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A novel approach to hybrid AI aimed at developing trustworthy agent collaborators.The vast majority of current AI relies wholly on machine learning (ML). However, the past thirty years of effort in this paradigm have shown that, despite the many things that ML can achieve, it is not an all-purpose solution to building human-like intelligent systems. One hope for overcoming this limitation is hybrid AI: that is, AI that combines ML with knowledge-based processing. In Agents in the Long Game of AI, Marjorie McShane, Sergei Nirenburg, and Jesse English present recent advances in hybrid AI with special emphases on content-centric computational cognitive modeling, explainability, and development methodologies. At present, hybridization typically involves sprinkling knowledge into an ML black box. The authors, by contrast, argue that hybridization will be best achieved in the opposite way: by building agents within a cognitive architecture and then integrating judiciously selected ML results. This approach leverages the power of ML without sacrificing the kind of explainability that will foster society's trust in AI. This book shows how we can develop trustworthy agent collaborators of a type not being addressed by the “ML alone” or “ML sprinkled by knowledge” paradigms—and why it is imperative to do so.

An expansive case for bibliography as infrastructure in information science.Cats, Carpenters, and Accountants argues that bibliography serves a foundational role within information science as infrastructure, and like all infrastructures, it needs and deserves attention. Wayne de Fremery's thoughtful provocation positions bibliography as a means to serve the many ends pursued by information scientists. He explains that bibliographic practices, such as enumeration and description, lie at the heart of knowledge practices and cultural endeavors, but these kinds of infrastructures are difficult to see. In this book, he reveals them and the ways that they formulate information and meaning, artificial intelligence, and human knowledge.Drawing on scholarship from areas as diverse as data science, machine learning, Korean poetry, and the history of bibliography, de Fremery makes the case for understanding bibliography as a generative mode of accounting for what has been received as data, what he calls “carpentry-accounting.” Referencing a well-known debate in the Anglo-American bibliographical tradition that features a willful cat, he suggests that bibliography and bibliographers are intentionally marginal figures who, paradoxically, perform foundational work in the service of the diverse disciplinary ends that formulate, however loosely, information science as a field. When we attend to the marginal but essential work of accounting for what humankind has fashioned as recorded knowledge, it becomes easier to consider the ways that human accounts can serve and, sometimes, injure us. Relevant to scholars and students from the sciences to the humanities, Cats, Carpenters, and Accountants is a highly original argument for bibliography as a marginal but foundationally powerful force shaping information science as a field and the ways that we know.

The essential reader on the philosophical foundations and implications of artificial intelligence, now comprehensively updated for the twenty-first century.In the quarter century since the publication of John Haugeland's Mind Design II, computer scientists have hit many of their objectives for successful artificial intelligence. Computers beat chess grandmasters, driverless cars navigate streets, autonomous robots vacuum our homes, and ChatGPT answers existential queries in iambic pentameter on command. Engineering has made incredible strides. But have we made progress in understanding and building minds? Comprehensively updated by Carl Craver and Colin Klein to reflect the astonishing ubiquity of machine learning in modern life, Mind Design III offers an essential collection of classic and contemporary essays on the philosophical foundations and implications of artificial intelligence. Contributions from a diverse range of philosophers and computer scientists address the nature of computation, the nature of thought, and the question of whether computers can be made to think. With extensive new material reflecting the explosive growth and diversification of AI approaches, this classic reader equips students to assess the possibility of, and progress toward, building minds out of computers.New edition highlights: New chapters on advances in deep neural networks, reinforcement learning, and causal learningNew material on the complementary intersection of neuroscience and AIOrganized thematically rather than chronologically Brand new introductions to each section that include suggestions for coursework and further reading

