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Finds fault with Turing's answer to the question, ‘Can a computer think’? Turing believed that if the answers given by a computer and a person leave an interpreter unable to discriminate between them, then computers must be said to be able to think. The author objects that in order for a computer to think, it must mean something by the answer it gives. Consequently, without evidence for the fact that a computer not merely possesses the syntax of the language it is responding in but also has a semantics, the findings of Turing's Test cannot be used as evidence for the claim that computers can, even in theory, think. Understanding the semantics of an object or creature requires that the interpreter be able to observe what in the world that is shared by interpreter and interpretant causes the latter's responses; having a semantics requires a history of engagement with others and with objects in the world. Turing's test for thought is inadequate, according to the author, not because it restricts the evidence to what can be observed about the computer from the outside, but because it does not allow enough of what is outside to be observed.

This chapter considers how a cultural insistence on confined space emerges in direct proportion to the manifest range or intensity of female networking. Beginning with the tradition of the locked room mystery and its disturbing misogynistic tendencies, the chapter analyses the isolating effects of technologies from telephones to e-mail when deployed in the name of romance or safety. A brief philosophical detour yields a curious opportunity in the epistemology of artificial intelligence as established by the so-called Turing Test, where the locked room required by the test’s controls creates an unanticipated space of gender ambiguity and desire. This dynamic returns in the pseudonymous e-mail choreography of You’ve got Mail, only to be shut down by gothic forces in thin disguise. The chapter comes finally to consider the gendered inversion of space in Bound as an impressive, if still troubling, escape from the space of crime.

Alan Turing’s model of computation (1936) is explicated in terms of the potential operations of a human “computer”, and his famous test for intelligence (1950) is based on indistinguishability from human verbal behaviour. But this chapter challenges the apparent human-centredness of the 1936 model, suggesting a focus instead on mathematical concepts, with human comparisons making an entrance only retrospectively. The 1950 account of intelligence also turns out to be far less human-centred than it initially appears to be, because the universality of computation makes human intelligence just one variety amongst many. It is only when Turing considers consciousness that he treats intelligence in a way that cannot properly be carried over to machines. But here he is mistaken, since his own work gave ample reason to reinterpret intelligence as sophisticated information processing for some purpose, and to divorce this from the subjective consciousness with which it is humanly associated.

A framework is provided for understanding the trajectory toward increasingly sophisticated artificial moral agents, emphasizing two dimensions: autonomy and sensitivity to morally relevant facts. Systems low on both dimensions have “operational morality” their moral significance is entirely in the hands of designers and users. Systems intermediate on either dimension have “functional morality” the machines themselves can assess and respond to moral challenges. Full moral agents, high on both dimensions, may be unattainable with present technology. This framework is compared to Moor's categories, which range from implicit ethical agents whose actions have ethical impact, to explicit ethical agents that are explicit ethical reasoners. Different ethical issues are raised by AI's various objectives from the augmentation of human decision making (basic decision support systems to cyborgs) to fully autonomous systems. Finally, the feasibility of a modified Turing Test for evaluating artificial moral agents—a Moral Turing Test—is discussed.

How closely could artificial agents, lacking human qualities such as consciousness and emotions, come to being considered moral agents? Chapter 4 begins by discussing the issue of whether a “mere” machine can be a moral agent. A pragmatically oriented approach is developed, which recognizes that full‐blown moral agency (which depends on “strong AI”) or even AI powerful enough to pass the Turing Test may be beyond current or future technology, but locates the project of developing artificial moral agents in the space between operational morality and genuine moral agency. This niche is labeled “functional morality.” The goal of this chapter is to address the question of what the various approaches to artificial intelligence (AI) from traditional symbol‐processing approaches to more recent approaches based on embodied cognition can provide toward functional morality.

