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This chapter examines how scientists working on Artificial Life have understood their practices as situated historically. It first considers the practice of finding genealogies for Artificial Life, arguing that such a search for ancestors carries acute historiographical and epistemological dangers. It then comments on computer simulations that fashion the computer as a kind of fish tank into which users can peer to see artificial life forms swimming about. It also discusses a different realm of modeling, that of cognition in Artificial Intelligence. The chapter concludes by suggesting a mode of imagining history that it calls an underwater archaeology of knowledge. In an underwater archaeology of knowledge, representational artifacts become mixed in with portraits of the world, requiring new sorts of narrative disentangling and qualification.

Artificial intelligence programs have increasingly entered public discourse in many diverse and overlapping ways. The various artificial intelligences are connected to our biologically based ones largely (though not solely) via the cyber network, which itself increasingly draws our species into its communicative framework. In this new, mediated, cyborg realm of culture there are no non-human animals, or plants, or any other natural forms of intelligence, but that does not mean we are all alone. Rather, there are new voices in our shared agora, now, and their voices do not necessarily attend to our own. This chapter explores the cultural overlaps of human and artificial intelligences online.

In this dialogue comprising seven widely diverse sections, José Hernández-Orallo, specialist in AI, reflects on a variety of topics surrounding Natural Intelligence and Artificial Intelligence (AI): 1. On what is measurable in intelligence, and what its ingredients are; 2. On how to universally measure intelligence; 3. On the Turing test; 4. On compared intelligences and the IQ (Intelligence Quotient); 5. On the AI agents of software; 6. On whether the human condition, (and happiness), can be mathematized; 7. On the relationship between intelligence and humor; and, 8. Are there universal ingredients in what we call intelligence? Toward the end, he talks about the current science and technology debate on whether the evolution of AI and its latest most disturbing incarnations (e.g., lethal autonomous weapons) can become an existential threat for humans or not. His reflections are culminated with arguments concerning a real danger— that someone, or something, might modify the present natural distribution of intelligence in the planet, which could end up being controlled by a global oligopoly.

This chapter questions the underlying assumptions of both classic Artificial Intelligence, founded in the analogy between the brain and the digital computer, and the newer tradition that construes the mind as an emergent property of interacting, distributed, parallel processes. It specifically explores Gestalt psychology and its brief engagement with cybernetics to suggest that was perhaps a missed opportunitt, and additionally examines John von Neumann's influential automata theory. The structure of insight helped explain the complex, nonmechanical behavior of living, acting organisms. For von Neumann, the creative plasticity of the nervous system served only to highlight the rather simplistic, and inferior, mechanical structure of the early computers, something he was of course well positioned to notice. His terse conclusion was that the logical structures involved in nervous system activity must “differ considerably” from the ones that are familiar in logic and mathematics.

This chapter addresses the question of whether a computer can become intelligent and how to test for that possibility. It introduces the idea of the Turing test, a test developed to determine, in an unbiased way, whether a program running in a computer is, or is not, intelligent. The development of artificial intelligence led, in time, to many applications of computers that are not possible using “non-intelligent” programs. One important area in artificial intelligence is machine learning, the technology that makes possible that computers learn, from existing data, in ways similar to the ways humans learn. A number of approach to perform machine learning is addressed in this chapter, including neural networks, decision trees and Bayesian learning. The chapter concludes by arguing that the brain is, in reality, a very sophisticated statistical machine aimed at improving the chances of survival of its owner.

This chapter compares the sequel's memory-making registers with what Dominick LaCapra has called ‘secondary memory’. Steven Spielberg's Artificial Intelligence: A. I. is explored to address the sequel's more complex registers of memory, repetition, erasure and history in three major contexts: cultural memory, the psychological development of memory and virtual memory. It persistently raises a number of psychoanalytic discourses and responses. Sequelisation in A. I. is a dominating feature of post-global warming, post-apocalyptic America. It then considers A. I. as a means by which a fuller understanding of both sequelisation and the Real can be obtained. The notion of the sequel organises the film's presentation of historical repetition. The repetition of the Real appears in David's case to create every experience and urge as a repetition of the ‘thing itself’, which is essentially himself. The sequel appears to problematise the experience, or memory, of the original.

By applying techniques used to understand large corpora within the digital humanities to a dataset of over 100,000 film subtitles ranging from the era of silent film to the present, this chapter presents a qualitative and quantitative overview of salient themes and trends in the way artificial intelligence is portrayed and discussed in twentieth- and twenty-first-century film. Examples are provided that demonstrate how combining the tools of traditional literary analysis with computational techniques for analysing large quantities of text can lead us to important texts, patterns, and nuanced insights that complement those derived from manual study alone.

This chapter employs anthropological approaches to examine the cultural influences on our stories about AI. First, the role of biological analogies in conceptions of AI will be highlighted. Second, a more nuanced approach to the anthropomorphism that results from such analogical thinking will be outlined, based on cognitive anthropology. This will allow us to identify specific intentional projections of human attributes that are mapped onto the nonhuman in our narratives. Third, we will consider how these projections come laden with cultural assumptions. Finally, the chapter will consider the parent–child relationship in AI narratives, and employ ethnographic research on the concept of the child to highlight what specific cultural assumptions about the human child, and then, subsequently, the AI child, are present in our stories.

Chapter 13 gives an impression of the development of the relatively young AI and computer science fields in Europe and China and how the current situation has developed over the past twenty years, where European and Chinese researchers are equal colleagues on an international stage and where diplomatic relations between the USA and China on the international stage have consequences felt directly by European AI researchers in their labs. In what ways are AI researchers in China and Europe competitors with each other, for example in terms of the global shortage of trained AI researchers and practitioners? At the same time, the AI research community collaborates globally, so how can we ensure that the field continues to benefit from open international collaboration?

