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Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification.

Robots may one day rule the world, but what is a robot-ruled Earth like? Many think the first truly smart robots will be brain emulations or ems. Scan a human brain, then run a model with the same connections on a fast computer, and you have a robot brain, but recognizably human. Train an em to do some job and copy it a million times: an army of workers is at your disposal. When they can be made cheaply, within perhaps a century, ems will displace humans in most jobs. In this new economic era, the world economy may double in size every few weeks. Some say we can't know the future, especially following such a disruptive new technology, but Professor Robin Hanson sets out to prove them wrong. Applying decades of expertise in physics, computer science, and economics, he uses standard theories to paint a detailed picture of a world dominated by ems. While human lives don't change greatly in the em era, em lives are as different from ours as our lives are from those of our farmer and forager ancestors. Ems make us question common assumptions of moral progress, because they reject many of the values we hold dear. Read about em mind speeds, body sizes, job training and career paths, energy use and cooling infrastructure, virtual reality, aging and retirement, death and immortality, security, wealth inequality, religion, teleportation, identity, cities, politics, law, war, status, friendship and love. This book shows you just how strange your descendants may be, though ems are no stranger than we would appear to our ancestors. To most ems, it seems good to be an em.

We live in an incredible period in history. The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before. But our love of computers should not cloud our thinking about their limitations. We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are useless in judging whether the unearthed patterns are sensible because computers do not think the way humans think. We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions for us. The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

This book examines new forms of nascent life that emerge through technical interactions within human-constructed environments—“machinic life”—in the sciences of cybernetics, artificial life, and artificial intelligence. With the development of such research initiatives as the evolution of digital organisms, computer immune systems, artificial protocells, evolutionary robotics, and swarm systems, it argues, machinic life has achieved a complexity and autonomy worthy of study in its own right. Drawing on the publications of scientists as well as a range of work in contemporary philosophy and cultural theory, but always with the primary focus on the “objects at hand”—the machines, programs, and processes that constitute machinic life—the book shows how they come about, how they operate, and how they are already changing. This understanding is a necessary first step, it further argues, that must precede speculation about the meaning and cultural implications of these new forms of life. Developing the concept of the “computational assemblage” (a machine and its associated discourse) as a framework to identify both resemblances and differences in form and function, the book offers a conceptual history of each of the three sciences. It considers the new theory of machines proposed by cybernetics from several perspectives, including Lacanian psychoanalysis and “machinic philosophy.” The book examines the history of the new science of artificial life and its relation to theories of evolution, emergence, and complex adaptive systems (as illustrated by a series of experiments carried out on various software platforms).

This book addresses the connections between computers, life, evolution, brains, and minds. Digital computers are recent and have changed our society. However, they represent just the latest way to process information, using algorithms to create order out of chaos. Before computers, the job of processing information was done by living organisms, which are nothing more than complex information processing devices, shaped by billions of years of evolution. The most advanced of these information processing devices is the human brain. Brains enable humans to process information in a way unparalleled by any other species, living or extinct, or by any existing machine. They provide humans with intelligence, consciousness and, some believe, even with a soul. Brains also enabled humans to develop science and technology to a point where it is possible to design computers with a power comparable to that of the human brain. Machine learning and artificial intelligence technologies will one day make it possible to create intelligent machines and computational biology will one day enable us to model, simulate, and understand biological systems and even complete brains, with unprecedented levels of detail. From these efforts, new minds will eventually emerge, minds that will emanate from the execution of programs running in powerful computers. These digital minds may one day rival our own, become our partners, and replace humans in many tasks. They may usher in a technological singularity, may make humans obsolete or even a threatened species. They make us super-humans or demi-gods.

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. This book introduces techniques for formalizing deductive argumentation in artificial intelligence, emphasizing emerging formalizations for practical argumentation. It discusses 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. The book elucidates and formalizes key elements of deductive argumentation.

For many decades, the proponents of `artificial intelligence' have maintained that computers will soon be able to do everything that a human can do. In his bestselling work of popular science, Sir Roger Penrose takes us on a fascinating tour through the basic principles of physics, cosmology, mathematics, and philosophy to show that human thinking can never be emulated by a machine. Oxford Landmark Science books are 'must-read' classics of modern science writing which have crystallized big ideas, and shaped the way we think.

