This Library Guide presents a selection of additions to the Library collection for Games, Media and Data Science & AI. This includes some of the latest acquisitions of (e-)books, video games, board games, links to the latest editions of key journals, and the latest developments in Library resources and services.
For additional information and suggestions for improvement please contact our Games, Media and Data Science & AI information specialist Linda Hurkmans
BUas now has access to ACM Digital Library and O'Reilly for Higher Education.
The ACM Digital Library is a research, discovery and networking platform containing full-text access to all ACM publications, including journals, conference proceedings, technical magazines, newsletters and books.
The O'Reilly digital platform includes books, videos, expert playlists, and audio books. Users also get access to exclusive O’Reilly content as well as resources from approximately 200 of the world’s leading publishers in business and technology. Topics range from programming to cybersecurity, to project management and communications, to design thinking, interior design and soft skills.
The content includes certification test prep materials, on-demand training videos, and much more.
Want to know more? BUas Library has a LibGuide dedicated to this database: https://buas.libguides.com/oreilly.
The AI Con
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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. Together, Bender and Hanna expose AI hype for what it is: a mask for Big Tech's drive for profit, with little concern for who it affects.
AI Strategy
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Is your business truly ready for the AI revolution? Discover how to unlock the full potential of artificial intelligence and future-proof your organization with a winning AI strategy. AI is transforming every industry - is your business keeping up? In this essential guide, bestselling author and futurist Bernard Marr provides a comprehensive playbook for leaders looking to harness the power of AI. From understanding AI's transformative impact to developing a clear, actionable strategy, this book equips leaders with the tools they need to drive innovation, manage risks, and stay ahead in an AI-driven world. AI Strategy is the definitive guide for leaders ready to unlock AI's potential at scale. Covering every aspect of AI adoption - from ethical considerations and data management to employee upskilling and tech infrastructure - this book delivers real-world examples across multiple organizations and industries, from energy, healthcare and education to marketing and HR. Whether you're just starting or scaling fast, this must-read guide will help you confidently build and execute a winning AI strategy.
Applications of Generative AI
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This book provides a comprehensive introduction to Generative AI in terms of basic concepts, core technologies, technical architecture, and application scenarios. Readers gain a deeper understanding of the emerging discipline of Generative AI. This book covers the latest cutting-edge application technologies of Generative AI in various fields. It provides relevant practitioners with ideas to solve problems and deepen their understanding of Generative AI. At the same time, it guides and helps Generative AI and related industries to deepen their understanding of the industry and enhance professional knowledge and skills. Starting from reality, this book lists many cases and analyzes theories in a popular image. The book is useful for AI researchers and specifically for those working with the applications at hand (primarily medical imaging and construction/twinning industry). It covers a variety of cutting-edge technologies in Generative AI, which provides researchers with new research ideas.
Artificial Intelligence of Things (AIoT)
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Artificial Intelligence of Things (AIoT): Current and Future Trends brings together researchers and developers from a wide range of domains to share ideas on how to implement technical advances, create application areas for intelligent systems, and how to develop new services and smart devices connected to the Internet. Section One covers AIoT in Everything, providing a wide range of applications for AIoT methods and technologies. Section Two gives readers comprehensive guidance on AIoT in Societal Research and Development, with practical case studies of how AIoT is impacting cultures around the world. Section Three covers the impact of AIoT in educational settings. The book also covers new capabilities such as pervasive sensing, multimedia sensing, machine learning, deep learning, and computing power. These new areas come with various requirements in terms of reliability, quality of service, and energy efficiency.
Building Machine Learning Systems with a Feature Store
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Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems. Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML--the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems. With this book, you'll be able to: Make the leap from training ML models to building ML systems Develop an ML system as modular feature, training, and inference pipelines Design, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generation Learn the problems a feature store for ML solves when building ML systems Understand the principles of MLOps for developing and safely updating ML systems Jim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden.
The EU Artificial Intelligence ACT
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AI in combination with other innovative technologies promises to bring unprecedented opportunities to all aspects of life. These technologies, however, hold great dangers, especially for the manipulation of the human mind, which have given rise to serious ethical concerns.
