The seeds of books germinate in both well-lit and shadowy imaginative spaces. In this way, books exhibit an affinity with the dreams Sigmund Freud studied in The Interpretation of Dreams (1900), where he learned that identifying the “background thoughts” from which dream symbols emerge, particularly those intricate or bizarre images resistant to quick explanation, was hardly a simple task. The search for their origins led him to free association, a process in which a patient focuses on specific images, not on a complete dream narrative, and to the conclusion that “interpretation en detail and not en masse” better enables an investigator to uncover the overdetermined nature of dream images—their provenance in several sources, not just one. Like dreams, books often arise from an untidy jumble of places: an archive of prior cultural texts (scrivened, visual, aural); major social, scientific and historical developments; and the imprints of individual experiences, large and small, etched on a writer's memory. Some of these are transformative or, in the worst of cases, traumatic—a stunning success or mortifying failure, a once-in-a-century pandemic and a pitched medical battle to vanquish it—while others are tethered to the banalities of everyday life that, surprisingly, demand expression. Such is the case with From the “Troubles” to Trumpism.

As a student of Irish history and culture for over forty years, I have enjoyed numerous opportunities to visit Ireland and Northern Ireland, and written about both, most often discussing literature, drama and theatrical production. This engagement constitutes one source of the pages that follow but, again, there are others. One in particular motivates the political bristle of this book: recent socio-political discord in America, particularly that associated with the presidential election of 2020, the insurrection at the Capitol on January 6, 2021 and the shocking state of affairs (and indictments) prefatory to the 2024 elections.

There is an incisive exchange about history and collective memory in Bernard MacLaverty's Cal, a novel (and, later, film) set during the Troubles complete with Orange Lodge parades, deadly ambushes and the firebombing of Catholic homes. Over his career, MacLaverty has written several novels and short stories portraying in often excruciating detail the emotional toll of living through such violence, with Cal being, arguably, the most poignant. When discussing the novel, critics often point to similarities between the dilemma of its main characters and that of Shakespeare's “star-crossed” lovers Romeo and Juliet, as its protagonist Cal McCrystal (McCluskey in later printings), an unemployed, working-class Catholic, falls in love with Marcella Morton, the young widow of a Protestant policeman in whose murder Cal was complicit. In this “love across the barricades” story, as in Shakespeare's play, a sense of tragic foreboding is occasionally relieved by glimmers of possibility—for example, when Cal finds fulfilling work on the Morton family farm and makes a new home there to be near Marcella. His days of living on the dole may be over, and his new job hints at a better future. Unlike the protagonists of Romeo and Juliet whose fates are tied to family lineages and histories they cannot alter, Cal seems convinced that he possesses the agency to escape his connection to sectarian violence. Sadly, in the novel's closing scene, his arrest and imminent punishment destroy any possibility of a future with Marcella. But the question remains unanswered, to recall Haines's observation in James Joyce's Ulysses, of how “history is to blame” for Cal's fate. Perhaps it isn’t. Perhaps historical memory and the at times nefarious uses to which history is put are the culprits (Figure 6).

Unlike Cal, MacLaverty's later novel Grace Notes (1997) develops tensions between memory and aspiration that lead to a happier, even refulgent conclusion. The novel begins with a fledgling composer, Catherine McKenna, returning to Northern Ireland from Scotland to attend her father's funeral. At the cemetery where he is interred, she passes the grave of a boy she once knew who “gave his life for Ireland,” as an inscription beneath his name on his headstone clarifies. Reading the epitaph, Catherine wonders what musical composition might best represent the militant nationalism for which her former classmate sacrificed his life.

In the same spring that John Hume's seminal article appeared in the Irish Times (May 1964) advocating for nonviolent means of addressing a growing crisis in Northern Ireland, Wendell Berry published his first book of poetry. Accompanied by stunning illustrations, the book was comprised of a single elegy, “November Twenty Six Nineteen Sixty Three,” which first appeared the previous December in The Nation. At the same time, profound social change was occurring in Ireland, Northern Ireland and America that would redefine the relationships between all three—and between all three and Britain.

In America on October 1, 1962, after the governor of Mississippi defied a Supreme Court order and a riot ensued that required the National Guard to subdue, James Meredith became the first African American to matriculate at the University of Mississippi. Some 250,000 civil rights marchers traveled to Washington the following August, where Martin Luther King Jr.'s “I Have a Dream” speech reverberated through the Lincoln Memorial. In the summer of 1964, the Civil Rights Act was passed, with the Voting Rights Act signed into law a year later. The impact of these events was enormous, and it was not confined to America.

