MIT Press Ltd – förlag

A more powerful innovation, which seeks to discover not how things work but why we need things.The standard text on innovation advises would-be innovators to conduct creative brainstorming sessions and seek input from outsiders—users or communities. This kind of innovating can be effective at improving products but not at capturing bigger opportunities in the marketplace. In this book Roberto Verganti offers a new approach—one that does not set out to solve existing problems but to find breakthrough meaningful experiences. There is no brainstorming—which produces too many ideas, unfiltered—but a vision, subject to criticism. It does not come from outsiders but from one person's unique interpretation.The alternate path to innovation mapped by Verganti aims to discover not how things work but why we need things. It gives customers something more meaningful—something they can love. Verganti describes the work of companies, including Nest Labs, Apple, Yankee Candle, and Philips Healthcare, that have created successful businesses by doing just this. Nest Labs, for example, didn't create a more advanced programmable thermostat, because people don't love to program their home appliances. Nest's thermostat learns the habits of the household and bases its temperature settings accordingly.Verganti discusses principles and practices, methods and implementation. The process begins with a vision and proceeds through developmental criticism, first from a sparring partner and then from a circle of radical thinkers, then from external experts and interpreters, and only then from users.Innovation driven by meaning is the way to create value in our current world, where ideas are abundant but novel visions are rare. If something is meaningful for both the people who create it and the people who consume it, business value follows.

An argument that love requires the courage to accept self-negation for the sake of discovering the Other.Byung-Chul Han is one of the most widely read philosophers in Europe today, a member of the new generation of German thinkers that includes Markus Gabriel and Armen Avanessian. In The Agony of Eros, a bestseller in Germany, Han considers the threat to love and desire in today's society. For Han, love requires the courage to accept self-negation for the sake of discovering the Other. In a world of fetishized individualism and technologically mediated social interaction, it is the Other that is eradicated, not the self. In today's increasingly narcissistic society, we have come to look for love and desire within the “inferno of the same.” Han offers a survey of the threats to Eros, drawing on a wide range of sources—Lars von Trier's film Melancholia, Wagner's Tristan und Isolde,Fifty Shades of Grey, Michel Foucault (providing a scathing critique of Foucault's valorization of power), Martin Buber, Hegel, Baudrillard, Flaubert, Barthes, Plato, and others. Han considers the “pornographication” of society, and shows how pornography profanes eros; addresses capitalism's leveling of essential differences; and discusses the politics of eros in today's “burnout society.” To be dead to love, Han argues, is to be dead to thought itself. Concise in its expression but unsparing in its insight, The Agony of Eros is an important and provocative entry in Han's ongoing analysis of contemporary society.This remarkable essay, an intellectual experience of the first order, affords one of the best ways to gain full awareness of and join in one of the most pressing struggles of the day: the defense, that is to say—as Rimbaud desired it—the “reinvention” of love.—from the foreword by Alain Badiou

A compelling argument for including the human perspective within science, and for how human experience makes science possible.“This is by far the best book I've read this year.”—Michael Pollan, Professor of the Practice of Non-fiction, Harvard University; #1 New York Times bestselling authorIt’s tempting to think that science gives us a God’s-eye view of reality. But we neglect the place of human experience at our peril. In The Blind Spot, astrophysicist Adam Frank, theoretical physicist Marcelo Gleiser, and philosopher Evan Thompson call for a revolutionary scientific worldview, where science includes—rather than ignores or tries not to see—humanity’s lived experience as an inescapable part of our search for objective truth. The authors present science not as discovering an absolute reality but rather as a highly refined, constantly evolving form of human experience. They urge practitioners to reframe how science works for the sake of our future in the face of the planetary climate crisis and increasing science denialism.Since the dawn of the Enlightenment, humanity has looked to science to tell us who we are, where we come from, and where we’re going, but we’ve gotten stuck thinking we can know the universe from outside our position in it. When we try to understand reality only through external physical things imagined from this outside position, we lose sight of the necessity of experience. This is the Blind Spot, which the authors show lies behind our scientific conundrums about time and the origin of the universe, quantum physics, life, AI and the mind, consciousness, and Earth as a planetary system. The authors propose an alternative vision: scientific knowledge is a self-correcting narrative made from the world and our experience of it evolving together. To finally “see” the Blind Spot is to awaken from a delusion of absolute knowledge and to see how reality and experience intertwine. The Blind Spot goes where no science book goes, urging us to create a new scientific culture that views ourselves both as an expression of nature and as a source of nature’s self-understanding, so that humanity can flourish in the new millennium.

A space cruiser, in search of its sister ship, encounters beings descended from self-replicating machines.