A provocative investigation of the future of photography and human perception in the age of AI.We are constantly photographing and being photographed while feeding machine learning databases with our data, which in turn is used to generate new images. Analyzing the transformation of photography by computation—and the transformation of human perception by algorithmically driven images, from CGI to AI—The Perception Machine investigates what it means for us to live surrounded by image flows and machine eyes. In an astute and engaging argument, Joanna Zylinska brings together media theory and neuroscience in a Vilém Flusser–Paul Virilio remix. Her “perception machine” names a technical universe of images and their infrastructures. But it also refers to a sociopolitical condition resulting from today's automation of vision, imaging—and imagination.Written by a theorist-practitioner, the book incorporates Zylinska's own art projects, some of which have been co-created with AI. The photographs, collages, films, and installations available as part of the book (and its companion website) provide a different mode of thinking about our technological futures, at a local as well as a planetary level. Offering provocative concepts such as eco-eco-punk, AUTO-FOTO-KINO, planetary micro-vision, loser images, and sensography, the book outlines an existential philosophy of messy media for a time when our practices of imaging and self-imaging are being radically redesigned. Importantly, it also offers a new vision of our future.

A new translation of Karel Čapek's play R.U.R.—which famously coined the term “robot”—and a collection of essays reflecting on the play's legacy from scientists and scholars who work in artificial life and robotics.Karel Čapek's “R.U.R.” and the Vision of Artificial Life offers a new, highly faithful translation by Štěpán Šimek of Czech novelist, playwright, and critic Karel Čapek's play R.U.R.: Rossum's Universal Robots, as well as twenty essays from contemporary writers on the 1920 play. R.U.R. is perhaps best known for first coining the term “robot” (in Czech, robota means serfdom or arduous drudgery). The twenty essays in this new English edition, beautifully edited by Jitka Čejková, are selected from Robot 100, an edited collection in Czech with perspectives from 100 contemporary voices that was published in 2020 to celebrate the hundredth anniversary of the play.Čapek's robots were autonomous beings, but biological, not mechanical, made of chemically synthesized soft matter resembling living tissue, like the synthetic humans in Blade Runner, Westworld, or Ex Machina. The contributors to the collection—scientists and other scholars—explore the legacy of the play and its connections to the current state of research in artificial life, or ALife. Throughout the book, it is impossible to ignore Čapek's prescience, as his century-old science fiction play raises contemporary questions with respect to robotics, synthetic biology, technology, artificial life, and artificial intelligence, anticipating many of the formidable challenges we face today.ContributorsJitka Čejková, Miguel Aguilera, Iñigo R. Arandia, Josh Bongard, Julyan Cartwright, Seth Bullock, Dominique Chen, Gusz Eiben, Tom Froese, Carlos Gershenson, Inman Harvey, Jana Horáková, Takashi Ikegami, Sina Khajehabdollahi, George Musser, Geoff Nitschke, Julie Nováková, Antoine Pasquali, Hemma Philamore, Lana Sinapayen, Hiroki Sayama, Nathaniel Virgo, Olaf Witkowski

AI is revolutionizing the world. Here's how democracies can come out on top.Artificial intelligence is revolutionizing the modern world. It is ubiquitous—in our homes and offices, in the present and most certainly in the future. Today, we encounter AI as our distant ancestors once encountered fire. If we manage AI well, it will become a force for good, lighting the way to many transformative inventions. If we deploy it thoughtlessly, it will advance beyond our control. If we wield it for destruction, it will fan the flames of a new kind of war, one that holds democracy in the balance. As AI policy experts Ben Buchanan and Andrew Imbrie show in The New Fire, few choices are more urgent—or more fascinating—than how we harness this technology and for what purpose. The new fire has three sparks: data, algorithms, and computing power. These components fuel viral disinformation campaigns, new hacking tools, and military weapons that once seemed like science fiction. To autocrats, AI offers the prospect of centralized control at home and asymmetric advantages in combat. It is easy to assume that democracies, bound by ethical constraints and disjointed in their approach, will be unable to keep up. But such a dystopia is hardly preordained. Combining an incisive understanding of technology with shrewd geopolitical analysis, Buchanan and Imbrie show how AI can work for democracy. With the right approach, technology need not favor tyranny.

How we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise.It's sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lacks the common sense of a human driver, who would quickly figure out what's happening and find a workaround. In Machines like Us, Ron Brachman and Hector Levesque—both leading experts in AI—consider what it would take to create machines with common sense rather than just the specialized expertise of today's AI systems. Using the stuck traffic light and other relatable examples, Brachman and Levesque offer an accessible account of how common sense might be built into a machine. They analyze common sense in humans, explain how AI over the years has focused mainly on expertise, and suggest ways to endow an AI system with both common sense and effective reasoning. Finally, they consider the critical issue of how we can trust an autonomous machine to make decisions, identifying two fundamental requirements for trustworthy autonomous AI systems: having reasons for doing what they do, and being able to accept advice. Both in the end are dependent on having common sense.

