Download or read book Artificial Intelligence Reports from the University of Illinois written by Comtex and published by John Wiley & Sons. This book was released on 1985-03-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Intelligence Technical Report UIUC BI AI DSS written by and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Intelligence Technical Report UIUC BI AI written by and published by . This book was released on 1993 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Social Sensing written by Dong Wang and published by Morgan Kaufmann. This book was released on 2015-04-17 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book
Download or read book Artificial Intelligence Technical Report UIUC BI AI RCV written by and published by . This book was released on 1992 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Final Report on Studies in Artificial Intelligence Computers written by Heinz M. Von Foerster and published by . This book was released on 1963 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Intelligence Technical Report UIUC BI AI written by and published by . This book was released on 1993 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Intelligence Technical Report UIUC BI AI RCV written by and published by . This book was released on 1992 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Intelligence Technical Report UIUC BI AI DSS written by and published by . This book was released on 1992 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning in Industry written by Shubhabrata Datta and published by Springer Nature. This book was released on 2021-07-24 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial problems. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems.
Download or read book Why Literary Periods Mattered written by Ted Underwood and published by Stanford University Press. This book was released on 2013-07-24 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the mid-nineteenth century, the study of English literature began to be divided into courses that surveyed discrete "periods." Since that time, scholars' definitions of literature and their rationales for teaching it have changed radically. But the periodized structure of the curriculum has remained oddly unshaken, as if the exercise of contrasting one literary period with another has an importance that transcends the content of any individual course. Why Literary Periods Mattered explains how historical contrast became central to literary study, and why it remained institutionally central in spite of critical controversy about literature itself. Organizing literary history around contrast rather than causal continuity helped literature departments separate themselves from departments of history. But critics' long reliance on a rhetoric of contrasted movements and fateful turns has produced important blind spots in the discipline. In the twenty-first century, Underwood argues, literary study may need digital technology in particular to develop new methods of reasoning about gradual, continuous change.
Download or read book Deep Learning Applications Volume 2 written by M. Arif Wani and published by Springer. This book was released on 2020-12-14 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
Download or read book Social Media Analytics for User Behavior Modeling written by Arun Reddy Nelakurthi and published by CRC Press. This book was released on 2020-01-21 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.
Download or read book Regulating Artificial Intelligence written by Thomas Wischmeyer and published by Springer Nature. This book was released on 2019-11-29 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book assesses the normative and practical challenges for artificial intelligence (AI) regulation, offers comprehensive information on the laws that currently shape or restrict the design or use of AI, and develops policy recommendations for those areas in which regulation is most urgently needed. By gathering contributions from scholars who are experts in their respective fields of legal research, it demonstrates that AI regulation is not a specialized sub-discipline, but affects the entire legal system and thus concerns all lawyers. Machine learning-based technology, which lies at the heart of what is commonly referred to as AI, is increasingly being employed to make policy and business decisions with broad social impacts, and therefore runs the risk of causing wide-scale damage. At the same time, AI technology is becoming more and more complex and difficult to understand, making it harder to determine whether or not it is being used in accordance with the law. In light of this situation, even tech enthusiasts are calling for stricter regulation of AI. Legislators, too, are stepping in and have begun to pass AI laws, including the prohibition of automated decision-making systems in Article 22 of the General Data Protection Regulation, the New York City AI transparency bill, and the 2017 amendments to the German Cartel Act and German Administrative Procedure Act. While the belief that something needs to be done is widely shared, there is far less clarity about what exactly can or should be done, or what effective regulation might look like. The book is divided into two major parts, the first of which focuses on features common to most AI systems, and explores how they relate to the legal framework for data-driven technologies, which already exists in the form of (national and supra-national) constitutional law, EU data protection and competition law, and anti-discrimination law. In the second part, the book examines in detail a number of relevant sectors in which AI is increasingly shaping decision-making processes, ranging from the notorious social media and the legal, financial and healthcare industries, to fields like law enforcement and tax law, in which we can observe how regulation by AI is becoming a reality.
Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2024-03-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Download or read book Exploring Artificial Intelligence written by Howard E. Shrobe and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploring Artificial Intelligence: Survey Talks from the National Conference on Artificial Intelligence provides information pertinent to the distinct subareas of artificial intelligence research. This book discusses developments in machine learning techniques. Organized into six parts encompassing 16 chapters, this book begins with an overview of intelligent tutoring systems, which describes how to guide a student to learn new concepts. This text then links closely with one of the concerns of intelligent tutoring systems, namely how to interact through the utilization of natural language. Other chapters consider the various aspects of natural language understanding and survey the huge body of work that tries to characterize heuristic search programs. This book discusses as well how computer programs can create plans to satisfy goals. The final chapter deals with computational facilities that support. This book is a valuable resource for cognitive scientists, psychologists, domain experts, computer scientists, instructional designers, expert teachers, and research workers.
Download or read book Machine Learning written by Yves Kodratoff and published by Elsevier. This book was released on 2014-06-28 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.
