LMU Library Staff Picks

AI's impact on human societies is and will be drastic in so many ways. AI is being adopted and implemented around the world, and government and universities are investing in AI studies, research, and development. However, very little research exists about the impact of AI on our lives. This book will address this gap; it will gather reflections from around the world to assess the impact of AI on different aspects of society as well as propose ways in which we can address this impact and the research agendas needed.

A smart, incisive look at the technologies sold as artificial intelligence, the drawbacks and pitfalls of technology sold under this banner, and why it's crucial to recognize the many ways in which AI hype covers for a small set of power-hungry actors at work and in the world. Is artificial intelligence going to take over the world? Have big tech scientists created an artificial lifeform that can think on its own? Is it going to put authors, artists, and others out of business? Are we about to enter an age where computers are better than humans at everything? The answer to these questions, linguist Emily M. Bender and sociologist Alex Hanna make clear, is "no," "they wish," "LOL," and "definitely not." This kind of thinking is a symptom of a phenomenon known as "AI hype." Hype looks and smells fishy: It twists words and helps the rich get richer by justifying data theft, motivating surveillance capitalism, and devaluing human creativity in order to replace meaningful work with jobs that treat people like machines. In The AI Con, Bender and Hanna offer a sharp, witty, and wide-ranging take-down of AI hype across its many forms. Bender and Hanna show you how to spot AI hype, how to deconstruct it, and how to expose the power grabs it aims to hide. Armed with these tools, you will be prepared to push back against AI hype at work, as a consumer in the marketplace, as a skeptical newsreader, and as a citizen holding policymakers to account.

For many, technology offers hope for the future--that promise of shared human flourishing and liberation that always seems to elude our species. Artificial intelligence (AI) technologies spark this hope in a particular way. They promise a future in which human limits and frailties are finally overcome--not by us, but by our machines. Yet rather than open new futures, today's powerful AI technologies reproduce the past. Forged from oceans of our data into immensely powerful but flawed mirrors, they reflect the same errors, biases, and failures of wisdom that we strive to escape. Our new digital mirrors point backward. They show only where the data say that we have already been, never where we might venture together for the first time. To meet today's grave challenges to our species and our planet, we will need something new from AI, and from ourselves. Shannon Vallor makes a wide-ranging, prophetic, and philosophical case for what AI could be: a way to reclaim our human potential for moral and intellectual growth, rather than lose ourselves in mirrors of the past. Rejecting prophecies of doom, she encourages us to pursue technology that helps us recover our sense of the possible, and with it the confidence and courage to repair a broken world. Vallor calls us to rethink what AI is and can be, and what we want to be with it.

This volume brings together eminent scholars from various parts of the world, representing different fields of knowledge in order to explore the social, cultural, political and economic effects of the development of new technologies. On the one hand, the book contextualises the discussion of algorithms and artificial intelligence (AI) within the broader framework of the digital revolution, on the other it also examines individual experiences and practices. Moreover, in light of the speed at which algorithms and AI are being incorporated into various aspects of life, contributors also question the ethical implications of their development. The widespread development of AI and algorithmic solutions is one of the most important contemporary phenomena. It has an overwhelming impact on the social and cultural life of the 21st century. In this context, one can point to both exciting examples of the application of algorithms and AI in business and popular culture, as well as the challenges of widening social inequality or the expanding scope of surveillance.

Artificial Intelligence, Volume 49 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics. Chapters in this new release include AI Teacher-Student based Adaptive Structural Deep Learning Model and Its Estimating Uncertainty of Image Data, Machine-derived Intelligence: Computations Beyond the Null Hypothesis, Object oriented basis of artificial intelligence methodologies I in Judicial Systems in India, Artificial Intelligence in Systems Biology, Machine-Learning in Geometry and Physics, Innovation and Machine Learning: Crowdsourcing Open-Source Natural Language Processing (NLP) Algorithms to Advance Public Health Surveillance, and more. Other chapters cover Learning and identity testing of Markov chains, Data privacy for machine learning and statistics, and The interface between AI and Mathematics.

As the world increasingly recognizes the importance of sustainability, businesses and investors are looking for ways to integrate sustainable practices into their operations and investment decisions. At the same time, advancements in artificial intelligence (AI) and technology are transforming the finance industry and are enabling more data-driven decision-making. The intersection of these fields presents a significant opportunity to accelerate progress towards a more sustainable future, while also improving financial performance. This book explores the crucial role of AI in sustainability and finance, examining how financial technologies and machine learning are shaping the approach of finance professionals towards environmental, social, and governance (ESG) issues. It provides a comprehensive and integrated perspective on how these areas are becoming increasingly intertwined and examines the ethical and social implications of AI in finance and its potential to unlock new opportunities for sustainability.

