Smoking has gone from doctor recommended to deadly. We used to think the Earth was the center of the universe and that Pluto was a planet. For decades, we were convinced that the brontosaurus was a real dinosaur. In short, what we know about the world is constantly changing.   But it turns out there’s an order to the state of knowledge, an explanation for how we know what we know. Samuel Arbesman is an expert in the field of scientometrics—literally the science of science. Knowl­edge in most fields evolves systematically and predict­ably, and this evolution unfolds in a fascinating way that can have a powerful impact on our lives.   Doctors with a rough idea of when their knowl­edge is likely to expire can be better equipped to keep up with the latest research. Companies and govern­ments that understand how long new discoveries take to develop can improve decisions about allocating resources. And by tracing how and when language changes, each of us can better bridge gen­erational gaps in slang and dialect.   Just as we know that a chunk of uranium can break down in a measurable amount of time—a radioactive half-life—so too any given field’s change in knowledge can be measured concretely. We can know when facts in aggregate are obsolete, the rate at which new facts are created, and even how facts spread.   Arbesman takes us through a wide variety of fields, including those that change quickly, over the course of a few years, or over the span of centuries. He shows that much of what we know consists of “mesofacts”—facts that change at a middle timescale, often over a single human lifetime. Throughout, he of­fers intriguing examples about the face of knowledge: what English majors can learn from a statistical analysis of The Canterbury Tales, why it’s so hard to measure a mountain, and why so many parents still tell kids to eat their spinach because it’s rich in iron.   The Half-life of Facts is a riveting journey into the counterintuitive fabric of knowledge. It can help us find new ways to measure the world while accepting the limits of how much we can know with certainty.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    Why do we recognize patterns? Is there such a thing as positive stress? Are we genetically programmed to be in conflict with each other? Those are just some of the 150 questions that the world's best scientific minds answer with elegant simplicity.With contributions from Jared Diamond, Richard Dawkins, Nassim Taleb, Brian Eno, Steven Pinker, and more, everything is explained in fun, uncomplicated terms that make the most complex concepts easy to comprehend.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Their visionary answers flow from the frontiers of psychology, philosophy, economics, physics, sociology, and more. Surprising and enlightening, these insights will revolutionize the way you think about yourself and the world.Contributors include:Daniel Kahneman on the “focusing illusion”Jonah Lehrer on controlling attentionRichard Dawkins on experimentationAubrey De Grey on conquering our fear of the unknownMartin Seligman on the ingredients of well-beingNicholas Carr on managing “cognitive load”Steven Pinker on win-win negotiatingDaniel Goleman on understanding our connection to the natural worldMatt Ridley on tapping collective intelligenceLisa Randall on effective theorizingBrian Eno on “ecological vision”J. Craig Venter on the multiple possible origins of life  Helen Fisher on temperamentSam Harris on the flow of thoughtLawrence Krauss on living with uncertainty

    Book by Waring, Chris

    We also need insights into the everyday things that frustrate and confuse us so that we can more effectively solve problems and get things done. Yet we know very little about when, why, or how insights are formed—or what blocks them. In Seeing What Others Don't, renowned cognitive psychologist Gary Klein unravels the mystery.Klein is a keen observer of people in their natural settings—scientists, businesspeople, firefighters, police officers, soldiers, family members, friends, himself—and uses a marvelous variety of stories to illuminate his research into what insights are and how they happen. What, for example, enabled Harry Markopolos to put the finger on Bernie Madoff? How did Dr. Michael Gottlieb make the connections between different patients that allowed him to publish the first announcement of the AIDS epidemic? What did Admiral Yamamoto see (and what did the Americans miss) in a 1940 British attack on the Italian fleet that enabled him to develop the strategy of attack at Pearl Harbor? How did a “smokejumper” see that setting another fire would save his life, while those who ignored his insight perished? How did Martin Chalfie come up with a million-dollar idea (and a Nobel Prize) for a natural flashlight that enabled researchers to look inside living organisms to watch biological processes in action?Klein also dissects impediments to insight, such as when organizations claim to value employee creativity and to encourage breakthroughs but in reality block disruptive ideas and prioritize avoidance of mistakes. Or when information technology systems are “dumb by design” and block potential discoveries. Both scientifically sophisticated and fun to read, Seeing What Others Don't shows that insight is not just a “eureka!” moment but a whole new way of understanding.

