In Auguste Villiers de l’Isle-Adam’s L’Ève future (1885–6), a fictional version of Thomas Edison builds a mechanical female automaton as a replacement for a human woman. This chapter reads the novel and its gynoid Hadaly as works of decadent speculative fiction. After tracing the relationships of L’Ève Future to decadence as a literary movement via late nineteenth-century writers such as J.-K. Huysmans, Paul Bourget, and Arthur Symons, it argues that the work’s decadent tropes and commitments allow it to place a critical spin on automata and automatism. Villiers’s vision of automacy – as alluringly artificial yet both relational and entangled in cultural norms about the human – not only exceeds the analogous ventures of the real-life Edison but also resonates with attempts to come to terms with the nature and functions of autonomous artificial entities today.

In this chapter, the philosopher Christoph Durt elaborates a novel view on AI and its relation to humans. He contends that AI is neither merely a tool, nor an artificial subject, nor necessarily a simulation of human intelligence. These misconceptions of AI have led to grave misunderstandings of the opportunities and dangers of AI. A more comprehensive concept of AI is needed to better understand the possibilities of responsible AI. The chapter shows the roots of the misconceptions in the Turing Test. The author argues that the simplicity of the setup of the Turing Test is deceptive, and that Turing was aware that the text exchanges can develop in much more intricate ways than usually thought. The Turing Test only seemingly avoids difficult philosophical questions by passing on the burden to an evaluator, who is part of the setup, and hides in plain sight his or her decisive contribution. Durt shows that, different from all previous technology, AI processes meaningful aspects of the world as experienced and understood by humans. He delineates a more comprehensive picture according to which AI integrates into the human lifeworld through its interrelations with humans and data.

This chapter addresses influential computational models of the mind. In the first section, we look at the physical symbol system hypothesis proposed by Herbert Simon and Allen Newell. The hypothesis suggests that thinking is a process of manipulating symbol structures according to well-defined rules. Next, we introduce Jerry Fodor's language of thought hypothesis which proposes that thinking has a language-like grammatical structure. We explain why the language of thought hypothesis is a concrete practice of the physical symbol system hypothesis in the human cognitive system. The last section explores an argument against the physical symbol theory developed by John Searle. His Russian (Chinese) room argument is intended to refute the claim that manipulating symbols is sufficient for intelligence.

Chapter 5 presents the subject matter of artificial intelligence, focusing on machine learning, where these machines are artificial agents. It presents simple examples of unsupervised learning, supervised learning, and reinforcement learning, and introduces notions of dynamic programming, Q-learning, and stochastic control. After that, it explores some links that can be established between artificial intelligence and the philosophy of mind, presenting and discussing the Turing test, the philosophical approaches of eliminativism and functionalism, and the problem of tacit knowledge.

Machine learning (ML) is a data-driven modeling approach that has become popular in recent years, thanks to major advances in software and hardware. Given enough data about a complex system, ML allows a computer model to imitate that system and predict its behavior. Unlike a deductive modeling approach, which requires some understanding of a system to be able to predict its behavior, the inductive approach of ML can predict the behavior of a system without ever understanding it in a traditional sense. Climate is a complex system, but there is not enough observed data describing an unprecedented event like global warming on which a computer model can be trained. Instead, it may be more fruitful to use ML to imitate a climate model, or a component of it, to greatly speed up computations. This will allow the parameter space of climate models to be explored more efficiently.

This chapter examines machine consciousness. It begins by looking at artificial life, and simulations such as BOIDS that model flocking behaviour. It introduces the concepts of complexity and emergence. It then examines artificial intelligence (AI) in detail. It looks at the idea of computer intelligence, and computers playing games, particularly chess and Deep Blue. The chapter discusses the Turing test and asks what would be an appropriate Turing two for consciousness. It looks at technological advance and asks when, if ever, we are likely to see a conscious computer. It discusses Kurzweil’s notion of the singularity. The chapter then discusses robotics, including different types of robot, and swarm intelligence and evolutionary robotics. The chapter examines attempts to build a brain, and enhancing cognition and consciousness through prosthetics. The chapter concludes by asking whether AI will be a threat.