Showing 1–12 of 32 results
A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.
Reconstructing the Past seeks to clarify and help resolve the vexing methodological issues that arise when biologists try to answer such questions as whether human beings are more closely related to chimps than they are to gorillas. It explores the case for considering the philosophical idea of simplicity/parsimony as a useful principle for evaluating taxonomic theories of evolutionary relationships.For the past two decades, evolutionists have been vigorously debating the appropriate methods that should be used in systematics, the field that aims at reconstructing phylogenetic relationships among species. This debate over phylogenetic inference, Elliott Sober observes, raises broader questions of hypothesis testing and theory evaluation that run head on into long standing issues concerning simplicity/parsimony in the philosophy of science.Sober treats the problem of phylogenetic inference as a detailed case study in which the philosophical idea of simplicity/parsimony can be tested as a principle of theory evaluation. Bringing together philosophy and biology, as well as statistics, Sober builds a general framework for understanding the circumstances in which parsimony makes sense as a tool of phylogenetic inference. Along the way he provides a detailed critique of parsimony in the biological literature, exploring the strengths and limitations of both statistical and nonstatistical cladistic arguments.
A brilliant and unsettling play from one of the UK’s leading dramatists. At the opening of the play, a young girl is questioning her aunt about having seen her uncle hitting people with an iron bar; by the end, several years later, the whole world is at war – including birds and animals. Far Away is a howl of anguish at the increasing – and increasingly accepted – levels of inhumanity in a world seemingly perpetually involved in conflict. Far Away is a SET TEXT for AS/A-Level Drama (WJEC)
An expert on business strategy offers a pragmatic take on how businesses of all sizes balance the competing demands of profitability and employment with sustainability. The demands and stresses on companies only grow as executives face a multitude of competing business goals. Their stakeholders are interested in corporate profits, jobs, business growth, and environmental sustainability. In this book, business strategy expert Yossi Sheffi offers a pragmatic take on how businesses of all sizes — from Coca Cola and Siemens to Dr. Bronner’s Magical Soaps and Patagonia — navigate these competing goals. Drawing on extensive interviews with more than 250 executives, Sheffi examines the challenges, solutions, and implications of balancing traditional business goals with sustainability. Sheffi, author of the widely read The Resilient Enterprise, argues that business executives’ personal opinions on environmental sustainability are irrelevant. The business merits of environmental sustainability are based on the fact that even the most ardent climate change skeptics in the C-suite face natural resource costs, public relations problems, regulatory burdens, and a green consumer segment. Sheffi presents three basic business rationales for corporate sustainability efforts: cutting costs, reducing risk, and achieving growth. For companies, sustainability is not a simple case of “profits versus planet” but is instead a more subtle issue of (some) people versus (other) people — those looking for jobs and inexpensive goods versus others who seek a pristine environment. This book aims to help companies satisfy these conflicting motivations for both economic growth and environmental sustainability.
How developments in science and technology may enable the emergence of purely digital minds — intelligent machines equal to or greater in power than the human brain. What do computers, cells, and brains have in common? Computers are electronic devices designed by humans; cells are biological entities crafted by evolution; brains are the containers and creators of our minds. But all are, in one way or another, information-processing devices. The power of the human brain is, so far, unequaled by any existing machine or known living being. Over eons of evolution, the brain has enabled us to develop tools and technology to make our lives easier. Our brains have even allowed us to develop computers that are almost as powerful as the human brain itself. In this book, Arlindo Oliveira describes how advances in science and technology could enable us to create digital minds. Exponential growth is a pattern built deep into the scheme of life, but technological change now promises to outstrip even evolutionary change. Oliveira describes technological and scientific advances that range from the discovery of laws that control the behavior of the electromagnetic fields to the development of computers. He calls natural selection the ultimate algorithm, discusses genetics and the evolution of the central nervous system, and describes the role that computer imaging has played in understanding and modeling the brain. Having considered the behavior of the unique system that creates a mind, he turns to an unavoidable question: Is the human brain the only system that can host a mind? If digital minds come into existence — and, Oliveira says, it is difficult to argue that they will not — what are the social, legal, and ethical implications? Will digital minds be our partners, or our rivals?