    All things are possible, only one thing actually happens; everything else is in the realm of probability. The twin disciplines of probability and statistics underpin every modern science and sketch the shape of all purposeful group activity- politics, economics, medicine, law, sports-giving humans a handle on the essential uncertainty of their existence. Yet while we are all aware of the hard facts, most of us still refuse to take account of probability-preferring to drive, not fly; buying into market blips; smoking cigarettes; denying we will ever age. There are some people, though-gamblers, risk buyers, forensic experts, doctors, strategists- who find probability's mass of incomplete uncertainties delightful and revelatory. "Chances Are" is their story. Combining philosophical and historical background with portraits of the men and women who command the forces of probability, this engaging, wide-ranging, and clearly written volume will be welcomed not only by the proven audiences for popular books like "E=MC2" and "The Golden Ratio" but by anyone interested in the workings of fate.

    In plain English, he enlightens us on the Asian crisis, corporate downsizing, and the globalization of the American economy, among other topics. The writing here brilliantly combines the acerbic style and clever analysis that has made Krugman famous. Imagine declaring New York its own country and you get a better picture of our trade balance with China and Hong Kong. Try reducing the economy to the production of hot dogs and buns and you’ll understand why common beliefs about the impact of production efficiency on labor demand are wrong. This is a collection that will amuse, provoke, and enlighten, in classic Paul Krugman style. "[Paul Krugman] writes better than any economist since John Maynard Keynes." — Rob Norton, Fortune "[Paul Krugman is] probably the most creative economist of his generation." — The Economist Winner of the John Bates Clark Medal

    Each chapter opens with a surprising insight—not a mathematic formula, but a common observation. From there, the authors leapfrog over math and anecdote toward profound ideas about nature, art, and music. Coincidences is a book for lovers of puzzles and posers of outlandish questions, lapsed math aficionados and the formula-phobic alike.

    In this work, eminent pollster John Zogby uses literally thousands of polls conducted over the past five decades to reveal a new and developing American character, one that will influence everything from the politicians they vote for to the goods and services they buy to the way they conduct our daily lives.

    Human beings have long been both fascinated and appalled by randomness. On the one hand, we love the thrill of a surprise party, the unpredictability of a budding romance, or the freedom of not knowing what tomorrow will bring. We are inexplicably delighted by strange coincidences and striking similarities. But we also hate uncertainty's dark side. From cancer to SARS, diseases strike with no apparent pattern. Terrorists attack, airplanes crash, bridges collapse, and we never know if we'll be that one in a million statistic. We are all constantly faced with situations and choices that involve randomness and uncertainty. A basic understanding of the rules of probability theory, applied to real-life circumstances, can help us to make sense of these situations, to avoid unnecessary fear, to seize the opportunities that randomness presents to us, and to actually enjoy the uncertainties we face. The reality is that when it comes to randomness, you can run, but you can't hide. So many aspects of our lives are governed by events that are simply not in our control. In this entertaining yet sophisticated look at the world of probabilities, author Jeffrey Rosenthal--an improbably talented math professor--explains the mechanics of randomness and teaches us how to develop an informed perspective on probability.

    Our brains, therefore, are designed not just to hunt and gather, but also to help us get ahead socially, often via deception and self-deception. But while we may be self-interested schemers, we benefit by pretending otherwise. The less we know about our own ugly motives, the better - and thus we don't like to talk or even think about the extent of our selfishness. This is "the elephant in the brain." Such an introspective taboo makes it hard for us to think clearly about our nature and the explanations for our behavior. The aim of this book, then, is to confront our hidden motives directly - to track down the darker, unexamined corners of our psyches and blast them with floodlights. Then, once everything is clearly visible, we can work to better understand ourselves: Why do we laugh? Why are artists sexy? Why do we brag about travel? Why do we prefer to speak rather than listen?Our unconscious motives drive more than just our private behavior; they also infect our venerated social institutions such as Art, School, Charity, Medicine, Politics, and Religion. In fact, these institutions are in many ways designed to accommodate our hidden motives, to serve covert agendas alongside their "official" ones. The existence of big hidden motives can upend the usual political debates, leading one to question the legitimacy of these social institutions, and of standard policies designed to favor or discourage them. You won't see yourself - or the world - the same after confronting the elephant in the brain.

    This title is intended for those who might enjoy an engaging dialogue on abstract mathematical ideas, and those who might wish to experience how new mathematics is created.