An expert offers a guide to where we should use artificial intelligence—and where we should not.Before we know it, artificial intelligence (AI) will work its way into every corner of our lives, making decisions about, with, and for us. Is this a good thing? There's a tendency to think that machines can be more “objective” than humans—can make better decisions about job applicants, for example, or risk assessments. In Awkward Intelligence, AI expert Katharina Zweig offers readers the inside story, explaining how many levers computer and data scientists must pull for AI's supposedly objective decision making. She presents the good and the bad: AI is good at processing vast quantities of data that humans cannot—but it's bad at making judgments about people. AI is accurate at sifting through billions of websites to offer up the best results for our search queries and it has beaten reigning champions in games of chess and Go. But, drawing on her own research, Zweig shows how inaccurate AI is, for example, at predicting whether someone with a previous conviction will become a repeat offender. It's no better than simple guesswork, and yet it's used to determine people's futures. Zweig introduces readers to the basics of AI and presents a toolkit for designing AI systems. She explains algorithms, big data, and computer intelligence, and how they relate to one another. Finally, she explores the ethics of AI and how we can shape the process. With Awkward Intelligence. Zweig equips us to confront the biggest question concerning AI: where we should use it—and where we should not.

Rethinking design through the lens of embodied cognition provides a novel way of understanding human interaction with technology. In this book, Christopher Baber uses embodied cognition as a lens through which to view both how designers engage in creative practices and how people use designed artifacts. This view of cognition as enactive, embedded, situated, or distributed, without recourse to internal representations, provides a theoretical grounding that makes possible a richer account of human interaction with technology. This understanding of everyday interactions with things in the world reveals opportunities for design to intervene. Moreover, Baber argues, design is an embodied activity in which the continual engagement between designers and their materials is at the heart of design practice. Baber proposes that design and creativity should be considered in dynamic, rather than discrete, terms and explores “task ecologies”—the concept of environment as it relates to embodied cognition. He uses a theory of affordance as an essential premise for design practice, arguing that affordances are neither form nor function but arise from the dynamics within the human-artifact-environment system. Baber explores agency and intent of smart devices and implications of tangible user interfaces and activity recognition for human-computer interaction. He proposes a systems view of human-artifact-environment interactions—to focus on any one component or pairing misses the subtleties of these interactions. The boundaries between components remain, but the borders that allow exchange of information and action are permeable, which gives rise to synergies and interactions.

MIT presents a concise primer on machine learning—computer programs that learn from data and the basis of applications like voice recognition and driverless cars. No in-depth knowledge of math or programming required! 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 every day, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of “the new AI.” This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin explains that as Big Data has grown, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He covers: • The evolution of machine learning• Important learning algorithms and example applications• Using machine learning algorithms for pattern recognition• Artificial neural networks inspired by the human brain• Algorithms that learn associations between instances• Reinforcement learning• Transparency, explainability, and fairness in machine learning• The ethical and legal implicates of data-based decision making A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programming—making it accessible for everyday readers and easily adoptable for classroom syllabi.

Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models.Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field.After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.ContributorsBabak Ahmadi, Hendrik Blockeel, Hung Bui, Yuqiao Chen, Arthur Choi, Jaesik Choi, Adnan Darwiche, Jesse Davis, Rodrigo de Salvo Braz, Pedro Domingos, Daan Fierens, Martin Grohe, Fabian Hadiji, Seyed Mehran Kazemi, Kristian Kersting, Roni Khardon, Angelika Kimmig, Jacek Kisynski, Daniel Lowd, Wannes Meert, Martin Mladenov, Raymond Mooney, Sriraam Natarajan, Mathias Niepert, David Poole, Scott Sanner, Pascal Schweitzer, Nima Taghipour, Guy Van den Broeck

A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems.One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.

