This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of artificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understanding of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so we suggest that “AI philosophy” provides a new method for philosophy.

Methods for analyzing and visualizing literary data receive substantially more attention in digital literary studies than the digital archives with which literary data are predominantly constructed. When discussed, digital archives are often perceived as entirely different from nondigital ones, and as passive – that is, as novel and enabling (or disabling) settings or backgrounds for research rather than active shapers of literary knowledge. This understanding produces abstract critiques of digital archives, and risks conflating events and trends in the histories of literary data with events and trends in literary history. By contrast, an emerging group of media-specific approaches adapt traditional philological and media archaeological methods to explore the complex and interdependent relationship between literary knowledges, technologies, and infrastructures.

Starting off substantive engagement with Sun Tzu with a focus on calculation serves a positive purpose. It is a way of emphasizing to contemporary audiences that there is more to Sun Tzu than being tricky or unorthodox – the strands of his way of war that readers, at least Western ones, widely note and often lionize. In present usage, the umbrella term “calculation” is intended to serve as a flexible rubric capable of covering intendedly rational judgments of more than one kind, many intuitive, others more formally structured.

The casino provided a unique location to probe the logic of chance for those seeking to understand fortune and misfortune, causation and correlation. Chance helped generate predictability. When we shift to consider the picture of luck that emerges, we see that it is exhibited in various systems designed to generate wins at the gambling table, lured to a person to through any number of bizarre superstitions, and made the object of social scientific inquiry. Luck was something that people could generate, manufacture, cultivate, or capture. This element of human agency speaks to a vision of the world that promoted the basic idea of human agency while also acknowledging its limits. Gambling systems and superstitions, especially when they did not rest on the foundation of the “maturity of chances,” were at their heart modern attempts to bend luck to one’s side.

The nineteenth-century Australian novel has predominantly been understood in terms of the dominance of Britain, both as the place where most books were published and as the source of literary traditions. But this account presumes and maintains the status of the book as the primary vehicle for transmission of literature, whereas the vast majority of Australian novels were serialised (either before or after book publication) and a great many were only ever published in serial form. A history of the early Australian novel that recognises the vital role of serialisation, as distinct from but also in relation to book publication, brings to light new trends in authorship, publication, circulation and reception. This history also uncovers new Australian novelists as well as previously unrecognised features of their fiction. In particular, a number of literary historians argue that early Australian novelists replicated the legal lie of terra nullius in excluding Aboriginal characters from their fiction. Considering fiction serialised in Australian newspapers indicates that these characters were actually widely depicted and suggests the need for a new account of the relationship between nineteenth-century Australian novels and colonisation.

This chapter provides an introduction and an overview of computational cognitive sciences. Computational cognitive sciences explore the essence of cognition and various cognitive functionalities through developing mechanistic, process-based understanding by specifying corresponding computational models. These models impute computational processes onto cognitive functions and thereby produce runnable programs. Detailed simulations and other operations can then be conducted. Understanding the human mind strictly from observations of, and experiments with, human behavior is ultimately untenable. Computational modeling is therefore both useful and necessary. Computational cognitive models are theoretically important because they represent detailed cognitive theories in a unique, indispensable way. Computational cognitive modeling has thus far deepened the understanding of the processes and the mechanisms of the mind in a variety of ways.

What counts as a philosophical issue in computational cognitive science? This chapter briefly reviews possible answers before focusing on a specific subset of philosophical issues. These surround challenges that have been raised by philosophers regarding the scope of computational models of cognition. The arguments suggest that there are aspects of human cognition that may, for various reasons, resist explanation or description in terms of computation. The primary targets of these “no go” arguments have been semantic content, phenomenal consciousness, and central reasoning. This chapter reviews the arguments and considers possible replies. It concludes by highlighting the differences between the arguments, their limitations, and how they might contribute to the wider project of estimating the value of ongoing research programs in computational cognitive science.

Belief is often formalized using tools of probability theory. However, probability theory often focuses on simple examples – like coin flips or basic parametric distributions – and these do not describe much about actual human thinking. I highlight some basic examples of the complexity and richness of human mental representations and review some work which attempts to marry plausible types of representations with probabilistic models of belief, one of the most exciting current directions in psychology and machine learning.

Until one is committed, there is hesitancy, the chance to draw back. Concerning all acts of initiative (and creation), there is one elementary truth, the ignorance of which kills countless ideas and splendid plans: that the moment one definitely commits oneself, then Providence moves too. All sorts of things occur to help one that would never otherwise have occurred. A whole stream of events issues from the decision, raising in one’s favor all manner of unforeseen incidents and meetings and material assistance, which no man could have dreamed would have come his way. I have learned a deep respect for one of Goethe’s couplet’s: “Whatever you can do, or dream you can do, begin it. Boldness has genius, power, and magic in it.”

Twenty-first century paradigms of global modernism implicitly endorse “babelization” (the inscrutable styles of literary texts, the addition of lesser taught languages to the field) as a corrective to linguistic imperialism and the reduction of language to a communicative medium. Yet this stance does not fully account for the distinction between natural and artificial languages. “Debabelization,” as linguist C. K. Ogden put it in 1931, motivated rich debates about the nature of language and whether technological intervention could make particular languages more efficient agents of cultural exchange. Designers of Esperanto, Ido, and Basic English each promised that their artificial language would bridge the gap between speakers of different national tongues. This essay shows how the competitive and techno-utopian discourse around auxiliary language movements intersects with the history and aesthetics of modernist literature. While linguists strove to regulate the vagaries of natural languages, modernist writers (for example, Aimé Césaire, G. V. Desani, James Joyce, Ezra Pound, H. G. Wells) used debabelization as a trope for exploring the limits of scientific objectivity and internationalist sentiment.