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This chapter selectively reviews computational theories of analogical reasoning from Evans, Gentner, Holyoak and Thagard, Ashley, Carbonell, and Hofstadter. While these theories provide insight into the processes involved in analogical reasoning, many of them operate with a perceptual model of analogical reasoning and appear to neglect normative questions. It is argued that most of the computational theories do, at least implicitly, incorporate normative principles and that those principles need to be examined critically. In particular, the chapter takes a close look at Gentner's systematicity principle. It is alleged that systematicity per se neither produces nor explains the plausibility of analogical arguments.

This chapter examines the impact of silver policy on American politics by focusing on the demonetization of the silver dollar in the Coinage Act of 1873 and the claims made by the free silver movement in the 1870s and 1880s that were subsequently adopted by the Populist Party and William Jennings Bryan. It first considers the passage of the Coinage Act, denounced by the Populists as the “Crime of '73,” and goes on to discuss the politics of silver and its relevance to general issues involving popular comprehension of democratic politics. It then explores Richard Hofstadter's reaction to the Populists' conspiratorial view of the silver issue and the studies echoing his arguments. It supports the Populists' contention that William Chapman Ralston, president of the Bank of California, bribed Henry Linderman, the official who wrote the Coinage Act of 1873, to influence silver policy. It suggests that silver had been demonetized to protect not only Ralston's business empire but also the gold standard and the public debt.

In February 2011, IBM’s Watson computer entered the championship round of the popular TV quiz show Jeopardy!, going on to beat Brad Rutter and Ken Jennings, each long-time champions of the game. Fourteen years earlier, in 1997, IBM’s Deep Blue computer had beaten world chess champion Garry Kasparov. At that time no one ascribed any aspects of human ‘intelligence’ to Deep Blue, even though playing chess well is often considered an indicator of human intelligence. Deep Blue’s feat, while remarkable, relied on using vast amounts of computing power to look ahead and search through many millions of possible move sequences. ‘Brute force, not “intelligence”,’ we all said. Watson’s success certainly appeared similar. Looking at Watson one saw dozens of servers and many terabytes of memory, packed into ‘the equivalent of eight refrigerators’, to quote Dave Ferrucci, the architect of Watson. Why should Watson be a surprise? Consider one of the easier questions that Watson answered during Jeopardy!: ‘Which New Yorker who fought at the Battle of Gettysburg was once considered the inventor of baseball?’ A quick Google search might reveal that Alexander Cartwright wrote the rules of the game; further, he also lived in Manhattan. But what about having fought at Gettysburg? Adding ‘civil war’ or even ‘Gettysburg’ to the query brings us to a Wikipedia page for Abner Doubleday where we find that he ‘is often mistakenly credited with having invented baseball’. ‘Abner Doubleday ’ is indeed the right answer, which Watson guessed correctly. However, if Watson was following these sequence of steps, just as you or I might, how advanced would its abilities to understand natural language have to be? Notice that it would have had to parse the sentence ‘is often mistakenly credited with . . .’ and ‘understand’ it to a sufficient degree and recognize it as providing sufficient evidence to conclude that Abner Doubleday was ‘once considered the inventor of baseball’. Of course, the questions can be tougher: ‘B.I.D. means you take and Rx this many times a day’—what’s your guess? How is Watson supposed to ‘know’ that ‘B.I.D.’ stands for the Latin bis in die, meaning twice a day, and not for ‘B.I.D. Canada Ltd.’, a manufacturer and installer of bulk handling equipment, or even Bid Rx, an internet website? How does it decide that Rx is also a medical abbreviation? If it had to figure all this out from Wikipedia and other public resources it would certainly need farmore sophisticated techniques for processing language than we have seen in Chapter 2.

On 14 October 2011, the Apple Computer Corporation launched the latest generation of the iPhone 4S mobile phone. The iPhone 4S included Siri, a speech interface that allows users to ‘talk to their phone’. As we look closer though, we begin to suspect that Siri is possibly more than ‘merely’ a great speech-to-text conversion tool. Apart from being able to use one’s phone via voice commands instead of one’s fingers, we are also able to interact with other web-based services. We can search the web, for instance, and if we are looking for a restaurant, those nearest our current location are retrieved, unless, of course, we indicated otherwise. Last but not least, Siri talks back, and that too in a surprisingly human fashion. ‘Voice-enabled location-based search—Google has it already, so what?’, we might say. But there is more. Every voice interaction is processed by Apple’s web-based servers; thus Siri runs on the ‘cloud’ rather than directly on one’s phone. So, as Siri interacts with us, it is also continuously storing data about each interaction on the cloud; whether we repeated words while conversing with it, which words, from which country we were speaking, and whether it ‘understands’ us or not in that interaction. As a result, we are told, Siri will, over time, learn from all this data, improve its speech-recognition abilities, and adapt itself to each individual’s needs. We have seen the power of machine learning in Chapter 3. So, regardless of what Siri does or does not do today, let us for the moment imagine what is possible. After all, Siri’s cloud-based back-end will very soon have millions of voice conversations to learn from. Thus, if we ask Siri to ‘call my wife Jane’ often enough, it should soon learn to ‘call my wife’, and fill in her name automatically. Further, since storage is cheap, Siri can remember all our actions, for every one of us: ‘call the same restaurant I used last week’, should figure out where I ate last week, and in case I eat out often, it might choose the one I used on the same day last week.