Why the United States has developed an economy divided between rich and poor and how racism helped bring this about. The United States is becoming a nation of rich and poor, with few families in the middle. In this book, MIT economist Peter Temin offers an illuminating way to look at the vanishing middle class. Temin argues that American history and politics, particularly slavery and its aftermath, play an important part in the widening gap between rich and poor. Temin employs a well-known, simple model of a dual economy to examine the dynamics of the rich/poor divide in America, and outlines ways to work toward greater equality so that America will no longer have one economy for the rich and one for the poor. Many poorer Americans live in conditions resembling those of a developing country — substandard education, dilapidated housing, and few stable employment opportunities. And although almost half of black Americans are poor, most poor people are not black. Conservative white politicians still appeal to the racism of poor white voters to get support for policies that harm low-income people as a whole, casting recipients of social programs as the Other — black, Latino, not like “us.” Politicians also use mass incarceration as a tool to keep black and Latino Americans from participating fully in society. Money goes to a vast entrenched prison system rather than to education. In the dual justice system, the rich pay fines and the poor go to jail.
A new edition of a book, written in a humorous question-and-answer style, that shows how to implement and use an elegant little programming language for logic programming. The goal of this book is to show the beauty and elegance of relational programming, which captures the essence of logic programming. The book shows how to implement a relational programming language in Scheme, or in any other functional language, and demonstrates the remarkable flexibility of the resulting relational programs. As in the first edition, the pedagogical method is a series of questions and answers, which proceed with the characteristic humor that marked The Little Schemer and The Seasoned Schemer. Familiarity with a functional language or with the first five chapters of T he Little Schemer is assumed. For this second edition, the authors have greatly simplified the programming language used in the book, as well as the implementation of the language. In addition to revising the text extensively, and simplifying and revising the “Laws” and “Commandments,” they have added explicit “Translation” rules to ease translation of Scheme functions into relations.
Signs, artwork, stories, and photographs from the March for Science Movement and community. In January 2017, an idea on social media launched the global March for Science movement. In a few short months, more than 600 cities, 250 partners, and countless volunteers banded together to organize a historical event that drew people of all backgrounds, interests, and political leanings. On April 22, 2017, more than one million marchers worldwide took to the streets to stand up for the importance of science in society and their own lives — and each of them has a story to tell. Through signs, artwork, stories, and photographs, Science Not Silence shares some of the voices from the March for Science movement. From Antarctica to the North Pole, from under the sea to the tops of mountains, whether alone or alongside thousands, people marched for science. A citizen scientist with advanced ALS spent countless hours creating an avatar using technology that tracks his eye movements so that he could give a speech. Couples carrying babies born using in vitro fertilization dressed them in shirts that said “Made By Science.” The former U.S. Chief Data Scientist spoke about what really makes America great. Activists championed the ways science should serve marginalized communities. Artists created stunning signs, patients marched with the doctors who saved them, and scientists marched with the community that supports them. Every story is a call to action. The march was just the beginning. Now the real work begins. Science Not Silence celebrates the success of the movement, amplifies the passion and creativity of its supporters, and reminds everyone how important it is to keep marching.
An accessible, concise primer on the neurological trait of synesthesia — vividly felt sensory couplings — by a founder of the field. One in twenty-three people carry the genes for the synesthesia. Not a disorder but a neurological trait — like perfect pitch — synesthesia creates vividly felt cross-sensory couplings. A synesthete might hear a voice and at the same time see it as a color or shape, taste its distinctive flavor, or feel it as a physical touch. In this volume in the MIT Press Essential Knowledge series, Richard Cytowic, the expert who returned synesthesia to mainstream science after decades of oblivion, offers a concise, accessible primer on this fascinating human experience. Cytowic explains that synesthesia’s most frequent manifestation is seeing days of the week as colored, followed by sensing letters, numerals, and punctuation marks in different hues even when printed in black. Other manifestations include tasting food in shapes, seeing music in moving colors, and mapping numbers and other sequences spatially. One synesthete declares, “Chocolate smells pink and sparkly”; another invents a dish (chicken, vanilla ice cream, and orange juice concentrate) that tastes intensely blue. Cytowic, who in the 1980s revived scientific interest in synesthesia, sees it now understood as a spectrum, an umbrella term that covers five clusters of outwardly felt couplings that can occur via several pathways. Yet synesthetic or not, each brain uniquely filters what it perceives. Cytowic reminds us that each individual’s perspective on the world is thoroughly subjective.
