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Neuroplasticity (The MIT Press Essential Knowledge series)
Fifty years ago, neuroscientists thought that a mature brain was fixed like a fly in amber, unable to change. Today, we know that our brains and nervous systems change throughout our lifetimes. This concept of neuroplasticity has captured the imagination of a public eager for self-improvement -- and has inspired countless Internet entrepreneurs who peddle dubious "brain training" games and apps. In this book, Moheb Costandi offers a concise and engaging overview of neuroplasticity for the general reader, describing how our brains change continuously in response to our actions and experiences. Costandi discusses key experimental findings, and describes how our thinking about the brain has evolved over time. He explains how the brain changes during development, and the "synaptic pruning" that takes place before brain maturity. He shows that adult brains can grow new cells (citing, among many other studies, research showing that sexually mature male canaries learn a new song every year). He describes the kind of brain training that can bring about improvement in brain function. It's not gadgets and games that promise to "rewire your brain" but such sustained cognitive tasks as learning a musical instrument or a new language. (Costandi also notes that London cabbies increase their gray matter after rigorous training in their city's complicated streets.) He tells how brains compensate after stroke or injury; describes addiction and pain as maladaptive forms of neuroplasticity; and considers brain changes that accompany childhood, adolescence, parenthood, and aging. Each of our brains is custom-built. Neuroplasticity is at the heart of what makes us human.
Published by: The MIT Press | Publication date: 08/16/2016
Kindle book details: Kindle Edition, 193 pages

What Algorithms Want: Imagination in the Age of Computing (MIT Press)
We depend on -- we believe in -- algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations -- the marriage vow, the shaman's curse -- do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm -- in practical terms, "a method for solving a problem" -- has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking. Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things.If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of "algorithmic reading" and scholarship that attends to process, spearheading a new experimental humanities.
Author: Ed Finn
Published by: The MIT Press | Publication date: 03/06/2017
Kindle book details: Kindle Edition, 272 pages

Metadata (The MIT Press Essential Knowledge series)
When "metadata" became breaking news, appearing in stories about surveillance by the National Security Agency, many members of the public encountered this once-obscure term from information science for the first time. Should people be reassured that the NSA was "only" collecting metadata about phone calls -- information about the caller, the recipient, the time, the duration, the location -- and not recordings of the conversations themselves? Or does phone call metadata reveal more than it seems? In this book, Jeffrey Pomerantz offers an accessible and concise introduction to metadata. In the era of ubiquitous computing, metadata has become infrastructural, like the electrical grid or the highway system. We interact with it or generate it every day. It is not, Pomerantz tell us, just "data about data." It is a means by which the complexity of an object is represented in a simpler form. For example, the title, the author, and the cover art are metadata about a book. When metadata does its job well, it fades into the background; everyone (except perhaps the NSA) takes it for granted.Pomerantz explains what metadata is, and why it exists. He distinguishes among different types of metadata -- descriptive, administrative, structural, preservation, and use -- and examines different users and uses of each type. He discusses the technologies that make modern metadata possible, and he speculates about metadata's future. By the end of the book, readers will see metadata everywhere. Because, Pomerantz warns us, it's metadata's world, and we are just living in it.
Published by: The MIT Press | Publication date: 11/13/2015
Kindle book details: Kindle Edition, 256 pages

Streaming, Sharing, Stealing: Big Data and the Future of Entertainment (MIT Press)
"[The authors explain] gently yet firmly exactly how the internet threatens established ways and what can and cannot be done about it. Their book should be required for anyone who wishes to believe that nothing much has changed." -- The Wall Street Journal"Packed with examples, from the nimble-footed who reacted quickly to adapt their businesses, to laggards who lost empires." -- Financial TimesTraditional network television programming has always followed the same script: executives approve a pilot, order a trial number of episodes, and broadcast them, expecting viewers to watch a given show on their television sets at the same time every week. But then came Netflix's House of Cards. Netflix gauged the show's potential from data it had gathered about subscribers' preferences, ordered two seasons without seeing a pilot, and uploaded the first thirteen episodes all at once for viewers to watch whenever they wanted on the devices of their choice. In this book, Michael Smith and Rahul Telang, experts on entertainment analytics, show how the success of House of Cards upended the film and TV industries -- and how companies like Amazon and Apple are changing the rules in other entertainment industries, notably publishing and music. We're living through a period of unprecedented technological disruption in the entertainment industries. Just about everything is affected: pricing, production, distribution, piracy. Smith and Telang discuss niche products and the long tail, product differentiation, price discrimination, and incentives for users not to steal content. To survive and succeed, businesses have to adapt rapidly and creatively. Smith and Telang explain how.How can companies discover who their customers are, what they want, and how much they are willing to pay for it? Data. The entertainment industries, must learn to play a little "moneyball." The bottom line: follow the data.
Published by: The MIT Press | Publication date: 08/08/2016
Kindle book details: Kindle Edition, 232 pages