This overview of the ethical issues raised by artificial intelligence moves beyond hype and nightmare scenarios to address concrete questions—offering a compelling, necessary read for our ChatGPT era.   Artificial intelligence powers Google’s search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the usual hype and nightmare scenarios to address concrete questions.   Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein’s monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society.

The classic work on the evaluation of city form.

What does the city's form actually mean to the people who live there? What can the city planner do to make the city's image more vivid and memorable to the city dweller? To answer these questions, Mr. Lynch, supported by studies of Los Angeles, Boston, and Jersey City, formulates a new criterion—imageability—and shows its potential value as a guide for the building and rebuilding of cities. The wide scope of this study leads to an original and vital method for the evaluation of city form. The architect, the planner, and certainly the city dweller will all want to read this book.

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment 

Umberto Eco's wise and witty guide to researching and writing a thesis, published in English for the first time. By the time Umberto Eco published his best-selling novel The Name of the Rose, he was one of Italy's most celebrated intellectuals, a distinguished academic and the author of influential works on semiotics. Some years before that, in 1977, Eco published a little book for his students, How to Write a Thesis, in which he offered useful advice on all the steps involved in researching and writing a thesis—from choosing a topic to organizing a work schedule to writing the final draft. Now in its twenty-third edition in Italy and translated into seventeen languages, How to Write a Thesis has become a classic. Remarkably, this is its first, long overdue publication in English. Eco's approach is anything but dry and academic. He not only offers practical advice but also considers larger questions about the value of the thesis-writing exercise. How to Write a Thesis is unlike any other writing manual. It reads like a novel. It is opinionated. It is frequently irreverent, sometimes polemical, and often hilarious. Eco advises students how to avoid “thesis neurosis” and he answers the important question “Must You Read Books?” He reminds students “You are not Proust” and “Write everything that comes into your head, but only in the first draft.” Of course, there was no Internet in 1977, but Eco's index card research system offers important lessons about critical thinking and information curating for students of today who may be burdened by Big Data.How to Write a Thesis belongs on the bookshelves of students, teachers, writers, and Eco fans everywhere. Already a classic, it would fit nicely between two other classics: Strunk and White and The Name of the Rose.ContentsThe Definition and Purpose of a Thesis • Choosing the Topic • Conducting Research • The Work Plan and the Index Cards • Writing the Thesis • The Final Draft

An accessible and gratifying introduction to the world of paranormal beliefs and bizarre experiences.Ghostly encounters, alien abduction, reincarnation, talking to the dead, UFO sightings, inexplicable coincidences, out-of-body and near-death experiences. Are these legitimate phenomena? If not, how should we go about understanding them? In this fascinating book, Chris French investigates paranormal claims to discover what lurks behind this “weird shit.” French provides authoritative, evidence-based explanations for a wide range of superficially mysterious phenomena, and then goes further to draw out lessons with wider applications to many other aspects of modern society where critical thinking is urgently needed.Using academic, comprehensive, logical, and, at times, mathematical approaches, The Science of Weird Shit convincingly debunks ESP, communicating with the dead, and alien abduction claims, among other phenomena. All the while, however, French maintains that our belief in such phenomena is neither ridiculous nor trivial; if anything, such claims can tell us a great deal about the human mind if we pay them the attention they are due. Filled with light-bulb moments and a healthy dose of levity, The Science of Weird Shit is a clever, memorable, and gratifying read you won’t soon forget.

A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.

The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.

Scientists must decode a message from intelligent beings in outer space in this classic science fiction tale by the legendary author of Solaris.“The universe is still struggling to catch up with the vast creative force that was Stanisław Lem.” —Washington PostBy pure chance, scientists detect a signal from space that may be communication from rational beings. How can people of Earth understand this message, knowing nothing about the senders—including whether or not they even exist? Written as the memoir of a mathematician who participates in the government project (code name: His Master’s Voice) attempting to decode what seems to be a message from outer space, this classic novel shows scientists grappling with fundamental questions about the nature of reality, the confines of knowledge, the limitations of the human mind, and the ethics of military-sponsored scientific research.