A seasoned Zen practitioner and neurologist looks more deeply at mindfulness, connecting it to our subconscious and to memory and creativity. This is a book for readers who want to probe more deeply into mindfulness. It goes beyond the casual, once-in-awhile meditation in popular culture, grounding mindfulness in daily practice, Zen teachings, and recent research in neuroscience. In Living Zen Remindfully, James Austin, author of the groundbreaking Zen and the Brain, describes authentic Zen training — the commitment to a process of regular, ongoing daily life practice. This training process enables us to unlearn unfruitful habits, develop more wholesome ones, and lead a more genuinely creative life. Austin shows that mindfulness can mean more than our being conscious of the immediate “now.” It can extend into the subconscious, where most of our brain’s activities take place, invisibly. Austin suggests ways that long-term meditative training helps cultivate the hidden, affirmative resource of our unconscious memory. Remindfulness, as Austin terms it, can help us to adapt more effectively and to live more authentic lives. Austin discusses different types of meditation, meditation and problem-solving, and the meaning of enlightenment. He addresses egocentrism (self-centeredness) and allocentrism (other-centeredness), and the blending of focal and global attention. He explains the remarkable processes that encode, store, and retrieve our memories, focusing on the covert, helpful remindful processes incubating at subconscious levels. And he considers the illuminating confluence of Zen, clinical neurology, and neuroscience. Finally, he describes an everyday life of “living Zen,” drawing on the poetry of Basho, the seventeenth-century haiku master.
Questions about the physical world, the mind, and technology in conversations that reveal a rich seam of interacting ideas. Science today is more a process of collaboration than moments of individual “eurekas.” This book recreates that kind of synergy by offering a series of interconnected dialogues with leading scientists who are asked to reflect on key questions and concepts about the physical world, technology, and the mind. These thinkers offer both specific observations and broader comments about the intellectual traditions that inform these questions; doing so, they reveal a rich seam of interacting ideas. The persistent paradox of our era is that in a world of unprecedented access to information, many of the most important questions remain unsolved. These conversations (conducted by a veteran science writer, Adolfo Plasencia) reflect this, with scientists addressing such issues as intelligence, consciousness, global warming, energy, technology, matter, the possibility of another earth, changing the past, and even the philosophical curveball, “is the universe a hologram?” The dialogues discuss such fascinating aspects of the physical world as the function of the quantum bit, the primordial cosmology of the universe, and the wisdom of hewn stones. They offer optimistic but reasoned views of technology, considering convergence culture, algorithms, “Beauty ≠ Truth,” the hacker ethic, AI, and other topics. And they offer perspectives from a range of disciplines on intelligence, discussing subjects that include the neurophysiology of the brain, affective computing, collaborative innovation, and the wisdom of crowds. Conversations with Hal Abelson, Ricardo Baeza-Yates, John Perry Barlow, Javier Benedicto, José Bernabéu, Michail Bletsas, Jose M. Carmena, David Casacuberta, Yung Ho Chang, Ignacio Cirac, Gianluigi Colalucci, Avelino Corma, Bernardo Cuenca Grau, Javier Echeverria, José Hernández-Orallo, Hiroshi Ishii, Pablo Jarillo-Herrero, Henry Jenkins, Anne Margulies, Mario J. Molina, Tim O’Reilly, John Ochsendorf, Paul Osterman, Alvaro Pascual-Leone, Rosalind W. Picard, Howard Rheingold, Alejandro W. Rodriguez, Israel Ruiz, Sara Seager, Richard Stallman, Antonio Torralba, Bebo White, José María Yturralde
What artificial intelligence can tell us about the mind and intelligent behavior. What can artificial intelligence teach us about the mind? If AI’s underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It’s a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example. In this book, Hector Levesque shifts the conversation to “good old fashioned artificial intelligence,” which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns — as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there’s no more soy milk. Levesque considers the role of language in learning. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence — the Winograd Schema Test, developed by Levesque and his colleagues. “If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it,” he observes. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.