Introduction

In the evolving discourse on artificial intelligence (AI), the quest for strong AI (Searle, 1980) remains paramount. This chapter seeks to elucidate the foundational assumptions that underpin philosophers’ and computer scientists’ assertions about the prerequisites for achieving strong AI. The current trajectory in AI, notably within computer science, is increasingly influenced by Google Brain's white paper on the Transformer architecture (Vaswani et al. 2017), a groundbreaking deep-learning model that reshaped the scientific landscape of Natural Language Processing (NLP) and Machine Learning (ML). The Transformer architecture forms the backbone behind the development of Large Language Models (LLMs) such as OpenAI's ChatGPT and Google's BERT. The Transformer architecture emphasizes the importance of handling sequential data through self-attention mechanisms, reflecting the human ability to emphasize certain textual elements over others based on context. In this chapter, I refer to these LLMs as ‘Statistical AI’, since Transformer architecture relies heavily on statistical methods when modelling these self-attention mechanisms. LLMs have not only achieved immediate commercial success and cultural impact for the tech industry, but the world's leading computer scientists like Ilya Sutskever also believe that they could be ‘slightly conscious’ (Sutskever 2022).

Contrasting this prevalent view, the chapter turns to the critiques offered by philosophers Noam Chomsky and Robert Brandom. Chomsky's internalist critique underscores the limitations of purely statistical models in capturing authentic human linguistic practices. Statistical models of reasoning have great practical use cases but are irrelevant to science. They are not proper models of reasoning, for human linguistic practices do not require agents to look up probability tables of what word should be used in an utterance (Chomsky et al. 2023). Conversely, Brandom's externalist perspective emphasizes the need for AI to engage in autonomous discursive practices (ADPs). ADPs are practices that are regulated by norms that are implicit within the practice itself and not regulated by external factors (i.e. training data in an AI context). ADPs involve making inferences when deployed within the context of communication or actions with other agents, while also allowing for self-correction when compared to the norms the ADP is grounded on and even correcting norms themselves (Brandom 2006).

Introduction

Wittgenstein's ideas are a common ground for developers of Natural Language Processing (NLP) systems and linguists working on Language Acquisition and Mastery (LAM) models (Mills 1993; Lowney et al. 2020; Skelac and Jandrić 2020). In recent years, we have witnessed a fast development of NLP systems capable of performing tasks as never before. NLP and LAM have been implemented based on deep-learning neural networks, which learn concept representation from rough data but are nonetheless very effective in tasks such as question answering, textual entailment and translation (Devlin et al. 2019; Kitaev, Cao, and Klein 2019; Wang et al. 2019). In this chapter, I will debate some Wittgensteinian concepts that impact the architectures of many NLP deep-learning systems. I will focus, in particular, on the attempt to build a specific kind of architecture to model a private language. The discussion, I think, helps extract philosophical assumptions leading the research and development of AI systems capable of language modelling. In this chapter, I will address some of the main features of NLP systems used for word embedding and one proposal to manipulate through a neural network a form of private language (Lowney et al. 2020).

In ‘The Private Language Argument’, I will reconstruct the complex path of the private language argument (PLA). In ‘Connectionist Language Models in NLP’, I will discuss connectionist language models and introduce notions about NLP systems’ architecture. An overview of this kind of model is helpful to introduce the work of Lowney et al. (2020). They submit that their model can respond to the issues raised by Wittgenstein in the famous PLA. This argument unexpectedly turned out to be relevant not only for the philosophy of language but also for NLP and LAM modellers. I will describe the language game concept in NLP, how it is embedded, and its role in inductive systems development. This central concept in Wittgenstein's work is relevant to describe the role of context in understanding the meanings of words. In ‘Wittgenstein and Connectionism’, I present the Wittgensteinian main concepts at play in the connectionist paradigm. I argue that the connectionist theoretical framework can better catch the dependency of word meaning on context.

The point of confronting the past is to deal with its effects in the present. That it is possible to confront the past presupposes that we are not determined by it but have some leeway to distinguish ourselves in our present from the past's multifaceted shaping power.

The key ethical claim of this book is that while we are inescapably shaped by our history, we are not imprisoned by it unless we choose to be. In Nietzsche's words, we can brace ourselves against it: we can push back. That ethical claim was treated in a general philosophical fashion in Chapter 1 and applied to specific historical details in Chapters 3 and 4. Chapter 5 addressed the theme of developing an ethics of political memory. This concluding chapter discusses the acceptance of nationalist, unionist and other identities, recognition of those identities and construction of a new political community open to updating and reinterpretation, not dissolution, of those identities.

Nationalist leaders occasionally show awareness that such is required. But their comments to that effect are couched in vague, abstract or poetic language, reflecting fear to enrage the ultra-nationalist SF-tending enragés by mild criticism of the 1916 leaders and other dead republicans. This is a failure in leadership and a refusal of Nietzsche's challenge. Specific items in the past, above all in the events of the 1912–23 period, must be reinterpreted with an eye to present needs.

Nietzsche claimed that history must be used for the purposes of life: in Ireland today, north and south, that purpose is political reconciliation, enough acceptance and mutual recognition between nationalists, unionists and others so that a live-and-let-live political community can be built. That can't be done with uncritical following of an unqualified version of the political philosophy of the 1912 Ulster Solemn League and Covenant or the 1916 Proclamation. The 1998 Agreement's political philosophy clashes with both, and the issue is: which shall we follow?