Over the past few decades, electronic computer technology has made enormous strides. Moreover, there can be little doubt that in the decades to follow, there will be further great advances in speed, capacity and logical design. The computers of today may be made to seem as sluggish and primitive as the mechanical calculators of yesteryear now appear to us. There is something almost frightening about the pace of development. Already computers are able to perform numerous tasks that had previously been the exclusive province of human thinking, with a speed and accuracy which far outstrip anything that a human being can achieve. We have long been accustomed to machinery which easily out-performs us in physical ways. That causes us no distress. On the contrary, we are only too pleased to have devices which regularly propel us at great speeds across the ground - a good five times as fast as the swiftest human athlete - or that can dig holes or demolish unwanted structures at rates which would put teams of dozens of men to shame. We are even more delighted to have machines that can enable us physically to do things we have never been able to do before: they can lift us into the sky and deposit us at the other side of an ocean in a matter of hours. These achievements do not worry our pride. But to be able to think - that has been a very human prerogative. It has, after all, been that ability to think which, when translated to physicaJ terms, has enabled us to transcend our physical iimitations and which has seemed to set us above our fellow creatures in achievement. If machines can one day excel us in that one important quality in which we have believed ourselves to be superior, shall we not then have surrendered that unique superiority to our creations? The question of whether a mechanical device could ever be said to think- perhaps even to experience feelings, or to have a mindis not really a new one. But it has been given a new impetus, even an urgency, by the advent of modern computer technology.

Willful blindness has reached epidemic proportions in our time. Nowhere is this more evident than in recent actions by the U.S. Congress to deny outright the massive and growing body of scientific data about the deterioration of the earth’s vital signs, while attempting to dismantle environmental laws and regulations. But the problem of ecological denial is bigger than recent events in Congress. It is flourishing in the “wise use” movement and extremist groups in the United States, among executives of global corporations, media tycoons, and on main street. Denial is in the air. Those who believe that humans are, or ought to be, something better than ecological vandals need to understand how and why some people choose to shun reality. Denial, however, must be distinguished from honest disagreement about matters of fact, logic, data, and evidence that is a normal part of the ongoing struggle to establish scientific truth. Denial is the willful dismissal or distortion of fact, logic, and data in the service of ideology and self-interest. The churchmen of the seventeenth century who refused to look through Galileo’s telescope, for example, engaged in denial. In that instance, their blind obedience to worn-out dogma was expedient to protect ecclesiastical authority. And denial is apparent in every historical epoch as a willing blindness to the events, trends, and evidence that threaten one established interest or another. In our time, great effort is being made to deny that there are any physical limits to our use of the earth or to the legitimacy of human wants. On the face of it, the case is absurd. Most physical laws define the limits of what it is possible to do. And all of the authentic moral teachings of 3,000 years have been consistent about the dangers and futility of unfettered desire. Rather than confront these things directly, however, denial is manifested indirectly. A particularly powerful form of denial in U.S. culture begins with the insistence on the supremacy over all other considerations of human economic freedom manifest in the market economy. If one chooses to believe that economies so dominated by lavishly subsidized corporations are, in fact, free, then the next assumption is easier: the religious belief that the market will solve all problems.

This chapter explores three different attempts to write a fitting obituary for Protestant fundamentalism. The first, Stanley Kramer's 1960 film Inherit the Wind, portrays fundamentalism as a reactionary movement whose intellectual bankruptcy was dramatically revealed in a famous trial about the teaching of evolution. The other two obituaries—Norman F. Furniss' The Fundamentalist Controversy, (1954) and Richard Hofstadter's Anti-Intellectualism in American Life (1963)—were both written by professional historians. Both books present fundamentalism as a form of Christianity that in the past blocked Americans' search for knowledge; both assert that religious fundamentalism's influence peaked in the 1920s and then went into steep decline.

The book’s last chapter reinterprets Kant’s distinction between a determining and a reflecting judgment in modern, cybernetic terms: a reflecting judgment is a feedback loop of the mind. It further argues that Kant’s perforce ignorance of the concept of feedback led him to place the “as if” of reflection in the human mind rather than in nature, the way modern scientists routinely do when they say, for example, that it is as if natural selection had chosen this or that biological solution to a species’s survival. More radically, this last chapter argues that the discovery of the concept of feedback has made the Critique of Teleological Judgment obsolete. The fact that living nature apparently orients itself according to goals has been explained, and explained away, by cybernetics and neo-Darwinism. But far from disqualifying the Critique of Aesthetic Judgment, this leaves the realm of art alone to bear the weight of having to answer Kant’s third fundamental question, “What are we allowed to hope?”

Chapter 1 analyses the postwar body of political thought known as pluralism. It explores the work of key contributors to this body of thought such as David Riesman, Seymour Martin Lipset, and Daniel Bell, organizing the chapter by the different themes or memes they participated in and articulated. Chapter 1 presents the argument that pluralism deeply shaped postwar liberalism and infused it with the conviction that American political institutions were the best in the world and truly open to all who wanted to participate. However, pluralists also feared the poor, who they associated with ignorance and authoritarianism.

Fake news has been cyclically surfacing in the history of journalism and public opinion. In the vein of some classic authors, the chapter identifies ideas that are surprisingly useful in the present media environment. It interweaves three historical threads relevant to today’s fake news: (1) the growth of canards in 19th-century Paris, observed by both Honoré de Balzac and Gérard de Nerval as the habitual invention of news when facts were not sufficiently attractive for readers; (2) the diffusion of fausses nouvelles during the Great War, described by Marc Bloch and propelled by the tendency, in times of crisis, to search for oracles more than information proper; (3) the propensity, suggested by Richard Hofstadter, to spread conspiracy theories, notably in the development of McCarthyism.

This chapter examines the decline of the antitrust movement in the United States. It analyzes Richard Hofstadter's What Happened to the Antitrust Movement? that concerns the disappearance of the movement just as it attained greater power as a regulator of industry structure and conduct. It considers the two arguments that Hofstadter believed to had happened that led to the decline: businesses had fit much more comfortably into the landscape of public opinion, and antitrust became much more technical and began to speak the language of economists.