Practitioners and scholars explore ethical, social, and conceptual issues arising in relation to such devices as fitness monitors, neural implants, and a toe-controlled computer mouse.Body-centered computing now goes beyond the “wearable” to encompass implants, bionic technology, and ingestible sensors—technologies that point to hybrid bodies and blurred boundaries between human, computer, and artificial intelligence platforms. Such technologies promise to reconfigure the relationship between bodies and their environment, enabling new kinds of physiological interfacing, embodiment, and productivity. Using the term embodied computing to describe these devices, this book offers essays by practitioners and scholars from a variety of disciplines that explore the accompanying ethical, social, and conceptual issues. The contributors examine technologies that range from fitness monitors to neural implants to a toe-controlled mouse. They discuss topics that include the policy implications of ingestibles; the invasive potential of body area networks, which transmit data from bodily devices to the internet; cyborg experiments, linking a human brain directly to a computer; the evolution of the ankle monitor and other intrusive electronic monitoring devices; fashiontech, which offers users an aura of “cool” in exchange for their data; and the “final frontier” of technosupremacism: technologies that seek to read our minds. Taken together, the essays show the importance of considering embodied technologies in their social and political contexts rather than in isolated subjectivity or in purely quantitative terms.ContributorsRoba Abbas, Andrew Iliadis, Gary Genosko, Suneel Jethani, Deborah Lupton, Katina Michael, M. G. Michael, Marcel O'Gorman, Maggie Orth, Isabel Pedersen, Christine Perakslis, Kevin Warwick, Elizabeth Wissinger

Why a new approach is needed in the quest for general artificial intelligence.Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to be kept as pets. And yet, although artificial intelligence has become increasingly sophisticated—with such achievements as driverless cars and humanless chess-playing—computer science has not yet created general artificial intelligence. In Algorithms Are Not Enough, Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent nor likely.Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choices—finding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence.Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes.

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.

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

Experts from a range of disciplines explore how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other.Humans are not limited to a fixed set of innate or preprogrammed tasks. We learn quickly through language and other forms of natural interaction, and we improve our performance and teach others what we have learned. Understanding the mechanisms that underlie the acquisition of new tasks through natural interaction is an ongoing challenge. Advances in artificial intelligence, cognitive science, and robotics are leading us to future systems with human-like capabilities. A huge gap exists, however, between the highly specialized niche capabilities of current machine learning systems and the generality, flexibility, and in situ robustness of human instruction and learning. Drawing on expertise from multiple disciplines, this Strüngmann Forum Report explores how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other.The contributors consider functional knowledge requirements, the ontology of interactive task learning, and the representation of task knowledge at multiple levels of abstraction. They explore natural forms of interactions among humans as well as the use of interaction to teach robots and software agents new tasks in complex, dynamic environments. They discuss research challenges and opportunities, including ethical considerations, and make proposals to further understanding of interactive task learning and create new capabilities in assistive robotics, healthcare, education, training, and gaming.ContributorsTony Belpaeme, Katrien Beuls, Maya Cakmak, Joyce Y. Chai, Franklin Chang, Ropafadzo Denga, Marc Destefano, Mark d'Inverno, Kenneth D. Forbus, Simon Garrod, Kevin A. Gluck, Wayne D. Gray, James Kirk, Kenneth R. Koedinger, Parisa Kordjamshidi, John E. Laird, Christian Lebiere, Stephen C. Levinson, Elena Lieven, John K. Lindstedt, Aaron Mininger, Tom Mitchell, Shiwali Mohan, Ana Paiva, Katerina Pastra, Peter Pirolli, Roussell Rahman, Charles Rich, Katharina J. Rohlfing, Paul S. Rosenbloom, Nele Russwinkel, Dario D. Salvucci, Matthew-Donald D. Sangster, Matthias Scheutz, Julie A. Shah, Candace L. Sidner, Catherine Sibert, Michael Spranger, Luc Steels, Suzanne Stevenson, Terrence C. Stewart, Arthur Still, Andrea Stocco, Niels Taatgen, Andrea L. Thomaz, J. Gregory Trafton, Han L. J. van der Maas, Paul Van Eecke, Kurt VanLehn, Anna-Lisa Vollmer, Janet Wiles, Robert E. Wray III, Matthew Yee-King

