, governance, and practical insight, the book explains key concepts including transparency, fairness, accountability, human oversight, and stakeholder participation. An interdisciplinary approach makes the material accessible to both technical and non-technical audiences, with realistic scenarios and reflection questions so readers connect principles to real-world AI applications.
As artificial intelligence chatbots offer increasingly sophisticated emotional support, society faces a profound question: can a machine truly empathize? Empathy and Artificial Intelligence provides the first comprehensive roadmap for this pivotal moment. Moving beyond simple binaries of 'hype' or 'doom,' this interdisciplinary volume unites leading psychologists, philosophers, and engineers to explore the tangled web of synthetic care. Key chapters investigate the 'AI Advantage' – where machines often outperform humans in perceived empathy – alongside the 'AI Penalty,' where discovering the artifice corrodes trust. The text navigates the distinct landscapes of text-based LLMs and embodied robots, addressing urgent ethical dilemmas and exploring whether reliance on AI risks the atrophy of our moral capacities or enables synthetic agents to scaffold stronger human relationships. Essential for researchers, students, and curious observers, this book investigates whether outsourcing our emotional labor saves us time, or costs us our humanity.
How can we build and govern trustworthy AI? Operationalizing Responsible AI brings together leading scholars and practitioners to address this urgent question. Each chapter explores a key dimension of responsibility - fairness, explainability, psychological safety, accountability, consent, transparency, auditability, and contextualization – defining what it means, why it matters, and how it can be achieved in practice. Through interdisciplinary perspectives and real-world examples, the book bridges ethical principles, legal frameworks such as the EU AI Act, and technical approaches including explainable AI and audit methodologies. Written for researchers, policymakers, and professionals, the book offers both conceptual clarity and practical guidance for advancing Responsible AI that is fair, transparent, and aligned with human values.
Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.
The Introduction explains important concepts and what they mean in this book. It also outlines the project scope, which covers both written and spoken uses of machine translation to fulfil communication and information access purposes in one of the sectors selected for analysis. Following a brief historical account of how social conceptions of machine translation have changed, the Introduction addresses a recent shift in translation research towards multilingual communication practices that take place outside education settings or the language services industry. Given how fast language technologies are evolving, it will not take long for the tools and types of human–computer interaction that appear in the book to change quite significantly. The Introduction addresses implications of this dynamic landscape for this book specifically and for translation and multilingual communication research more broadly.
This chapter is about trust. It is the only chapter in the book that considers the opinions of professionals who had not used machine translation at work. Most of these professionals were prepared to use machine translation if needed. Some were enthusiastic about it. Others had reservations which they would want to see addressed before deciding to use a machine translation tool. These reservations included: (1) concerns about accuracy; (2) concerns about privacy and confidentiality; (3) perceptions of social and professional norms and whether their use of machine translation would be accepted by others; and (4) concerns about how machine translation may lack ‘the human touch’ in sensitive interactions. The chapter examines human communication in relation to the concept of empathy and AI’s ability to mimic it. It ends with a discussion of workplace training by exploring what machine translation users would like to see covered in initiatives aimed at enhancing their trust judgment abilities.
The sections that follow describe the project’s methodology. All stages of data collection were approved by the Faculty of Arts ethics committee at the University of Bristol.
The questionnaire responses discussed in Chapters 3, 4 and 5 were collected in 2024 in a survey of over 2,500 professionals based in the UK. I included a summary of the survey methodology in a preliminary report published by the Chartered Institute of Linguists. Some of the information provided below first appeared in that report.
I distributed the survey via Prolific (www.prolific.com), a database of pre-registered individuals who can be invited to take part in research. Only studies that offer remuneration to participants can be distributed via this service. Remuneration involves the risk of attracting participants who are only interested in the payment and who are therefore more likely to submit satisficing responses. On the other hand, remuneration can also be considered an ethical principle. It is a way of compensating participants for their time and of recognising the importance of their input.
Chapter 6 is a case study of multilingual communication in social work. It draws on in-depth interviews carried out with eighteen UK social workers. The interviews included vivid accounts of challenging scenarios. In one of them, a child protection team manager had to sit with a family for two hours while trying to contact a Vietnamese interpreter. A different social worker had concerns that human interpreters could fail to convey explicit but crucial details relating to rape and sexual abuse. Machine translation was used in all such cases. Its balance of risks and benefits was sometimes clear. More often, it was highly complex. Two problems raised by the social workers are foregrounded in the chapter. The first is the issue of languages for which both human and technological resources are less abundant. The second concerns additional needs that involve more than a barrier between verbal languages. The chapter draws attention to the practical challenges of navigating cultural and linguistic differences in the provision of social services. It echoes the social workers’ requests for more guidance and support.
What should a nurse do when non-speakers of the local language come to the ward seeking information about a loved one? What should a receptionist do when they need to book an appointment and a language barrier takes them by surprise? How can an emergency call handler let a caller know that a human interpreter is being contacted? Chapter 3 examines circumstances in which the risks of multilingual AI are ostensibly low. It proposes a distinction between ancillary and core communication but argues that communicative settings are fluid. What starts as ancillary communication can easily turn into core care, so risk is not associated with specific roles or with levels of professional seniority. The chapter argues that, in the sectors under analysis, communication is rarely risk-free. Even where machine translation may not directly lead to harm or loss of life, it may be a feature of complex communicative environments which pose significant systemic risks.
Chapter 2 has three main sections. First it draws on the philosophy of technology literature, more specifically post-phenomenology, to interrogate the meaning of human–technology relations. Can visitors to a website be considered users of machine translation even if they are unaware that a machine translation tool is in use? What is the meaning of ‘use’ exactly? When a police officer uses machine translation to speak to a driver, what type of relationship does the driver have with the machine translation tool? These are some of the questions initially addressed in this chapter. The chapter then examines technologies’ influencing properties. Convenience is persuasive and machine translation tools are designed to be convenient. They reflect specific social and economic values which research on their use needs to consider. Lastly, the chapter discusses the complex decision-making that uses of machine translation call for in the sectors under analysis. A case is made for the notion of virtue as an apt framework for engaging with the dilemmas posed by risky but potentially beneficial uses of machine translation.
Chapter 1 lays foundations for the study of AI-mediated multilingual communication. It proposes a typology of machine translation use based on whether communication takes place at a distance or in a shared physical space, whether the use of machine translation is overt or covert and whether it happens in real time or with delays between sending and receiving messages. The chapter examines how the pursuit of cost efficiencies is a recurrent and sometimes problematic feature of organisational deployments of machine translation tools. It reviews important incidents and draws on case law and official documents to discuss uses of machine translation by immigration officers and the police. The chapter concludes by examining the concept of AI literacy, a type of meta-literacy associated with broader competencies such as being able to evaluate the quality of information and to use it critically.