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Matthew Hoffman: How a Kind Word Can Make the World Better
Matthew Hoffman is a Chicago-based artist and designer whose public works have been exhibited internationally. His ideas have been included in publications such as Good and the New York Times Magazine, and his You Are Beautiful movement has evolved from less than 100 stickers in 2002 into a collection of murals, public installations, and exhibitions involving scores of other artists. In this audio-only course, Justin Ahrens visits Matthew in his studio to learn more about the man behind the message. Matthew discusses the genesis of his career, how he grew You Are Beautiful (and grappled with the ensuing success and attention), and how he built the infrastructure needed to turn his passion project into a growing business.
Hattie's Advocate
This book is a witty and intriguing look into the world of foster care through the eyes of a foster parent. It breaks down the expectations and regulations that parents in foster care are faced with, and it touches on the problems in government policy that affect foster children. It does all this while thoroughly entertaining the reader. It is an indispensable resource for anyone considering adoption or foster care and a great read for just about anyone else.
Automated Machine Learning and Meta-Learning for Multimedia
This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.
Visual Rhetoric and the Eloquence of Design
The essays in VISUAL RHETORIC AND THE ELOQUENCE OF DESIGN foreground the rhetorical functions of design artifacts. Rhetoric, normally understood as verbal or visual messages that have a tactical persuasive objective—a speech that wants to convince us to vote for someone, or an ad that tries to persuade us to buy a particular product—becomes in Visual Rhetoric and the Eloquence of Design the persuasive use of a broad set of meta-beliefs. Designed objects are particularly effective at this second level of persuasion because they offer audiences communicative data that reflect, and also orchestrate, a potentially broad array of cultural concerns. Persuasion entails both the aesthetic form and material composition of any object.
Alternative Assets and Cryptocurrencies
Alternative assets such as fine art, wine, or diamonds have become popular investment vehicles in the aftermath of the global financial crisis. Correlation with classical financial markets is typically low, such that diversification benefits arise for portfolio allocation and risk management. Cryptocurrencies share many alternative asset features, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This collection of papers addresses alternative assets and cryptocurrencies from economic, financial, statistical, and technical points of view. It gives an overview of their current state and explores their properties and prospects using innovative approaches and methodologies.
Distributional Reinforcement Learning
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key...
Ultra-Reliable and Low-Latency Communications (URLLC) Theory and Practice
Ultra-Reliable and Low-Latency Communications (URLLC) Theory and Practice Comprehensive resource presenting important recent advances in wireless communications for URLLC services, including device-to-device communication, multi-connectivity, and more Ultra-Reliable and Low-Latency Communications (URLLC) Theory and Practice discusses the typical scenarios, possible solutions, and state-of-the-art techniques that enable URLLC in different perspectives from the physical layer to higher-level approaches, aiming to tackle URLLC’s challenges with both theoretical and practical approaches, which bridges the lacuna between theory and practice. With long-term contributions to the development of fu...
Methods and Applications of Autonomous Experimentation
Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field.
Lifelong Machine Learning, Second Edition
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past he...