THE FUTURE OF GAME DESIGN IN THE AGE OF AI: Can games measure intelligence? And how will artificial intelligence inform games of the future? In Playing Smart, Julian Togelius explores the connections between games and intelligence to offer a new vision of future games and game design. Video games already depend on AI. We use games to test AI algorithms, challenge our thinking, and better understand both natural and artificial intelligence. In the future, Togelius argues, game designers will be able to create smarter games that make us smarter in turn, applying advanced AI to help design games. In this book, he tells us how. Games are the past, present, and future of artificial intelligence. In 1948, Alan Turing, one of the founding fathers of computer science and artificial intelligence, handwrote a program for chess. Today we have IBM's Deep Blue and DeepMind's AlphaGo, and huge efforts go into developing AI that can play such arcade games as Pac-Man. Programmers continue to use games to test and develop AI, creating new benchmarks for AI while also challenging human assumptions and cognitive abilities. Game design is at heart a cognitive science, Togelius reminds us—when we play or design a game, we plan, think spatially, make predictions, move, and assess ourselves and our performance. By studying how we play and design games, Togelius writes, we can better understand how humans and machines think. AI can do more for game design than providing a skillful opponent. We can harness it to build game-playing and game-designing AI agents, enabling a new generation of AI-augmented games. With AI, we can explore new frontiers in learning and play.

Six classic science fiction stories and commentary that illustrate and explain key algorithms or principles of artificial intelligence.This book presents six classic science fiction stories and commentary that illustrate and explain key algorithms or principles of artificial intelligence. Even though all the stories were originally published before 1973, they help readers grapple with two questions that stir debate even today: how are intelligent robots programmed? and what are the limits of autonomous robots? The stories—by Isaac Asimov, Vernor Vinge, Brian Aldiss, and Philip K. Dick—cover telepresence, behavior-based robotics, deliberation, testing, human-robot interaction, the “uncanny valley,” natural language understanding, machine learning, and ethics. Each story is preceded by an introductory note, “As You Read the Story,” and followed by a discussion of its implications, “After You Have Read the Story.” Together with the commentary, the stories offer a nontechnical introduction to robotics. The stories can also be considered as a set of—admittedly fanciful—case studies to be read in conjunction with more serious study.Contents“Stranger in Paradise” by Isaac Asimov, 1973“Runaround” by Isaac Asimov, 1942“Long Shot” by Vernor Vinge, 1972“Catch That Rabbit” by Isaac Asimov, 1944“Super-Toys Last All Summer Long” by Brian Aldiss, 1969“Second Variety” by Philip K. Dick, 1953

Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs.The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart.Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.

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?

A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars.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.

An innovative proposal for understanding how mental organisms make decisions and control behavior.In this book, Whitman Richards offers a novel and provocative proposal for understanding decision making and human behavior. Building on Valentino Braitenberg's famous “vehicles,” Richards describes a collection of mental organisms that he calls “daemons”—virtual correlates of neural modules. Daemons have favored choices and make decisions that control behaviors of the group to which they belong, with each daemon preferring a different outcome. Richards arranges these preferences in graphs, linking similar choices, which thus reinforce each other. “Anigrafs” refers to these two components—animals, or the mental organisms (agents or daemons), and the graphs that show similarity relations. Together these two components are the basis of a new cognitive architecture. In Richards's account, a collection of daemons compete for control of the cognitive system in which they reside; the challenge is to get the daemons to agree on one of many choices. Richards explores the results of group decisions, emphasizing the Condorcet voting procedure for aggregating preferences. A neural mechanism is proposed. Anigrafs presents a series of group decisions that incorporate simple and complex movements, as well as aspects of cognition and belief. Anigrafs concludes with a section on “metagrafs,” which chart relationships between different anigraf models.