Handbook of Artificial Intelligence in Education
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Gathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day. The Handbook evaluates the use of AI techniques such as modelling in closed and open domains, machine learning, analytics, language understanding and production to create systems aimed at helping learners, teachers, and educational administrators. Chapters examine theories of affect, metacognition and pedagogy applied in AIED systems; foundational aspects of AIED architecture, design, authoring and evaluation; and collaborative learning, the use of games and psychomotor learning. It concludes with a critical discussion of the wider context of Artificial Intelligence in Education, examining its commercialisation, social and political role, and the ethics of its systems, as well as reviewing the possible challenges and opportunities for AIED in the next 20 years. Providing a broad yet detailed account of the current field of Artificial Intelligence in Education, researchers and advanced students of education technology, innovation policy, and university management will benefit from this thought-provoking Handbook. Chapters will also be useful to support undergraduate courses in AI, computer science, and education.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
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Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
An Introduction to Statistical Learning
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
The Opposite of Cheating
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In these days of an ever-expanding internet, generative AI, and term paper mills, students may find it too easy and tempting to cheat, and teachers may think they can't keep up. What's needed, and what Tricia Bertram Gallant and David A. Rettinger offer in this timely book, is a new approach--one that works with the realities of the twenty-first century, not just to protect academic integrity but also to maximize opportunities for students to learn. The Opposite of Cheating presents a positive, forward-looking, research-backed vision for what classroom integrity can look like in the GenAI era, both in cyberspace and on campus. Accordingly, the book outlines workable measures teachers can use to better understand why students cheat and to prevent cheating while aiming to enhance learning and integrity. Bertram Gallant and Rettinger provide practical suggestions to help faculty revise the conversation around integrity, refocus classes and students on learning, reconsider the structure and goals of assessment, and generally reframe our response to cheating. At the core of this strategy is a call for teachers, academic staff, institutional leaders, and administrators to rethink how we "show up" for students, and to reinforce and fully support quality teaching, learning, and assessment. With its evidentiary basis and its useful tips for instructors across disciplines, levels of experience, and modes of instruction, this book offers a much-needed chance to pause, rethink our purpose, and refocus on what matters--creating classes that center human interactions that foster the personal and professional growth of our students.
Oxford Handbook of Ethics of AI
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This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of placing current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and "intelligence", presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."
Unmasking Deepfakes
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Unmasking Deepfakes: Exploring the World of Synthetic Realities In 2024, the digital world stands on the precipice of a new era where reality blurs with fiction. Dive into the comprehensive exploration of deepfakes: from their historical origins to the latest advancements. Grasp the technological might that powers them, the threats they pose, and the countermeasures available. Witness how deepfakes shape our media, politics, and society, and glimpse the possibilities they hold for the future. This crucial read is not only an exploration but a guide to navigate a world where seeing is no longer believing. Equip yourself with knowledge and be prepared for the evolving digital landscape of tomorrow.
For a complete overview of content databases BUas has access to, see: A-Z Databases
Databases with content relevant for the AGM domains:
The O'Reilly digital platform includes books, videos, expert playlists, and audio books. Users also get access to exclusive O’Reilly content as well as resources from approximately 200 of the world’s leading publishers in business and technology. Topics range from programming to cybersecurity, to project management and communications, to design thinking, interior design and soft skills.
The content includes certification test prep materials, on-demand training videos, and much more.
Want to know more? BUas Library has a LibGuide dedicated to this database: https://buas.libguides.com/oreilly.
The ACM Digital Library is a research, discovery and networking platform containing full-text access to all ACM publications, including journals, conference proceedings, technical magazines, newsletters and books.
ScienceDirect enables you to search Elseviers publisher database. The chief topic areas are natural sciences, architecture, biological sciences, medical science, social sciences and humanities. This database comprises some 2,000 scientific journals. Partially access to Elseviers journals & articles. Archive available as from 1995.
BUas-students/employees have access to full-text articles in the Freedom collection of Science Direct.
For more ejournals on artificial intelligence, check out the library's list of ejournals: