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. (shrink)

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...) “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. (shrink)

In the international bestseller, Thinking, Fast and Slow, Daniel Kahneman, the renowned psychologist and winner of the Nobel Prize in Economics, takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. The impact of overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the profound effect of cognitive (...) biases on everything from playing the stock market to planning our next vacation—each of these can be understood only by knowing how the two systems shape our judgments and decisions. -/- Engaging the reader in a lively conversation about how we think, Kahneman reveals where we can and cannot trust our intuitions and how we can tap into the benefits of slow thinking. He offers practical and enlightening insights into how choices are made in both our business and our personal lives—and how we can use different techniques to guard against the mental glitches that often get us into trouble. Winner of the National Academy of Sciences Best Book Award and the Los Angeles Times Book Prize and selected by The New York Times Book Review as one of the ten best books of 2011, Thinking, Fast and Slow is destined to be a classic. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the (...) ‘what’ of AI ethics —rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. (shrink)

We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that (...) at least some regulatory proposals for explainable AI could end up setting the bar higher than is necessary or indeed helpful. The demands of practical reason require the justification of action to be pitched at the level of practical reason. Decision tools that support or supplant practical reasoning should not be expected to aim higher than this. We cast this desideratum in terms of Daniel Dennett’s theory of the “intentional stance” and argue that since the justification of action for human purposes takes the form of intentional stance explanation, the justification of algorithmic decisions should take the same form. In practice, this means that the sorts of explanations for algorithmic decisions that are analogous to intentional stance explanations should be preferred over ones that aim at the architectural innards of a decision tool. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...) methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a “black box problem.” That is, machine learning algorithms are “opaque” to human users, failing to be “interpretable” or “explicable” in terms that would render categorization procedures “understandable.” The purpose of this paper is to challenge the widespread agreement about the existence (...) and importance of a black box problem. The first section argues that “interpretability” and cognates lack precise meanings when applied to algorithms. This makes the concepts difficult to use when trying to solve the problems that have motivated the call for interpretability. Furthermore, since there is no adequate account of the concepts themselves, it is not possible to assess whether particular technical features supply formal definitions of those concepts. The second section argues that there are ways of being a responsible user of these algorithms that do not require interpretability. In many cases in which a black box problem is cited, interpretability is a means to a further end such as justification or non-discrimination. Since addressing these problems need not involve something that looks like an “interpretation” of an algorithm, the focus on interpretability artificially constrains the solution space by characterizing one possible solution as the problem itself. Where possible, discussion should be reformulated in terms of the ends of interpretability. (shrink)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

Reason, we are told, is what makes us human, the source of our knowledge and wisdom. If reason is so useful, why didn't it also evolve in other animals? If reason is that reliable, why do we produce so much thoroughly reasoned nonsense? In their groundbreaking account of the evolution and workings of reason, Hugo Mercier and Dan Sperber set out to solve this double enigma. Reason, they argue with a compelling mix of real-life and experimental evidence, is not geared (...) to solitary use, to arriving at better beliefs and decisions on our own. What reason does, rather, is help us justify our beliefs and actions to others, convince them through argumentation, and evaluate the justifications and arguments that others address to us. In other words, reason helps humans better exploit their uniquely rich social environment. This interactionist interpretation explains why reason may have evolved and how it fits with other cognitive mechanisms. It makes sense of strengths and weaknesses that have long puzzled philosophers and psychologists--why reason is biased in favor of what we already believe, why it may lead to terrible ideas and yet is indispensable to spreading good ones.--. (shrink)

The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system’s outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in terms of the democratic ideal of ‘public reason’. I argue that situating demands for algorithmic accountability within this justificatory framework enables us to (...) better articulate their purpose and assess the adequacy of efforts toward them. (shrink)

Since it appeared in 1971, John Rawls's A Theory of Justice has become a classic. The author has now revised the original edition to clear up a number of difficulties he and others have found in the original book. Rawls aims to express an essential part of the common core of the democratic tradition--justice as fairness--and to provide an alternative to utilitarianism, which had dominated the Anglo-Saxon tradition of political thought since the nineteenth century. Rawls substitutes the ideal of the (...) social contract as a more satisfactory account of the basic rights and liberties of citizens as free and equal persons. "Each person," writes Rawls, "possesses an inviolability founded on justice that even the welfare of society as a whole cannot override." Advancing the ideas of Rousseau, Kant, Emerson, and Lincoln, Rawls's theory is as powerful today as it was when first published. (shrink)

Though the revised edition of A Theory of Justice, published in 1999, is the definitive statement of Rawls's view, so much of the extensive literature on Rawls's theory refers to the first edition. This reissue makes the first edition once again available for scholars and serious students of Rawls's work.