The evolution of artificial intelligence in science fiction film has showcased an array of technological marvels, and yet each reflects the era in which the films were made, be it what the device looks like, the extent of its power, or the ethical/moral issues surrounding its existence. Ex Machina is no different, with the development of AI firmly embedded in the tech industry. Caleb’s entire purpose for being at Nathan’s compound is to determine whether Nathan has, in fact, created artificial intelligence or if Ava is simply imitating human interactions. This is called the Turing Test, which has been around for nearly 70 years, and it has been rigorously debated for almost its entire existence. Ex Machina pushes this debate by accepting and challenging key assumptions of the Turing Test while positing its own: The role affection/attraction/love might play in the entire process. As such, by considering these emotional components (as expressed toward the creation rather than from it) grounds the discussion in terms of morality and soul, something previous films have treated more as a by-product of artificial intelligence.

Since the 1950s, robotics research has sought to build a general-purpose agent capable of autonomous, open-ended interaction with realistic, unconstrained environments. Cognition is perceived to be at the core of this process, yet understanding has been challenged because cognition is referred to differently within and across research areas, and is not clearly defined. The classic robotics approach is decomposition into functional modules which perform planning, reasoning, and problem-solving or provide input to these mechanisms. Although advancements have been made and numerous success stories reported in specific niches, this systems-engineering approach has not succeeded in building such a cognitive agent. The emergence of an action-oriented paradigm offers a new approach: action and perception are no longer separable into functional modules but must be considered in a complete loop. This chapter reviews work on different mechanisms for action-perception learning and discusses the role of embodiment in the design of the underlying representations and learning. It discusses the evaluation of agents and suggests the development of a new embodied Turing Test. Appropriate scenarios need to be devised in addition to current competitions, so that abilities can be tested over long time periods.

This chapter explores the philosophical question of whether computers can really think. It considers the Turing Test and Searle's Chinese Room argument. It suggests that regardless of one's position on the philosophical issues, we are still left with what might be called the AI question: If it is indeed true that tricks and fakery are not sufficient to generate intelligent behavior such as passing some form of the Turing Test, then what is? In the end, it is this question that is perhaps the most profound one to emerge out of the entire discussion, and one that will not be resolved by merely arguing one way or another.

This chapter details the inner workings of spam filters, algorithmic devices that separate desirable messages from undesirable messages. It argues that such filters are a particularly important kind of sieve insofar as they readily exhibit key features of sieving devices in general, and algorithmic sieving in particular. More broadly, it describes the relation between ontology (assumptions that drive interpretations) and inference (interpretations that alter assumptions) as it plays out in the classification and transformation of identities, types, or kinds. Focusing on the unstable processes whereby identifying algorithms, identified types, and evasive transformations are dynamically coupled over time, it also theorizes various kinds of ontological inertia and highlights various kinds of algorithmic ineffability. Finally, it shows how similar issues underlie a much wider range of processes, such as the Turing Test, Bayesian reasoning, and machine learning more generally.

Historically, major intellectual figures, most notably Rene Descartes, have been committed to the claim that only humans can have experiences. Radical conservatism of this sort is a mistake. The mistake has its origins in religious conviction, mind-body dualism, and an alleged connection between thought and language. This conservatism survives in certain contemporary views that hold that experience is necessarily thought-like or conceptual. However, as this chapter argues, there are few compelling reasons we should adopt these conceptualist views, and they face many problems. Conceptualism, therefore, and with it the belief that consciousness is necessarily found only in humans, can be rejected.

While sophisticated artificial beings are still the stuff of science fiction, it is reasonable to address the challenge of determining the moral status of such systems now. Since humans have spent centuries discussing the ethics of humans and animals, a sensible shortcut is to develop tests for matching artificial beings with existing beings and assigning them a corresponding moral status. While there are a multitude of moral theories addressing the matter of status, the focus is on two of the most common types. The first comprises theories based on reason (exemplified by Kant). The second comprises theories based on feeling (exemplified by Mill). Regardless of the actual tests, there will always be room for doubt. To address this, three arguments are presented in favor of the presumption of status, similar to that of the presumption of innocence in the legal system.