An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion modelsShort, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of modelsStreamlined presentation separates critical ideas from background context and extraneous detailMinimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks 

An exploration of the scientific limits of knowledge that challenges our deep-seated beliefs about our universe, our rationality, and ourselves.Many books explain what is known about the universe. This book investigates what cannot be known. Rather than exploring the amazing facts that science, mathematics, and reason have revealed to us, this work studies what science, mathematics, and reason tell us cannot be revealed. In The Outer Limits of Reason, Noson Yanofsky considers what cannot be predicted, described, or known, and what will never be understood. He discusses the limitations of computers, physics, logic, and our own thought processes.Yanofsky describes simple tasks that would take computers trillions of centuries to complete and other problems that computers can never solve; perfectly formed English sentences that make no sense; different levels of infinity; the bizarre world of the quantum; the relevance of relativity theory; the causes of chaos theory; math problems that cannot be solved by normal means; and statements that are true but cannot be proven. He explains the limitations of our intuitions about the world—our ideas about space, time, and motion, and the complex relationship between the knower and the known.Moving from the concrete to the abstract, from problems of everyday language to straightforward philosophical questions to the formalities of physics and mathematics, Yanofsky demonstrates a myriad of unsolvable problems and paradoxes. Exploring the various limitations of our knowledge, he shows that many of these limitations have a similar pattern and that by investigating these patterns, we can better understand the structure and limitations of reason itself. Yanofsky even attempts to look beyond the borders of reason to see what, if anything, is out there.

Understanding games--whether computer games, card games, board games, or sports--by analyzing certain common traits.Characteristics of Games offers a new way to understand games: by focusing on certain traits--including number of players, rules, degrees of luck and skill needed, and reward/effort ratio--and using these characteristics as basic points of comparison and analysis. These issues are often discussed by game players and designers but seldom written about in any formal way. This book fills that gap. By emphasizing these player-centric basic concepts, the book provides a framework for game analysis from the viewpoint of a game designer. The book shows what all genres of games--board games, card games, computer games, and sports--have to teach each other. Today's game designers may find solutions to design problems when they look at classic games that have evolved over years of playing.

The follow-up volume to Atlas of Perfumed Botany continues the exploration of nature’s most captivating scents, focusing on the botanical elements that shape the world of perfumery.This volume presents a selection of woods, leaves, flowers, fruits, gums, resins, seeds, and roots, each contributing unique aromatic qualities. From the deep, rich notes of rosewood and oud to the fresh, green scents of mint and rosemary, this book by Jean-Claude Ellena, the ‘‘nose’’ for Hermès for more than a decade, unveils the diverse origins of fragrance ingredients.Illustrated with vibrant images, this atlas serves as an essential guide for both fragrance enthusiasts and professionals, offering insights into the natural sources of perfumery’s most treasured aromas.

A breathtakingly elegant visual dictionary of 2000 Japanese words for rain, with 100 drawings in indigo.In Water of the Sky, artist Miya Ando offers us a beautifully rich, bilingual visual dictionary for rain. Through a collection of 2,000 Japanese words, their English interpretations, and 100 drawings, Ando describes the breadth and diversity of rain’s many expressions: when it falls, how it falls, and how its observer might be transformed physically or emotionally by its presence. The words range from prosaic to esoteric, extending from the meteorological (mukaame, or “very fine rain that falls in spring”) to the mystical (bunryūu, or “rain that splits a dragon's body in half”) and from the minute (kisame, or “raindrops that fall off the leaves and branches of trees”) to the vast (takuu, or “blessed rain that quenches all things in the universe”).Ando’s visual interpretations of these terms are not so much illustrations as evocations, attempts to embody or imagine each rain’s precise and essential quality. The book presents 100 of these 2,000 drawings of rain accompanied by the full index of 2,000 Japanese words and their approximate English equivalents, presented alphabetically using a hybrid Japanese-English alphabetization rubric.

What intelligence really is, and how AI’s emergence is a natural consequence of evolution. Included in the Financial Times's Best Books of 2025: TechnologyIncluded in Bloomberg News's "The 82 Books That Top Business Leaders Couldn’t Put Down"2026 PROSE Award Winner: Engineering and TechnologyIt has come as a shock to some AI researchers that a large neural net that predicts next words seems to produce a system with general intelligence. Yet this is consistent with a long-held view among some neuroscientists that the brain evolved precisely to predict the future—the “predictive brain” hypothesis.In What Is Intelligence?, Blaise Agüera y Arcas takes up this idea—that prediction is fundamental not only to intelligence and the brain but to life itself—and explores the wide-ranging implications. These include radical new perspectives on the computational properties of living systems, the evolutionary and social origins of intelligence, the relationship between models and reality, entropy and the nature of time, the meaning of free will, the problem of consciousness, and the ethics of machine intelligence.The book offers a unified picture of intelligence from molecules to organisms, societies, and AI, drawing from a wide array of literature in many fields, including computer science and machine learning, biology, physics, and neuroscience. It also adds recent and novel findings from the author, his research team, and colleagues. Combining technical rigor and deep up-to-the-minute knowledge about AI development, the natural sciences (especially neuroscience), and philosophical literacy, What Is Intelligence? argues—quite against the grain—that certain modern AI systems do indeed have a claim to intelligence, consciousness, and free will.

