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Summary of Rob Reich, Mehran Sahami, and Jeremy Weinstein's System Error
Buy now to get the main key ideas from Rob Reich, Mehran Sahami, and Jeremy Weinstein's System Error The preoccupation with efficiency and optimization in big tech companies has come at the expense of core human values. System Error (2021) lays forth actions that may be taken to alter the future, rebuild our democracy, and save humanity. Rob Reich, Mehran Sahami, and Jeremy M. Weinstein uncover the source of our present predicament: how big tech's obsessive drive for efficiency promotes prejudice, diminishes anonymity, displaces employees, and contaminates information. With big tech's dominance becoming a societal problem, they provide startling ideas and real answers to help everyone grasp what is occurring, what is at risk, and what can be done to manage technology rather than allowing it to rule humankind.
System Error
Read this if you want to understand how to shape our technological future and reinvigorate democracy along the way. -- Reed Hastings, co-founder and CEO of Netflix __________ A forward-thinking manifesto from three Stanford professors which reveals how big tech's obsession with optimization and efficiency has sacrificed fundamental human values and outlines steps we can take to change course, renew our democracy, and save ourselves. __________ In no more than the blink of an eye, a naïve optimism about technology's liberating potential has given way to a dystopian obsession with biased algorithms, surveillance capitalism, and job-displacing robots. Yet too few of us see any alternative to a...
Text Mining
The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify te
Lazy Learning
This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.
Political Theory of the Digital Age
With the rise of far-reaching technological innovation, from artificial intelligence to Big Data, human life is increasingly unfolding in digital lifeworlds. While such developments have made unprecedented changes to the ways we live, our political practices have failed to evolve at pace with these profound changes. In this path-breaking work, Mathias Risse establishes a foundation for the philosophy of technology, allowing us to investigate how the digital century might alter our most basic political practices and ideas. Risse engages major concepts in political philosophy and extends them to account for problems that arise in digital lifeworlds including AI and democracy, synthetic media and surveillance capitalism and how AI might alter our thinking about the meaning of life. Proactive and profound, Political Theory of the Digital Age offers a systemic way of evaluating the effect of AI, allowing us to anticipate and understand how technological developments impact our political lives – before it's too late.
Machine Learning and Knowledge Discovery for Engineering Systems Health Management
This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.
Natural Language Information Retrieval
The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa tion access has exploded. Emerging applications in computer-assisted infor mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has he...
Data Clustering
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabi...
Designing Machine Learning Systems
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objec...
Computational Intelligent Data Analysis for Sustainable Development
Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present