A concise introduction to a complex field, bringing together recent work in cognitive science and cognitive robotics to offer a solid grounding on key issues.This book offers a concise and accessible introduction to the emerging field of artificial cognitive systems. Cognition, both natural and artificial, is about anticipating the need for action and developing the capacity to predict the outcome of those actions. Drawing on artificial intelligence, developmental psychology, and cognitive neuroscience, the field of artificial cognitive systems has as its ultimate goal the creation of computer-based systems that can interact with humans and serve society in a variety of ways. This primer brings together recent work in cognitive science and cognitive robotics to offer readers a solid grounding on key issues.The book first develops a working definition of cognitive systems—broad enough to encompass multiple views of the subject and deep enough to help in the formulation of theories and models. It surveys the cognitivist, emergent, and hybrid paradigms of cognitive science and discusses cognitive architectures derived from them. It then turns to the key issues, with chapters devoted to autonomy, embodiment, learning and development, memory and prospection, knowledge and representation, and social cognition. Ideas are introduced in an intuitive, natural order, with an emphasis on the relationships among ideas and building to an overview of the field. The main text is straightforward and succinct; sidenotes drill deeper on specific topics and provide contextual links to further reading.

An updated edition of an authoritative text showing the relevance for philosophy of mind of theoretical and experimental results in the natural sciences.In Matter and Consciousness, Paul Churchland presents a concise and contemporary overview of the philosophical issues surrounding the mind and explains the main theories and philosophical positions that have been proposed to solve them. Making the case for the relevance of theoretical and experimental results in neuroscience, cognitive science, and artificial intelligence for the philosophy of mind, Churchland reviews current developments in the cognitive sciences and offers a clear and accessible account of the connections to philosophy of mind.For this third edition, the text has been updated and revised throughout. The changes range from references to the iPhone's'Siri'to expanded discussions of the work of such contemporary philosophers as David Chalmers, John Searle, and Thomas Nagel. Churchland describes new research in evolution, genetics, and visual neuroscience, among other areas, arguing that the philosophical significance of these new findings lies in the support they tend to give to the reductive and eliminative versions of materialism.Matter and Consciousness, written by the most distinguished theorist and commentator in the field, offers an authoritative summary and sourcebook for issues in philosophy of mind. It is suitable for use as an introductory undergraduate text.

An investigation into the assignment of moral responsibilities and rights to intelligent and autonomous machines of our own making.One of the enduring concerns of moral philosophy is deciding who or what is deserving of ethical consideration. Much recent attention has been devoted to the'animal question'—consideration of the moral status of nonhuman animals. In this book, David Gunkel takes up the'machine question': whether and to what extent intelligent and autonomous machines of our own making can be considered to have legitimate moral responsibilities and any legitimate claim to moral consideration.The machine question poses a fundamental challenge to moral thinking, questioning the traditional philosophical conceptualization of technology as a tool or instrument to be used by human agents. Gunkel begins by addressing the question of machine moral agency: whether a machine might be considered a legitimate moral agent that could be held responsible for decisions and actions. He then approaches the machine question from the other side, considering whether a machine might be a moral patient due legitimate moral consideration. Finally, Gunkel considers some recent innovations in moral philosophy and critical theory that complicate the machine question, deconstructing the binary agent–patient opposition itself.Technological advances may prompt us to wonder if the science fiction of computers and robots whose actions affect their human companions (think of HAL in 2001: A Space Odyssey) could become science fact. Gunkel's argument promises to influence future considerations of ethics, ourselves, and the other entities who inhabit this world.

The definitive presentation of Soar, one AI's most enduring architectures, offering comprehensive descriptions of fundamental aspects and new components. In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components.The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.

A provocative analysis of what it means to be human in an era of incomprehensible technological complexity and change.In The Techno-Human Condition, Braden Allenby and Daniel Sarewitz explore what it means to be human in an era of incomprehensible technological complexity and change. They argue that if we are to have any prospect of managing that complexity, we will need to escape the shackles of current assumptions about rationality, progress, and certainty, even as we maintain a commitment to fundamental human values.Humans have been co-evolving with their technologies since the dawn of prehistory. What is different now is that we have moved beyond external technological interventions to transform ourselves from the inside out—even as we also remake the Earth system itself. Coping with this new reality, say Allenby and Sarewitz, means liberating ourselves from such categories as “human,” “technological,” and “natural” to embrace a new techno-human relationship. ContributorsBoris Barbour, Mario Biagioli, Paul S. Brookes, Finn Brunton, Alex Csiszar, Alessandro Delfanti, Emmanuel Didier, Sarah de Rijcke, Daniele Fanelli, Yves Gingras, James R. Griesemer, Catherine Guaspare, Marie-Andrée Jacob, Barbara M. Kehm, Cyril Labbé, Jennifer Lin, Alexandra Lippman, Burkhard Morganstern, Ivan Oransky, Michael Power, Sergio Sismondo, Brandon Stell, Tereza Stöckelová, Elizabeth Wager, Paul Wouters