In this book, Seumas Miller examines the moral foundations of contemporary social institutions. Offering an original general theory of social institutions, he posits that all social institutions exist to realize various collective ends, indeed, to produce collective goods. He analyses key concepts such as collective responsibility and institutional corruption. Miller also provides distinctive special theories of particular institutions, including governments, welfare agencies, universities, police organizations, business corporations, and communications and information technology entities. These theories are philosophical and, thus, foundational and (...) synoptic in character. They are normative accounts of a sampling of contemporary social institutions, not descriptive accounts of all social institutions, both past and present. Miller also addresses various ethical challenges confronting contemporary institutional designers and policymakers, including the renovation of the international financial system, the 'dumbing down' of the media, the challenge of world poverty, and human rights infringements by security agencies combating global terrorism. (shrink)

The renowned philosopher John Searle reveals the fundamental nature of social reality. What kinds of things are money, property, governments, nations, marriages, cocktail parties, and football games? Searle explains the key role played by language in the creation, constitution, and maintenance of social reality. We make statements about social facts that are completely objective, for example: Barack Obama is President of the United States, the piece of paper in my hand is a twenty-dollar bill, I got married in London, etc. (...) And yet these facts only exist because we think they exist. How is it possible that we can have factual objective knowledge of a reality that is created by subjective opinions? This is part of a much larger question: How can we give an account of ourselves, with our peculiar human traits DS mind, reason, freedom, society - in a world that we know independently consists of mindless, meaningless particles? How can we account for our social and mental existence in a realm of brute physical facts? In answering this question, Searle avoids postulating different realms of being, a mental and a physical, or worse yet, a mental, a physical, and a social. There is just one reality: Searle shows how the human reality fits into that one reality. Mind, language, and civilization are natural products of the basic facts of the physical world described by physics, chemistry and biology. Searle explains how language creates and maintains the elaborate structures of human social institutions. These institutions serve to create and distribute power relations that are pervasive and often invisible. These power relations motivate human actions in a way that provides the glue that holds human civilization together. Searle shows how this account illuminates human rationality, free will, political power, and human rights. Our social world is a world created and maintained by language. (shrink)

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...) the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?". (shrink)

The Embodied Mind provides a unique, sophisticated treatment of the spontaneous and reflective dimension of human experience.

An overview of individualism in the philosophy of mind and cognitive science.

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...) secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

This book, which inaugurates the Princeton Monographs in Philosophy series, starts from Plato's analogy in the Republic between conflict in the soul and conflict in the city. Plato's solution required reason to impose agreement and harmony on the warring passions, and this search for harmony and agreement constitutes the main tradition in political philosophy up to and including contemporary liberal theory. Hampshire undermines this tradition by developing a distinction between justice in procedures, which demands that both sides in a conflict (...) should be heard, and justice in matters of substance, which will always be disputed. Rationality in private thinking consists in adversary reasoning, and so it does in public affairs. Moral conflict is eternal, and institutionalized argument is its only universally acceptable restraint and the only alternative to tyranny.In the chapter "Against Monotheism," Hampshire argues that monotheistic beliefs are only with difficulty made compatible with pluralism in ethics. In "Conflict and Conflict Resolution," he argues that socialism, seen as the proposal of extended political solutions for natural human ills, is still a relevant, yet strongly contested, ideal. (shrink)

There is widespread agreement that there should be a principle requiring that artificial intelligence be ‘explicable’. Microsoft, Google, the World Economic Forum, the draft AI ethics guidelines for the EU commission, etc. all include a principle for AI that falls under the umbrella of ‘explicability’. Roughly, the principle states that “for AI to promote and not constrain human autonomy, our ‘decision about who should decide’ must be informed by knowledge of how AI would act instead of us” :689–707, 2018). There (...) is a strong intuition that if an algorithm decides, for example, whether to give someone a loan, then that algorithm should be explicable. I argue here, however, that such a principle is misdirected. The property of requiring explicability should attach to a particular action or decision rather than the entity making that decision. It is the context and the potential harm resulting from decisions that drive the moral need for explicability—not the process by which decisions are reached. Related to this is the fact that AI is used for many low-risk purposes for which it would be unnecessary to require that it be explicable. A principle requiring explicability would prevent us from reaping the benefits of AI used in these situations. Finally, the explanations given by explicable AI are only fruitful if we already know which considerations are acceptable for the decision at hand. If we already have these considerations, then there is no need to use contemporary AI algorithms because standard automation would be available. In other words, a principle of explicability for AI makes the use of AI redundant. (shrink)

Jocelyn Maclure and Charles Taylor provide a clearly reasoned, articulate account of the two main principles of secularism—equal respect, and freedom of conscience—and argue that in our religiously diverse, politically interconnected world, secularism, properly understood, may offer the only path to religious and philosophical freedom.