Can Machines Think?

The philosophy of AI encompasses epistemological, psychological, ontological, technical and ethical issues. Even though these matters have different natures and theoretical implications, they are closely related to the fundamental problem in the philosophy of AI – whether machines can think.

The question ‘Can machines think?’ has received two plausible answers captured in a now-standard distinction in the field, namely, the weak and strong conceptions of AI. In this chapter, the former represents the view of AI as a valuable tool that simulates but does not display mentality (see Searle 1980, 417). Strong AI represents the conception of AI as being itself a mind rather than merely a set of simulating devices. In strong AI, as the programs are themselves minds and display cognitive states, their workings directly explain the functioning of the human mind (see Searle 1980, 417). In this regard, both conceptions of AI relate to different uses of psychological language. In weak AI, psychological language is applied to machines not literally but figuratively – it is as if machines learn, think or perceive, but they really don’t. In contrast, the strong view of AI is related to literal or ‘primary’ uses of psychological language – machines do (or in principle could) think, learn and perceive in the way humans do. Most of the answers to the question ‘Can machines think?’ lie either on one or the other side of the dichotomy and are based on different theories with specific ontological commitments, for instance, dualism, functionalism, biological naturalism, identity theory and the computational theory of the mind. Consider the following quote.

According to this perspective, machines can display actual mental powers given that the nature of mentality can be instantiated in artificial devices. Strong AI might emerge from different philosophical sources.

Acknowledgments

Introduction

In 1912, Ireland was a united country, part of the UK, governed directly from Westminster. By 1922, it was divided into Northern Ireland with Home Rule status in the UK and the Irish Free State with dominion status in the British Empire (or Commonwealth). Almost none of those whose political leadership had led to that outcome had desired it in 1912.

Nationalists had wanted independence, sovereignty or at least self-governance, and that was what most got. Their desire that Ireland not be partitioned was frustrated. Unionists had wanted no devolution from Westminster, and that desire was not fulfilled; they had wanted not to be ruled by nationalists, and those in the north-east achieved that.

Hardly any nationalist or unionist leaders had given thought to what it would mean to govern. Northern unionists began to envisage the prospect of self-governance for their part of Ireland only from 1914; but they thought only of defending the perimeter, not of what it would mean to govern with a large recalcitrant nationalist minority. Failure to plan for governing a minority opposed to Home Rule was a more striking failure on the nationalist side, since they had worked for self-government for decades. They had constantly complained of British government neglect of Ireland, so more could have been expected of them in the 1890–1920 period as regards how they would govern better. They were so focussed on the issue of who should govern that they neglected the issue of how to govern, taking it as axiomatic that a native government would deliver better governance (Hoppen 2016, 11–62).

Why Wittgenstein and Artificial Intelligence?

Ludwig Wittgenstein (1889–1951) is widely regarded as one of the most significant philosophers of the twentieth century. His influence has been deep and wide-ranging, extending well beyond philosophy. Alongside a lasting impact in areas where he wrote extensively (the philosophies of language, logic, mathematics, mind and psychology, as well as metaphysics, and the theory of knowledge), Wittgenstein's philosophy has continued to influence areas beyond his focus (e.g. aesthetics, ethics and jurisprudence). Many of the themes of his work bear – either directly, or indirectly – on issues that arise in (both scientific and commercial) attempts to produce artificial intelligence (AI).

The link between Wittgenstein and AI should perhaps not be a surprising one. During his life, Wittgenstein interacted with Alan Turing (1912–1954), considered by many to be the founder of AI as an area of inquiry: Turing attended Wittgenstein's 1939 lectures in Cambridge on the foundations of mathematics (Wittgenstein 1976: LFM; see also Copeland 2012, 32–34; and see Floyd 2019 for an account of what their mutual intellectual influence may have been). And years earlier, in the Blue Book of 1933, Wittgenstein had even discussed the central animating question of AI, ‘Is it possible for a machine to think?’ (Wittgenstein 1958: BB, 47).

There is therefore ample reason to suspect both that historical investigations of Wittgenstein's work and the context in which it was situated can reveal the intellectual landscape at the dawn of AI, and that philosophical engagement with his work might shed light on important issues in the theory and application of AI. The present collection touches on the former, historical variety of inquiry (see especially Proudfoot's contribution), but it focuses primarily on the latter, more philosophical project.

Why Now?

This collection is not the first work to explore the interaction between Wittgenstein and AI. However, the last concentrated look at the topic was published over a quarter of a century ago: Shanker's (1998) single-authored monograph, Wittgenstein's Remarks on the Foundations of AI. Since then, there have been significant advances in AI – most notably, the advent of big data and the use of deep neural networks for machine learning (ML), which underpin recent generative AI systems (such as ChatGPT, Dall-E and Midjourney) – as well as changes in Wittgenstein scholarship. So the time is ripe for bringing the two together afresh.