NOT JUST FOR WEIGHT LOSS: A neuroscientist explores the science and history of intermittent fasting, revealing the wide-ranging mental and physical benefits of this time-tested eating pattern.Most of us eat 3 meals a day with a smattering of snacks because we think that’s the normal, healthy way to eat. But when we look at the eating patterns of our distant ancestors, we can see that an intermittent fasting eating pattern is normal—and eating 3 meals a day is not. In The Intermittent Fasting Revolution, prominent neuroscientist Mark Mattson shows that frequent periods of time with little or negligible amounts of food is not only normal but also good for us. He describes the specific ways intermittent fasting can: • Enhance our ability to cope with stress by making cells more resilient• Improve mental and physical performance• Slow aging and reduce the risk of diseases like obesity, Alzheimer’s, and diabetesMattson—whose pioneering research uncovered the ways that the brain responds to fasting and exercise—explains how thriving while fasting became an evolutionary adaptation; it’s not just the latest fad diet for weight loss. He also offers practical advice on adopting an intermittent fasting eating pattern as well as information for parents and physicians.

FOR NON-EXPERTS: Get an accessible introduction to quantum computing as a mathematician explains quantum algorithms, quantum entanglement, and more. Quantum computing is a beautiful fusion of quantum physics and computer science! Quantum computing incorporates some of the most stunning ideas from 20th-century physics into an entirely new way of thinking about computation. Here, Chris Bernhardt offers an introduction to quantum computing that is accessible to anyone comfortable with high school mathematics. A mathematician himself, Bernhardt simplifies the mathematics and provides elementary examples that illustrate both how the math works and what it means. He explains for the non-expert:  • Quantum bits, or qubits—the basic unit of quantum computing• Quantum entanglement and what it means when qubits are entangled• Quantum cryptography• Classical computing topics like bits, gates, and logic• Quantum gates• Quantum algorithms and their speed• Quantum computers and how they’re built• And more!  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.

A cartography of fragrance that charts the botany and geography of perfume composition.For perfume makers, each smell carries with it a multitude of associations and impressions that must be carefully analyzed and understood before the sum of all its parts emerges. All perfumers have their own idiosyncratic methods, drawn from their individual olfactory experiences, for classifying fragrances. In Atlas of Perfumed Botany, virtuoso perfumer Jean-Claude Ellena leads readers on a poetic, geographic, and botanical journey of perfume discovery. Ellena offers a varied and fascinating cartography of fragrances, tracing historical connections and cultural exchanges. Full-page entries on plants ranging from bergamot to lavender are accompanied by detailed and vivid full-color botanical illustrations. Readers interested in this book will also be interested in the follow-up book, Atlas of Botanical Fragrance.

A fresh, groundbreaking analysis of renowned Renaissance architect Leon Battista Alberti’s five built works, suggesting a new relationship of form to meaning.Much has been written about Renaissance architect Leon Battista Alberti’s mantra of part-to-whole as one of the continuing conditions of architecture. While this underlying thesis has often been repeated in the annals of architectural history and theory, architects have rarely questioned the idea. In Rewriting Alberti, architect Peter Eisenman suggests, however, that Alberti provoked a radical discourse beyond the part-to-whole dialogue featured in his Ten Books of Architecture. Eisenman’s in-depth analysis of Alberti’s five built works reveals a disjunction between the architect’s buildings and theoretical writings, suggesting a new relationship of form to meaning based on the fragmentation of homogeneous space.Rewriting Alberti includes contributions by Pier Vittorio Aureli, Mario Carpo, and Daniel Sherer. Carpo, an architectural historian and critic, theorizes that Alberti’s work initiated an idea of the discipline as a notational system akin to contemporary computational logics. By way of comparison, Sherer, an architectural historian, reconsiders critic Manfredo Tafuri’s readings of Alberti, and architect and theorist Aureli draws on Alberti to propose another idea of the architectural “project.”Here, in one book are four different discourses (and more than 60 drawings) that look back at the origins of architectural signs and semiology and forward to understand the way that history informs architecture today.

