Experts from disciplines that range from computer science to philosophy consider the challenges of building AI systems that humans can trust. Artificial intelligence-based algorithms now marshal an astonishing range of our daily activities, from driving a car ("turn left in 400 yards") to making a purchase ("products recommended for you"). How can we design AI technologies that humans can trust, especially in such areas of application as law enforcement and the recruitment and hiring process? In this volume, experts from a range of disciplines discuss the ethical and social implications of the proliferation of AI systems, considering bias, transparency, and other issues. The contributors, offering perspectives from computer science, engineering, law, and philosophy, first lay out the terms of the discussion, considering the "ethical debts" of AI systems, the evolution of the AI field, and the problems of trust and trustworthiness in the context of AI. They go on to discuss specific ethical issues and present case studies of such applications as medicine and robotics, inviting us to shift the focus from the perspective of a "human-centered AI" to that of an "AI-decentered humanity." Finally, they consider the future of AI, arguing that, as we move toward a hybrid society of cohabiting humans and machines, AI technologies can become humanity's allies.

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.

One of the most persistent concerns about the future is whether it will be dominated by the predictive algorithms of AI – and, if so, what this will mean for our behaviour, for our institutions and for what it means to be human. AI changes our experience of time and the future and challenges our identities, yet we are blinded by its efficiency and fail to understand how it affects us. At the heart of our trust in AI lies a paradox: we leverage AI to increase our control over the future and uncertainty, while at the same time the performativity of AI, the power it has to make us act in the ways it predicts, reduces our agency over the future. This happens when we forget that that we humans have created the digital technologies to which we attribute agency. These developments also challenge the narrative of progress, which played such a central role in modernity and is based on the hubris of total control. We are now moving into an era where this control is limited as AI monitors our actions, posing the threat of surveillance, but also offering the opportunity to reappropriate control and transform it into care. As we try to adjust to a world in which algorithms, robots and avatars play an ever-increasing role, we need to understand better the limitations of AI and how their predictions affect our agency, while at the same time having the courage to embrace the uncertainty of the future.

Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better.

This book presents cutting-edge concepts on the question of trust. Written by leading experts, it investigates a paradoxical feature of contemporary society: while information and communication technologies, on the one hand, and scientific discourses, on the other, can promote more informed participation in public and democratic life, they have also led to a dramatic decline in our communicative and cooperative skills. The book analyzes the notion of trust from an interdisciplinary perspective by combining the normative (continental) and empirical (Anglo-American) approaches and by considering the political, epistemological, and historical transformations in the interpersonal relationships sparked by new technologies. Using trust as a model, it then investigates and clarifies the new types of participation that are made possible by scientific and technological advances.

There’s nothing like the smell and taste of fresh homemade bread. But who has the time to make it anymore? You do—with a little help from your automatic bread machine. All bread machines can make good bread; they just need a little help from you to turn out a good loaf. With a little practice and a lot of fun, you too can make freshly baked bread in your kitchen with the touch of a button. Bread Machines For Dummies is for anyone who has ever been frustrated by a bread machine and wants to know if it’s really possible to turn out great bread with a minimum of time and effort (it is!). This fun and easy guide shares simple techniques and more than 85 tested, foolproof recipes for making aromatic and flavorful breads—either for your bread machine or from dough that you shape yourself and bake in the oven. You’ll see how to make: Soft white bread Cracked wheat bread Basic danish dough Babka and C hallah Bread bowls Bread sticks, pizza, and focaccia And so much more! This handy resource guide provides everything you “knead” to know about making bread, including the best ingredients to use, how to work with dough, and how to get the best results out of your machine. Along with plenty of cooking, measuring, and shopping tips, you get expert advice on how to: Shape simple doughs into beautiful breads Mix flours and liquids for perfect bread texture Adapt machine recipes for two loaf sizes Understand the different wheat flours Fit bread into a gluten-free diet Avoid moisture mistakes Make breads with alternative ingredients such as rice flour, potato starch, and tapioca flour Featuring a cheat sheet with standard measuring equivalents and temperature conversions, tips for troubleshooting your machine, and delicious recipes for such tasty delights as Cheddar Cheese Corn Bread, Pecan Sticky Rolls, Cranberry Nut Bread, and Banana Lemon Loaf, Bread Machines For Dummies reveals the best ways to bake, store, and enjoy your bread!

Recent technological and scientific developments have demonstrated a condition that has already long been upon us. We have entered a posthuman era, an assertion shared by an increasing number of thinkers such as N. Katherine Hayles, Rosi Braidotti, Donna Haraway, Bruno Latour, Richard Grusin, and Bernard Stiegler. The performing arts have reacted to these developments by increasingly opening up their traditionally human domain to non-human others. Both philosophy and performing arts thus question what it means to be human from a posthumanist point of view and how the agency of non-humans be they technology, objects, animals, or other forms of being works on both an ontological and performative level. The contributions in this volume brings together scholars, dramaturgs, and artists, uniting their reflections on the consequences of the posthuman condition for creative practices, spectatorship, and knowledge.

Social machines are a type of network connected by interactive digital devices made possible by the ubiquitous adoption of technologies such as the Internet, the smartphone, social media and the read/write World Wide Web, connecting people at scale to document situations, cooperate on tasks, exchange information, or even simply to play. Existing social processes may be scaled up, and new social processes enabled, to solve problems, augment reality, create new sources of value, and disrupt existing practice. This book considers what talents one would need to understand or build a social machine, describes the state of the art, and speculates on the future, from the perspective of the EPSRC project SOCIAM – The Theory and Practice of Social Machines. The aim is to develop a set of tools and techniques for investigating, constructing and facilitating social machines, to enable us to narrow down pragmatically what is becoming a wide space, by asking ‘when will it be valuable to use these methods on a sociotechnical system?’ The systems for which the use of these methods adds value are social machines in which there is rich person-to-person communication, and where a large proportion of the machine’s behaviour is constituted by human interaction.

This book is a history of artificial intelligence, that audacious effort to duplicate in an artifact what we consider to be our most important property—our intelligence. It is an invitation for anybody with an interest in the future of the human race to participate in the inquiry.