Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. Richly illustrated, Complexity: A Guided Tour--winner of the 2010 Phi Beta Kappa Book Award in Science--offers a wide-ranging overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for its contribution to solving some of the most important scientific questions of our time.

    There are updates throughout the book as well. These take into account important advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. The chapter "Economic Reality" has also been revised to reflect a change in emphasis in Simon's thinking about the respective roles of organizations and markets in economic systems."People sometimes ask me what they should read to find out about artificial intelligence. Herbert Simon's book The Sciences of the Artificial is always on the list I give them. Every page issues a challenge to conventional thinking, and the layman who digests it well will certainly understand what the field of artificial intelligence hopes to accomplish. I recommend it in the same spirit that I recommend Freud to people who ask about psychoanalysis, or Piaget to those who ask about child psychology: If you want to learn about a subject, start by reading its founding fathers." -- George A. Miller

    This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting.Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    While pursuing success in a dynamic, complex environment with limited resources and multiple goal conflicts, a succession of small, everyday decisions eventually produced breakdowns on a massive scale. We have trouble grasping the complexity and normality that gives rise to such large events. We hunt for broken parts, fixable properties, people we can hold accountable. Our analyses of complex system breakdowns remain depressingly linear, depressingly componential - imprisoned in the space of ideas once defined by Newton and Descartes. The growth of complexity in society has outpaced our understanding of how complex systems work and fail. Our technologies have gotten ahead of our theories. We are able to build things - deep-sea oil rigs, jackscrews, collateralized debt obligations - whose properties we understand in isolation. But in competitive, regulated societies, their connections proliferate, their interactions and interdependencies multiply, their complexities mushroom. This book explores complexity theory and systems thinking to understand better how complex systems drift into failure. It studies sensitive dependence on initial conditions, unruly technology, tipping points, diversity - and finds that failure emerges opportunistically, non-randomly, from the very webs of relationships that breed success and that are supposed to protect organizations from disaster. It develops a vocabulary that allows us to harness complexity and find new ways of managing drift.

    This book is a step-by-step guide to understanding the processes of variation, interaction, and selection that are at work in all organizations. The authors show how to use their own paradigm of "bottom up" management, the Complex Adaptive System-whether in science, public policy, or private commerce. This simple model of how people work together will change forever how we think about getting things done in a group."Harnessing Complexity distills the managerial essence of current research on complexity.…A very valuable contribution to the emerging theory of competition and competitive advantage."-C.K. Prahalad, University of Michigan, coauthor of Competing for the Future"A brilliant exposition that demystifies both the theory and use of Complex Adaptive Systems."-John Seely Brown, Xerox Corporation and Palo Alto Research Center

    Scharmer argues that our capacity to pay attention coshapes the world. What prevents us from attending to situations more effectively is that we aren't fully aware of that interior condition from which our attention and actions originate. Scharmer calls this lack of awareness our blind spot. He illuminates the blind spot in leadership today and offers hands-on methods to help change makers overcome it through the process, principles, and practices of Theory U. And he outlines a framework for updating the operating systems of our educational institutions, our economies, and our democracies. This book enables leaders and organizations in all industries and sectors to shift awareness, connect with the highest future possibilities, and strengthen the capacity to co-shape the future.

    Noting that most design—from consumer goods and digital technologies to built environments—currently serves capitalist ends, Escobar argues for the development of an “autonomous design” that eschews commercial and modernizing aims in favor of more collaborative and placed-based approaches. Such design attends to questions of environment, experience, and politics while focusing on the production of human experience based on the radical interdependence of all beings. Mapping autonomous design’s principles to the history of decolonial efforts of indigenous and Afro-descended people in Latin America, Escobar shows how refiguring current design practices could lead to the creation of more just and sustainable social orders.

    But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your work

    This is a wonderful book."--Charles Duhigg, author of The Power of Habit and Smarter Faster Better A crash on the Washington, D.C. metro system. An accidental overdose in a state-of-the-art hospital. An overcooked holiday meal. At first glance, these disasters seem to have little in common. But surprising new research shows that all these events--and the myriad failures that dominate headlines every day--share similar causes. By understanding what lies behind these failures, we can design better systems, make our teams more productive, and transform how we make decisions at work and at home.Weaving together cutting-edge social science with riveting stories that take us from the frontlines of the Volkswagen scandal to backstage at the Oscars, and from deep beneath the Gulf of Mexico to the top of Mount Everest, Chris Clearfield and Andras Tilcsik explain how the increasing complexity of our systems creates conditions ripe for failure and why our brains and teams can't keep up. They highlight the paradox of progress: Though modern systems have given us new capabilities, they've become vulnerable to surprising meltdowns--and even to corruption and misconduct.But Meltdown isn't just about failure; it's about solutions--whether you're managing a team or the chaos of your family's morning routine. It reveals why ugly designs make us safer, how a five-minute exercise can prevent billion-dollar catastrophes, why teams with fewer experts are better at managing risk, and why diversity is one of our best safeguards against failure. The result is an eye-opening, empowering, and entirely original book--one that will change the way you see our complex world and your own place in it.

    

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    The timely provision of relevant information with minimal 'noise' is critical to modern society and this is what information retrieval (IR) is all about. It is a dynamic subject, with current changes driven by the expansion of the World Wide Web, the advent of modern and inexpensive graphical user interfaces and the development of reliable and low-cost mass storage devices. Modern Information Retrieval discusses all these changes in great detail and can be used for a first course on IR as well as graduate courses on the topic.The organization of the book, which includes a comprehensive glossary, allows the reader to either obtain a broad overview or detailed knowledge of all the key topics in modern IR. The heart of the book is the nine chapters written by Baeza-Yates and Ribeiro-Neto, two leading exponents in the field. For those wishing to delve deeper into key areas there are further state-of-the-art ch

    Albert-László Barabási, the nation’s foremost expert in the new science of networks and author of Bursts, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are more similar than previously thought. Grasping a full understanding of network science will someday allow us to design blue-chip businesses, stop the outbreak of deadly diseases, and influence the exchange of ideas and information. Just as James Gleick and the Erdos–Rényi model brought the discovery of chaos theory to the general public, Linked tells the story of the true science of the future and of experiments in statistical mechanics on the internet, all vital parts of what would eventually be called the Barabási–Albert model.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases