I. is explored and explained in this best selling text. Assuming no prior knowledge, it covers topics like neural networks and robotics. This text explores the range of problems which have been and remain to be solved using A. I. tools and techniques. The second half of this text is an excellent reference.

    But often their solutions fall far short of what they want to accomplish and what is truly needed. Moreover, the answers they propose and fund often produce the opposite of what they want over time. We end up with temporary shelters that increase homelessness, drug busts that increase drug-related crime, or food aid that increases starvation. How do these unintended consequences come about and how can we avoid them? By applying conventional thinking to complex social problems, we often perpetuate the very problems we try so hard to solve, but it is possible to think differently, and get different results. Systems Thinking for Social Change enables readers to contribute more effectively to society by helping them understand what systems thinking is and why it is so important in their work. It also gives concrete guidance on how to incorporate systems thinking in problem solving, decision making, and strategic planning without becoming a technical expert. Systems thinking leader David Stroh walks readers through techniques he has used to help people improve their efforts to end homelessness, improve public health, strengthen education, design a system for early childhood development, protect child welfare, develop rural economies, facilitate the reentry of formerly incarcerated people into society, resolve identity-based conflicts, and more. The result is a highly readable, effective guide to understanding systems and using that knowledge to get the results you want.

    In this book, Chris Bernhardt offers an introduction to quantum computing that is accessible to anyone who is comfortable with high school mathematics. He explains qubits, entanglement, quantum teleportation, quantum algorithms, and other quantum-related topics as clearly as possible for the general reader. Bernhardt, a mathematician himself, simplifies the mathematics as much as he can and provides elementary examples that illustrate both how the math works and what it means.Bernhardt introduces the basic unit of quantum computing, the qubit, and explains how the qubit can be measured; discusses entanglement--which, he says, is easier to describe mathematically than verbally--and what it means when two qubits are entangled (citing Einstein's characterization of what happens when the measurement of one entangled qubit affects the second as "spooky action at a distance"); and introduces quantum cryptography. He recaps standard topics in classical computing--bits, gates, and logic--and describes Edward Fredkin's ingenious billiard ball computer. He defines quantum gates, considers the speed of quantum algorithms, and describes the building of quantum computers. By the end of the book, readers understand that quantum computing and classical computing are not two distinct disciplines, and that quantum computing is the fundamental form of computing. The basic unit of computation is the qubit, not the bit.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    The revised edition maintains the lucid flow and continuity which has been the strength of the book.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    With this hands-on guide, you'll learn how to write effective, idiomatic Python code by leveraging its best and possibly most neglected features. Author Luciano Ramalho takes you through Python's core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time.Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3.This book covers:Python data model: understand how special methods are the key to the consistent behavior of objectsData structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode ageFunctions as objects: view Python functions as first-class objects, and understand how this affects popular design patternsObject-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritanceControl flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packagesMetaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work"

    Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter -- on a self-contained page -- corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination.

    Readers will progress from building a basic physics engine to creating intelligent moving objects and complex systems, setting the foundation for further experiments in generative design. Subjects covered include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. The book's examples are written in Processing, an open-source language and development environment built on top of the Java programming language. On the book's website (http://www.natureofcode.com), the examples run in the browser via Processing's JavaScript mode.

    In this provocative and informative book, Don Norman writes that the complexity of our technology must mirror the complexity and richness of our lives. It's not complexity that's the problem, it's bad design. Bad design complicates things unnecessarily and confuses us. Good design can tame complexity.Norman gives us a crash course in the virtues of complexity. Designers have to produce things that tame complexity. But we too have to do our part: we have to take the time to learn the structure and practice the skills. This is how we mastered reading and writing, driving a car, and playing sports, and this is how we can master our complex tools.Complexity is good. Simplicity is misleading. The good life is complex, rich, and rewarding—but only if it is understandable, sensible, and meaningful.

    With great skill, he examines the very foundations of today's economic philosophy and adds a compelling analysis of the forces behind economic crisis. His goal is nothing less than preventing the seemingly endless procession of damaging boom-bust cycles, unsustainable economic bubbles, crippling credit crunches, and debilitating inflation. His direct, conscientious, and honest approach will captivate any reader and is an invaluable aid in understanding today's economy.

    This-is-Lean

    It offers a glimpse into the minds of a vast range of different peoples: early hunter-gatherers and farmers, ancient Egyptians, traditional Chinese sages, the founders of Christianity, trail-blazers of the Scientific Revolution, and those who constructed our modern consumer society.Taking the reader on an archaeological exploration of the mind, the author, an entrepreneur and sustainability leader, uses recent findings in cognitive science and systems theory to reveal the hidden layers of values that form today's cultural norms. Uprooting the tired clichés of the science-religion debate, he shows how medieval Christian rationalism acted as an incubator for scientific thought, which in turn shaped our modern vision of the conquest of nature. The author probes our current crisis of unsustainability and argues that it is not an inevitable result of human nature, but is culturally driven: a product of particular mental patterns that could conceivably be reshaped. By shining a light on our possible futures, the book foresees a coming struggle between two contrasting views of humanity: one driving to a technological endgame of artificially enhanced humans, the other enabling a sustainable future arising from our intrinsic connectedness with each other and the natural world. This struggle, it concludes, is one in which each of us will play a role through the meaning we choose to forge from the lives we lead.

    He traces AI's origins in ancient myth, through literary classics like Frankenstein, to today's sci-fi blockbusters, arguing that a fascination with AI is hardwired into the human psyche. He explains AI's history, technology, and potential; its manifestations in intelligent machines; its connections to neurology and consciousness, as well as—perhaps most tellingly—what AI reveals about us as human beings.In Our Own Image argues that we are on the brink of a fourth industrial revolution—poised to enter the age of Artificial Intelligence as science fiction becomes science fact. Ultimately, Zarkadakis observes, the fate of AI has profound implications for the future of science and humanity itself.