Algorithmic Reading Comprehension

Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing. Presents the first comprehensive resource on machine reading comprehension (MRC) Performs a deep-dive into MRC, from fundamentals to latest developments Offers the latest thinking and research in the field of MRC, including the BERT model Provides theoretical discussion, code analysis, and real-world applications of MRC Gives insight from research which has led to surpassing human parity in MRC

Reading comprehension solving is a skill. It is different from literature in the sense that it is independent of interpretation. There is no subjectivity in RC questions. The art of solving can be learnt by mastering the topic at three levels – The ability to read, to eliminate options and to build mistake patterns to learn from them. These three levels lie at the core of "Algorithmic Reading Comprehension" – an approach to build expertise in RC solving. This approach was created by my team and me over the past 6 years of training thousands of students for the Reading comprehension section for CAT-GMAT and other aptitude-based entrance examinations. The book deals with two major aspects of reading, "Central Idea" and "Contextual Word Learning". It moves on to discuss the meaning of different question types asked across exams and provides elimination frameworks to tackle tricky options. Finally, students get many passages arranged in levels and then in a practice chapter to practice and to learn from. Welcome to the world of flawless RC learning!

The Psychology of Learning and Motivation series publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditioning to complex learning and problem solving. Each chapter thoughtfully integrates the writings of leading contributors, who present and discuss significant bodies of research relevant to their discipline. Volume 51 includes chapters on such varied topics as emotion and memory interference, electrophysiology, mathematical cognition, and reader participation in narrative. Volume 51 of the highly regarded Psychology of Learning and Motivation series An essential reference for researchers and academics in cognitive science Relevant to both applied concerns and basic research

A hands-on, problem-based introduction to building algorithms and data structures to solve problems with a computer. Algorithmic Thinking will teach you how to solve challenging programming problems and design your own algorithms. Daniel Zingaro, a master teacher, draws his examples from world-class programming competitions like USACO and IOI. You'll learn how to classify problems, choose data structures, and identify appropriate algorithms. You'll also learn how your choice of data structure, whether a hash table, heap, or tree, can affect runtime and speed up your algorithms; and how to adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems. Line-by-line breakdowns of the code will teach you how to use algorithms and data structures like: The breadth-first search algorithm to find the optimal way to play a board game or find the best way to translate a book Dijkstra's algorithm to determine how many mice can exit a maze or the number of fastest routes between two locations The union-find data structure to answer questions about connections in a social network or determine who are friends or enemies The heap data structure to determine the amount of money given away in a promotion The hash-table data structure to determine whether snowflakes are unique or identify compound words in a dictionary NOTE: Each problem in this book is available on a programming-judge website. You'll find the site's URL and problem ID in the description. What's better than a free correctness check?

This book presents high-quality research on the concepts and developments in the field of information and communication technologies, and their applications. It features 134 rigorously selected papers (including 10 poster papers) from the Future of Information and Communication Conference 2020 (FICC 2020), held in San Francisco, USA, from March 5 to 6, 2020, addressing state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of future research. Discussing various aspects of communication, data science, ambient intelligence, networking, computing, security and Internet of Things, the book offers researchers, scientists, industrial engineers and students valuable insights into the current research and next generation information science and communication technologies.

Artificial intelligence not only powers our cars, hospitals and courtrooms: predictive algorithms are becoming deeply lodged inside us too. Machine intelligence is learning our private preferences and discreetly shaping our personal behaviour, telling us how to live, who to befriend and who to date. In Algorithmic Intimacy, Anthony Elliott examines the power of predictive algorithms in reshaping personal relationships today. From Facebook friends and therapy chatbots to dating apps and quantified sex lives, Elliott explores how machine intelligence is working within us, amplifying our desires and steering our personal preferences. He argues that intimate relationships today are threatened not by the digital revolution as such, but by the orientation of various life strategies unthinkingly aligned with automated machine intelligence. Our reliance on algorithmic recommendations, he suggests, reflects a growing emergency in personal agency and human bonds. We need alternatives, innovation and experimentation for the interpersonal, intimate effort of ongoing translation back and forth between the discourses of human and machine intelligence. Accessible and compelling, this book sheds fresh light on the impact of artificial intelligence on the most intimate aspects of our lives. It will appeal to students in the social sciences and humanities and to a wide range of general readers.

This book is useful to understand and write alongside non-human agents, examine the impact of algorithms and AI on writing, and accommodate relationships with autonomous agents. This ground-breaking future-driven framework prepares scholars and practitioners to investigate and plan for the social, digital literacy, and civic implications arising from emerging technologies. This book prepares researchers, students, practitioners, and citizens to work with AI writers, virtual humans, and social robots. This book explores prompts to envision how fields and professions will change. The book’s unique integration with Fabric of Digital Life, a database and structured content repository for conducting social and cultural analysis of emerging technologies, provides concrete examples throughout. Readers gain imperative direction for collaborative, algorithmic, and autonomous writing futures.

Global inequalities and our social identities shape who we are, who we can be online, and what we know. From social media to search engines to Wikipedia, the internet is thoroughly embedded in how we produce, find, and share knowledge around the world. Who Should We Be Online? examines the challenges of the online world using numerous epistemological approaches. Tackling problems of online content moderation, fake news, and hoaxes, Frost-Arnold locates the role that sexism, racism, and other forms of oppression play in creating and sharing knowledge online. Timely and interdisciplinary, Who Should We Be Online? weaves together internet studies scholarship from across the humanities, social sciences, and computer science. Frost-Arnold recognizes that the internet can both fuel ignorance and misinformation and simultaneously offer knowledge to marginalized groups and activists. Presenting case studies of moderators, imposters, and other internet personas, Frost-Arnold explains the problems with our current internet ecosystem and imagines a more just online future. Who Should We Be Online? argues for a social epistemology that values truth and objectivity, while recognizing that inequalities shape our collective ability to attain these goals. Frost-Arnold proposes numerous suggestions and reform strategies to make the internet more conducive to knowledge production and sharing.