Computing: A Concise History (The MIT Press Essential Knowledge series)
The history of computing could be told as the story of hardware and software, or the story of the Internet, or the story of "smart" hand-held devices, with subplots involving IBM, Microsoft, Apple, Facebook, and Twitter. In this concise and accessible account of the invention and development of digital technology, computer historian Paul Ceruzzi offers a broader and more useful perspective. He identifies four major threads that run throughout all of computing's technological development: digitization--the coding of information, computation, and control in binary form, ones and zeros; the convergence of multiple streams of techniques, devices, and machines, yielding more than the sum of their parts; the steady advance of electronic technology, as characterized famously by "Moore's Law"; and the human-machine interface. Ceruzzi guides us through computing history, telling how a Bell Labs mathematician coined the word "digital" in 1942 (to describe a high-speed method of calculating used in anti-aircraft devices), and recounting the development of the punch card (for use in the 1890 U.S. Census). He describes the ENIAC, built for scientific and military applications; the UNIVAC, the first general purpose computer; and ARPANET, the Internet's precursor. Ceruzzi's account traces the world-changing evolution of the computer from a room-size ensemble of machinery to a "minicomputer" to a desktop computer to a pocket-sized smart phone. He describes the development of the silicon chip, which could store ever-increasing amounts of data and enabled ever-decreasing device size. He visits that hotbed of innovation, Silicon Valley, and brings the story up to the present with the Internet, the World Wide Web, and social networking.
Published by: The MIT Press | Publication date: 06/15/2012
Kindle book details: Kindle Edition, 199 pages

Introduction to Computation and Programming Using Python: With Application to Understanding Data (MIT Press)
This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.
Published by: The MIT Press | Publication date: 08/08/2016
Kindle book details: Kindle Edition, 472 pages

Principles of Neural Design (MIT Press)
Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods. A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing. In this book, Peter Sterling and Simon Laughlin, two leading neuroscientists, strive to fill this gap, outlining a set of organizing principles to explain the whys of neural design that allow the brain to compute so efficiently. Setting out to "reverse engineer" the brain -- disassembling it to understand it -- Sterling and Laughlin first consider why an animal should need a brain, tracing computational abilities from bacterium to protozoan to worm. They examine bigger brains and the advantages of "anticipatory regulation"; identify constraints on neural design and the need to "nanofy"; and demonstrate the routes to efficiency in an integrated molecular system, phototransduction. They show that the principles of neural design at finer scales and lower levels apply at larger scales and higher levels; describe neural wiring efficiency; and discuss learning as a principle of biological design that includes "save only what is needed."Sterling and Laughlin avoid speculation about how the brain might work and endeavor to make sense of what is already known. Their distinctive contribution is to gather a coherent set of basic rules and exemplify them across spatial and functional scales.
Published by: The MIT Press | Publication date: 06/12/2015
Kindle book details: Kindle Edition, 568 pages

Driverless: Intelligent Cars and the Road Ahead (MIT Press)
In the coming decade, intelligent cars and trucks will hit the streets, rearranging established industries, enabling new business models, saving lives, easing air pollution, and reshaping cities. Long-suffering commuters will finally be able to live wherever they wish, and read (or nap) on their drive to work. The bad news is that hundreds of thousands of driving-related jobs are at risk, our legal system will need to be re-drawn, and passenger privacy could become a luxury of the past.This book provides a jargon-free overview of the disruptive technologies that enable driverless cars, in particular, Deep Learning, an emerging form of artificial intelligence. Although the technology is nearly ready, car companies and policy makers may not be. The authors shed light on the opportunities and risks posed by self-driving vehicles, and make a compelling case for why their development should be our society's next concerted "Apollo moment." Driverless is a comprehensive and entertaining exploration of self-driving cars. 
Published by: The MIT Press | Publication date: 09/30/2016
Kindle book details: Kindle Edition, 324 pages

Machine Learning: The New AI (The MIT Press Essential Knowledge series)
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning -- the foundation of efforts to process that data into knowledge -- has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
Published by: The MIT Press | Publication date: 09/30/2016
Kindle book details: Kindle Edition, 224 pages

Competitive Strategy: Options and Games (MIT Press)
Corporate managers who face both strategic uncertainty and market uncertainty confront a classic trade-off between commitment and flexibility. They can stake a claim by making a large capital investment today, influencing their rivals' behavior, or they can take a "wait and see" approach to avoid adverse market consequences tomorrow. In Competitive Strategy, Benoît Chevalier-Roignant and Lenos Trigeorgis describe an emerging paradigm that can quantify and balance commitment and flexibility, "option games," by which the decision-making approaches of real options and game theory can be combined. The authors first discuss prerequisite concepts and tools from basic game theory, industrial organization, and real options analysis, and then present the new approach in discrete time and later in continuous time. Their presentation of continuous-time option games is the first systematic coverage of the topic and fills a significant gap in the existing literature. Competitive Strategy provides a rigorous yet pragmatic and intuitive approach to strategy formulation. It synthesizes research in the areas of strategy, economics, and finance in a way that is accessible to readers not necessarily expert in the various fields involved.
Published by: The MIT Press | Publication date: 12/22/2011
Kindle book details: Kindle Edition, 520 pages
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