The derivation, exposition, and justification of the Selective Tuning model of vision and attention.Although William James declared in 1890,'Everyone knows what attention is,'today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation—rather than simply the language of mathematics—as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis.Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and color; additional color illustrations and movies are available on the book's Web site

Experts report on the latest artificial intelligence research concerning reasoning about reasoning itself. The capacity to think about our own thinking may lie at the heart of what it means to be both human and intelligent. Philosophers and cognitive scientists have investigated these matters for many years. Researchers in artificial intelligence have gone further, attempting to implement actual machines that mimic, simulate, and perhaps even replicate this capacity, called metareasoning. In this volume, leading authorities offer a variety of perspectives—drawn from philosophy, cognitive psychology, and computer science—on reasoning about the reasoning process.The book offers a simple model of reasoning about reason as a framework for its discussions. Following this framework, the contributors consider metalevel control of computational activities, introspective monitoring, distributed metareasoning, and, putting all these aspects of metareasoning together, models of the self. Taken together, the chapters offer an integrated narrative on metareasoning themes from both artificial intelligence and cognitive science perspectives.

Background and techniques for formalizing deductive argumentation in a logic-based framework for artificial intelligence.Logic-based formalizations of argumentation, which assume a set of formulae and then lay out arguments and counterarguments that can be obtained from these formulae, have been refined in recent years in an attempt to capture more closely real-world practical argumentation. In Elements of Argumentation, Philippe Besnard and Anthony Hunter introduce techniques for formalizing deductive argumentation in artificial intelligence, emphasizing emerging formalizations for practical argumentation. Besnard and Hunter discuss how arguments can be constructed, how key intrinsic and extrinsic factors can be identified, and how these analyses can be harnessed for formalizing argumentation for use in real-world problem analysis and decision making.The book focuses on a monological approach to argumentation, in which there is a set of possibly conflicting pieces of information (each represented by a formula) that has been collated by an agent or a pool of agents. The role of argumentation is to construct a collection of arguments and counterarguments pertaining to some particular claim of interest to be used for analysis or presentation.Elements of Argumentation is the first book to elucidate and formalize key elements of deductive argumentation. It will be a valuable reference for researchers in computer science and artificial intelligence and of interest to scholars in such fields as logic, philosophy, linguistics, and cognitive science.

Advances in computational geometry and machine learning that offer new methods for search, regression, and classification with large amounts of high-dimensional data. Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications. The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naïve methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.

The story of the U.S. Department of Defense's extraordinary effort, in the period from 1983 to 1993, to achieve machine intelligence.This is the story of an extraordinary effort by the U.S. Department of Defense to hasten the advent of'machines that think.'From 1983 to 1993, the Defense Advanced Research Projects Agency (DARPA) spent an extra $1 billion on computer research aimed at achieving artificial intelligence. The Strategic Computing Initiative (SCI) was conceived as an integrated plan to promote computer chip design and manufacture, computer architecture, and artificial intelligence software. What distinguished SCI from other large-scale technology programs was that it self-consciously set out to advance an entire research front. The SCI succeeded in fostering significant technological successes, even though it never achieved machine intelligence. The goal provided a powerful organizing principle for a suite of related research programs, but it did not solve the problem of coordinating these programs. In retrospect, it is hard to see how it could have.In Strategic Computing, Alex Roland and Philip Shiman uncover the roles played in the SCI by technology, individuals, and social and political forces. They explore DARPA culture, especially the information processing culture within the agency, and they evaluate the SCI's accomplishments and set them in the context of overall computer development during this period. Their book is an important contribution to our understanding of the complex sources of contemporary computing.