A modern classic about how people really make decisions: drawing on prior experience, using a combination of intuition and analysis.Since its publication twenty years ago, Sources of Power has been enormously influential. The book has sold more than 50,000 copies, has been translated into six languages, has been cited in professional journals that range from Journal of Marketing Research to Journal of Nursing, and is mentioned by Malcolm Gladwell in Blink. Author Gary Klein has collaborated with Nobel laureate Daniel Kahneman and served on a team that redesigned the White House Situation Room to support more effective decision making. The model of decision making Klein proposes in the book has been adopted in fields including law enforcement training and petrochemical plant operation. What is the groundbreaking new way to approach decision making described in this modern classic? We have all seen images of firefighters rescuing people from burning buildings and paramedics treating bombing victims. How do these individuals make the split-second decisions that save lives? Most studies of decision making, based on artificial tasks assigned in laboratory settings, view people as biased and unskilled. Klein proposes a naturalistic approach to decision making, which views people as gaining experience that enables them to use a combination of intuition and analysis to make decisions. To illustrate this approach, Klein tells stories of people—from pilots to chess masters—acting under such real-life constraints as time pressure, high stakes, personal responsibility, and shifting conditions.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. 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.

When problem solving doesn’t work anymore, we need a different logic: start with the underlying human values, reframe, and create space for deep change.The nature of the problems we are facing has shifted, becoming more open, complex, dynamic, and networked than ever before. As a consequence, domains across society are finding out the hard way that “business as usual” doesn’t work anymore and that our current modes of innovation fall far short of what is needed. Our current reality truly requires us to change our thinking on a deep level, to embrace a different logic, and develop new approaches that go beyond mere problem solving. In Deep Change, Kees Dorst shows how this can be done.Building on his bestseller Frame Innovation, Dorst takes creative practices from design to a wholly different level. He shows how these practices can be used to inform organizational change (by synchronizing top-down and bottom-up reframing), sector-level change (by creating and adopting new paradigms), and even societal change (by shaping major transitions). All these practices come together in his studio model, a new type of innovation infrastructure that creates the space for deep and ongoing change.With 24+ fascinating case studies and many other real-world examples, the book takes the reader through the new thinking and the new practices, inspiring growth and change. Once you see this, you can’t unsee it.

From the New York Times–bestselling author of The Hacker's Mind and Data & Goliath, an informative and wide-ranging exploration of how AI will alter every facet of democracy, and how to harness the technology to distribute rather than concentrate power.AI is changing democracy. We still get to decide how.AI’s impact on democracy will go far beyond headline-grabbing political deepfakes and automated misinformation. Everywhere it will be used, it will create risks and opportunities to shake up long-standing power structures.In this highly readable and advisedly optimistic book, Rewiring Democracy, security technologist Bruce Schneier and data scientist Nathan Sanders cut through the AI hype and examine the myriad ways that AI is transforming every aspect of democracy—for both good and ill.The authors describe how the sophistication of AI will fulfill demands from lawmakers for more complex legislation, reducing deference to the executive branch and altering the balance of power between lawmakers and administrators. They show how the scale and scope of AI is enhancing civil servants’ ability to shape private-sector behavior, automating either the enforcement or neglect of industry regulations. They also explain how both lawyers and judges will leverage the speed of AI, upending how we think about law enforcement, litigation, and dispute resolution.Whether these outcomes enhance or degrade democracy depends on how we shape the development and use of AI technologies. Powerful players in private industry and public life are already using AI to increase their influence, and AIs built by corporations don’t deliver the fairness and trust required by democratic governance. But, steered in the right direction, AI’s broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures.Democracy is facing new challenges worldwide, and AI has become a part of that. It can inform, empower, and engage citizens. It can also disinform, disempower, and disengage them. The choice is up to us. Schneier and Sanders blaze the path forward, showing us how we can use AI to make democracy stronger and more participatory.

A thought-provoking argument that consciousness—more widespread than previously assumed—is the feeling of being alive, not a type of computation or a clever hack  In The Feeling of Life Itself, Christof Koch offers a straightforward definition of consciousness as any subjective experience, from the most mundane to the most exalted—the feeling of being alive. Psychologists study which cognitive operations underpin a given conscious perception. Neuroscientists track the neural correlates of consciousness in the brain, the organ of the mind. But why the brain and not, say, the liver? How can the brain—three pounds of highly excitable matter, a piece of furniture in the universe, subject to the same laws of physics as any other piece—give rise to subjective experience? Koch argues that what is needed to answer these questions is a quantitative theory that starts with experience and proceeds to the brain. In The Feeling of Life Itself, Koch outlines such a theory, based on integrated information.  Koch describes how the theory explains many facts about the neurology of consciousness and how it has been used to build a clinically useful consciousness meter. The theory predicts that many, and perhaps all, animals experience the sights and sounds of life; consciousness is much more widespread than conventionally assumed. Contrary to received wisdom, however, Koch argues that programmable computers will not have consciousness. Even a perfect software model of the brain is not conscious. Its simulation is fake consciousness. Consciousness is not a special type of computation—it is not a clever hack. Consciousness is about being.