Logic is now widely recognized as one of the foundational disciplines of computing and has applications in virtually all aspects of the subject, from software engineering and hardware to programming languages and artificial intelligence. The Handbook of Logic in Artificial Intelligence and its companion The Handbook of Logic in Computer Science were created in response to the growing need for an in-depth survey of these applications. This handbook comprises five volumes, each an in-depth overview of one of the major topics in this area. The result of years of cooperative effort by internationally renowned researchers, it will be the standard reference work in AI for years to come. Volume 5 focuses on logic programming. The chapters, which in many cases are of monograph length and scope, emphasize possible unifying themes.

In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s–2030s), and is being actively investigated in the US, Europe and China. The EPSRC’s UK network on Human-Like Computing (HLC) was one of the first internationally to initiate and support research specifically in this area. Starting activities in 2018, the network represents around sixty leading UK groups Artificial Intelligence and Cognitive Scientists involved in the development of the inter-disciplinary area of HLC. The research of network groups aims to address key unsolved problems at the interface between Psychology and Computer Science. The chapters of this book have been authored by a mixture of these UK and other international specialists based on recent workshops and discussions at the Machine Intelligence 20 and 21 workshops (2016,2019) and the Third Wave Artificial Intelligence workshop (2019). Some of the key questions addressed by the Human-Like Computing programme include how AI systems might 1) explain their decisions effectively, 2) interact with human beings in natural language, 3) learn from small numbers of examples and 4) learn with minimal supervision. Solving such fundamental problems involves new foundational research in both the Psychology of perception and interaction as well as the development of novel algorithmic approaches in Artificial Intelligence.

As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of "Web intelligence", as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected. Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

The idea of intelligent machines has become part of popular culture. But tracing the history of the actual science of machine intelligence reveals a rich network of cross-disciplinary contributions—the unrecognized origins of ideas now central to artificial intelligence, artificial life, cognitive science, and neuroscience. In this book, scientists, artists, historians, and philosophers discuss the multidisciplinary quest to formalize and understand the generation of intelligent behavior in natural and artificial systems as a wholly mechanical process. The chapters illustrate the diverse and interacting notions that chart the evolution of the idea of the mechanical mind. They describe the mechanized mind as, among other things, an analogue system, an organized suite of chemical interactions, a self-organizing electromechanical device, an automated general-purpose information processor, and an integrated collection of symbol-manipulating mechanisms. The chapters investigate the views of pivotal figures that range from Descartes and Heidegger to Alan Turing and Charles Babbage, and emphasize such frequently overlooked areas as British cybernetic and pre-cybernetic thinkers. The book concludes with the personal insights of five highly influential figures in the field: John Maynard Smith, John Holland, Oliver Selfridge, Horace Barlow, and Jack Cowan.

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. This book offers a variety of perspectives—drawn from philosophy, cognitive psychology, and computer science—on reasoning about the reasoning process. It 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.

In this book Pollock deals with the subject of probabilistic reasoning, making general philosophical sense of objective probabilities and exploring their relationship to the problem of induction. He argues that probability is fundamental not only to physical science, but to induction, epistemology, the philosophy of science and much of the reasoning relevant to artificial intelligence. Pollock’s main claim is that the fundamental notion of probability is nomic--that is, it involves the notion of natural law, valid across possible worlds. The various epistemic and statistical conceptions of probability, he demonstrates, are derived from this nomic notion. He goes on to provide a theory of statistical induction, an account of computational principles allowing some probabilities to be derived from others, an account of acceptance rules, and a theory of direct inference.

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games. The material is presented with minimal technicalities and is accessible to undergraduate students with no specialized knowledge or technical background beyond high school mathematics. Students use Prolog, learning to express what they need as a Prolog program and letting Prolog search for answers. After an introduction to the basic concepts, the book offers three chapters on Prolog, covering back-chaining, programs and queries, and how to write the sorts of Prolog programs used in the book. The book follows this with case studies of tasks that appear to require thought, then looks beyond Prolog to consider learning, explaining, and propositional reasoning. Most of the chapters conclude with short bibliographic notes and exercises.