Artificial Intelligence (AI) represents a key technology for social change in the 21st century. Numerous technological applications are now in use that are based on machine learning and the associated possibilities for data collection, use and exploitation. By making large amounts of data manageable and hidden patterns and connections visible, AI makes many things faster, easier and more efficient - be it in everyday life, at work or in organizations. However, the question remains open as to what profound and sometimes latent consequences for humans as social beings and social coexistence are associated with the use and development of AI. How is the relationship between people and technology changing through AI and how should this change be assessed? What opportunities and risks do the use and development of AI open up for people and society? What are the limits of change and what design options are available? And last but not least: What and who determines the development paths that AI takes - with what consequences and for whom?

A concise but informative overview of AI ethics and policy. Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring home-owners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, voters in liberal democracies? This book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues including transparency, bias, liability, privacy, and regulation.

From a brilliant longtime AI insider with intimate access to the world of Sam Altman's OpenAI from the beginning, an eye-opening account of arguably the most fateful tech arms race in history, reshaping the planet in real time, from the cockpit of the company that is driving the frenzy. When AI expert and investigative journalist Karen Hao first began covering OpenAI in 2019, she thought they were the good guys. Founded as a nonprofit with safety enshrined as its core mission, the organization was meant, its leader Sam Altman told us, to act as a check against more purely mercantile, and potentially dangerous, forces. What could go wrong? Over time, Hao began to wrestle ever more deeply with that question. Increasingly, she realized that the core truth of this massively disruptive sector is that its vision of success requires an almost unprecedented amount of resources: the "compute" power of high-end chips and the processing capacity to create massive large language models, the sheer volume of data that needs to be amassed at scale, the humans "cleaning up" that data for sweatshop wages throughout the Global South, and a truly alarming spike in the usage of energy and water underlying it all. The truth is that we have entered a new and ominous age of empire: only a small handful of globally scaled companies can even enter the field of play. At the head of the pack with its ChatGPT breakthrough, how would OpenAI resist such temptations? Spoiler alert: it didn't.

This comprehensive volume delves deep into the diverse applications and implications of generative AI across accounting, finance, economics, business, and management, providing readers with a holistic understanding of this rapidly evolving landscape. Generative Artificial Intelligence in Finance: Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes provides a comprehensive guide to ethically harnessing generative AI systems to reshape financial management.

This book explores the metamorphosis of fundamental social interactions and communal experiences, fueled by technologies such as artificial intelligence, immersive online environments, augmented reality, blockchain, crypto and FinTech. It examines the competitors, regulators and governments who are locked in a struggle to control the economic and social future shaped by these technologies.

Generative AI tools like ChatGPT are amazing - but how can you get the most out of them in your daily work? This book introduces cutting-edge AI tools and the practical techniques you need to use them safely and effectively. Introduction to Generative AI gives you the hows-and-whys of generative AI in accessible language. In this easy-to-read introduction, you'll learn: How large language models (LLMs) work How to integrate generative AI into your personal and professional work Balancing innovation and responsibility The social, legal, and policy landscape around generative AI Societal impacts of generative AI Where AI is going.

Looking at the emergence of generative AI, Language Machines presents a new theory of meaning in language and computation, arguing that humanistic scholarship misconstrues how large language models (LLMs) function. Seeing LLMs as a convergence of computation and language, Leif Weatherby contends that AI does not simulate cognition, as widely believed, but rather creates culture.

Artificial Intelligence (AI) is everywhere, yet it causes damage to society in ways that can't be fixed. Instead of helping to address our current crises, AI causes divisions that limit people's life chances, and even suggests fascistic solutions to social problems. This book provides an analysis of AI's deep learning technology and its political effects and traces the ways that it resonates with contemporary political and social currents, from global austerity to the rise of the far right. Dan McQuillan calls for us to resist AI as we know it and restructure it by prioritising the common good over algorithmic optimisation.

The essays in this volume illustrate how AI can be used as a productive metaphor and intellectual tool for the humanities. From formalizing concepts like Stimmung and vibe, to challenging traditional distinctions between writing and thought or between history and data, Thinking with AI shows how AI can be not just be an object of study but a conceptual catalyst that ignites unexpected connections to long-standing humanistic concerns.