This chapter argues that narratives about artificial intelligence (AI) have a major impact on science, policy, and society. These imaginaries of intelligent machines matter because they form the backdrop against which AI systems are being developed, and against which these developments are interpreted and assessed. The authors show how this book explores the way AI narratives have addressed, and offer sophisticated thinking about, some of the legitimate concerns that AI technologies now raise, such as loss of skills because of automation, replacement of the workforce by machines, and their role in perpetuating systems of oppression. At the same time, this book intervenes in a landscape in which prevalent AI narratives are mistrusted or criticized, for example, for their extremism, utopian or dystopian, or for their misrepresentation of current technology, for instance, in their tendency to focus on anthropomorphic representations.

New technologies like artificial intelligence, robotics, machine intelligence, and the Internet of Things are seeing repetitive tasks move away from humans to machines. Humans cannot become machines, but machines can become more human-like. The traditional model of educating workers for the workforce is fast becoming irrelevant. There is a massive need for the retooling of human workers. Humans need to be trained to remain focused in a society which is constantly getting bombarded with information. The two basic elements of physical and mental capacity are slowly being taken over by machines and artificial intelligence. This changes the fundamental role of the global workforce.

Much public discussion about an economy dominated by artificial intelligence has focused on robots and the future of work, particularly the destruction of jobs. On the other hand, economists have highlighted the historical record of job creation despite job displacement, and documented the way technological advances have eliminated jobs in some sectors but expanded jobs and increased wages in the economy overall. This chapter considers the role of policy in an artificial-intelligence-intensive economy (interpreting artificial intelligence broadly). It emphasizes the speed of adoption of the technology for the impact on the job market and the implications for inequality across people and across places. It also discusses the challenges of enacting a Universal Basic Income as a response to widespread adoption of artificial intelligence, and discusses pricing, privacy and competition policy, as well as the question of whether artificial intelligence could affect policy making itself.

This chapter uses the film Artificial Intelligence (2002) to set the stage for a discussion of the ethics of emerging technologies. The film focuses on an 11-year-old artificial boy, David (Haley Joel Osment), who was created to give love without demands: he would need no food, no rest, no love in return. The film addresses questions such as: What is our responsibility toward our own creations? How can society deal with enhancement technologies applied to the human body? The chapter reflects on these topics from a bioethical standpoint, and argues that responsibility and humility should guide medical activity, as illustrated in parts of the Hippocratic Oath. Emerging technologies and their applications to health care should not be subjected to wholesale legislative limitations, regardless of context or culture. Law-making must be interdisciplinary, intimately involved in the discussion of and reflection on bioethical issues, in order to deal with their complexity as well as their social aspects and peculiarities.

One reason it matters whether phenomenally conscious robots will soon be forthcoming is that such robots would have moral rights. The view that they are on the horizon often rests on a certain philosophical view about consciousness, one called “nomological behaviorism” in this chapter. The view entails that, as a matter of nomological necessity, if a robot had exactly the same patterns of dispositions to peripheral behavior as a phenomenally conscious being, then the robot would be phenomenally conscious. The chapter experimentally investigates whether the folk think that certain (hypothetical) robots made of silicon and steel would have the same conscious states as certain familiar biological beings with the same patterns of dispositions to peripheral behavior as the robots. The findings provide evidence that the folk largely reject the view that silicon-based robots would have the sensations that they, the folk, attribute to the biological beings in question.

This essay is the first in the book’s Science, Fiction and Reality section. It was originally a paper read at a conference held at the University of Teesside in April 1995 and was later published in The Governance of Cyberspace. The essay talks about the evolution of science fiction and how it’s easy to spot when it has been overtaken by the development of technology; specifically in computers, artificial intelligence, robotics, and cybernetics. In her discussion of technology and cyberspace, Jones alludes to the possibility of consciousness, self-awareness and freedom of information, and makes reference to the science fiction novels of William Gibson and Pat Cadigan.

Sarah Dillon and Michael Dillon bring political theory into dialogue with literary criticism in order to explore the interaction between artificial intelligence and the ancient conflict between sovereignty and governance, in which sovereignty issues the warrant to rule, and governance operationalizes it. They focus on three novels in which games, governance, and AI weave themselves through the text’s fabric: Iain M. Banks’s The Player of Games (1988) and Excession (1996), and Ann Leckie’s Ancillary Justice (2013). These novels play out the sovereign-governance game with artificial as well as human actors. In doing so, they question what might be politically novel about AI, but reveal that whilst AI impacts the pieces on the board, it does not materially change the logic of the game. These texts therefore raise questions, but do not provide answers, with regard to what might be required for AI technologies to change the algorithms of modern rule.

Antonio Torralba, member of MIT CSAIL, opens the dialogue by describing the research he performs in the field of computer vision and related artificial intelligence (AI). He also compares the conceptual differences and the context of the early days of artificial intelligence—where hardly any image recording devices existed—with the present situation, in which an enormous amount of data is available. Next, through the use of examples, he talks about the huge complexity faced by research in computer vision to get computers and machines to understand the meanings of what they “see” in the scenes, and the objects they contain, by means of digital cameras. As he explains afterward, the challenge of this complexity for computer vision processing is particularly noticeable in settings involving robots, or driverless cars, where it makes no sense to develop vision systems that can see if they cannot learn. Later he argues why today’s computer systems have to learn “to see” because if there is no learning process, for example machine learning, they will never be able to make autonomous decisions.