We talk about irrationality when behaviour defies explanation or prediction, when decisions are driven by emotions or instinct rather than by reflection, when reasoning fails to conform to basic principles of logic and probability, and when beliefs lack coherence or empirical support. Depending on the context, agents exhibiting irrational behaviour may be described as foolish, ignorant, unwise or even insane. -/- In this clear and engaging introduction to current debates on irrationality, Lisa Bortolotti presents the many facets of the concept (...) and offers an original account of the importance of judgements of irrationality as value judgements. The book examines the standards against which we measure human behaviour, and reviews the often serious implications of judgements of irrationality for ethics and policy. Bortolotti argues that we should adopt a more critical stance towards accepted standards of rationality in the light of the often surprising outcomes of philosophical inquiry and cognitive science research into decision making. -/- Irrationality is an accessible guide to the concept and will be essential reading for students and scholars interested in the limitations of human cognition and human agency. (shrink)

Anthony Simon Laden explores the kind of reasoning we engage in when we live together: when we are responsive to others and neither commanding nor deferring to them. He argues for a new, social picture of the activity of reasoning, in which reasoning is a species of conversation--social, ongoing, and governed by a set of characteristic norms.

This chapter contains sections titled: Introduction Getting to Twin Earth: What's in the Head? The Cognitive Science Gesture Functionalism, Physicalism, and Individualism The Appeal to Causal Powers Externalism and Metaphysics The Debate Over Marr's Theory of Vision Exploitative Representation and Wide Computationalism Narrow Content and Marr's Theory Individualism and the Problem of Self‐knowledge.

Why does reason matter, if in the end everything comes down to blind faith or gut instinct? Why not just go with what you believe even if it contradicts the evidence? Why bother with rational explanation when name-calling, manipulation, and force are so much more effective in our current cultural and political landscape? Michael Lynch's In Praise of Reason offers a spirited defense of reason and rationality in an era of widespread skepticism--when, for example, people reject scientific evidence about such (...) matters as evolution, climate change, and vaccines when it doesn't jibe with their beliefs and opinions. In recent years, skepticism about the practical value of reason has emerged even within the scientific academy. Many philosophers and psychologists claim that the reasons we give for our most deeply held views are often little more than rationalizations of our prior convictions. _In_ _Praise of Reason_ gives us a counterargument. Although skeptical questions about reason have a deep and interesting history, they can be answered. In particular, appeals to scientific principles of rationality are part of the essential common currency of any civil democratic society. The idea that everything is arbitrary--that reason has no more weight than blind faith--undermines a key principle of a civil society: that we owe our fellow citizens explanations for what we do. Reason matters--not just for the noble ideal of truth, but for the everyday world in which we live. (shrink)

The political philosophy of health care has been characterized by considerable conceptual inflation in recent years. First, the concept of health that lies at its core has come to encompass ever-increasing aspects of individuals’ existences. And second, the emergence of the public health perspective has increased the range of resources relevant to health equity. This expansion has not been without cost. The decision to include more rather than less within the ambit of "health" is ultimately a moral/political rather than an (...) ontological or metaphysical one, and there are several ethical reasons to define the scope of theories of distributive justice in health narrowly. (shrink)

RÉSUMÉJe défends dans ce texte une version particulière de la position que Sharon Street a appelée le «constructivisme humien». J'esquisserai pourquoi je considère que ce constructivisme est préférable à la fois au réalisme moral et au constructivisme kantien sur le plan de la compréhension du statut ontologique des valeurs. Après avoir accepté de reconnaître le rôle des pressions de l’évolution dans l’émergence de la moralité, le constructivisme humien doit toutefois préciser le rôle de l'intersubjectivité historique dans l’évolution subséquente de la (...) morale. J'expliquerai aussi pourquoi je considère que l'intégration d'un volet cohérentiste au constructivisme permet d'atténuer les craintes concernant le caractère potentiellement non-cognitiviste des théories contextualistes. Enfin, j'esquisserai pourquoi l'idée d'un «espace des raisons morales» indépendant, à certains égards, de l'esprit humain est compatible avec le constructivisme humien. (shrink)