Richard Powers’ latest novel, Playground (2024), foregrounds two realms of scientific issues in the public sphere—the oceans and AI. In that sense, it looks in large part like a follow-up to two of his previous works: The Overstory (2018), an analogous treatment of the environment as represented by forests, and Galatea 2.2 (1995), which describes the creation of a very early version of an AI. Here, the focus appears to be an elaborate scheme to fill the South Pacific with floating cities, to allow wealthy people to escape the tribulations of the real world, with the development of a Facebook-like app as a secondary theme. The exposition proceeds via three narrative streams: the stories of a software engineer, a pioneering deep ocean explorer, and a tiny Polynesian island designated as the home base for the floating cities project. As the plotlines evolve, the reader is led to anticipate how the pieces will connect up—until a totally unexpected plot reversal near the end forces a drastic reappraisal of what the book is primarily about, suggesting that despite the elaborate portrayal of risks to the oceans, AI may, in fact, well prove to be the greater threat.

Drawing on Umberto Eco’s epistemological metaphor of the open work, this paper explores the intersection of two open forms of notation, inherent scores and text scores, with generative AI. Building on the notion of inherent scores, in which the interface merges with the notation, we introduce embodied sketching, a notational approach that streamlines composition and performance with real-time neural audio synthesis (NAS). We then examine text scores in text-to-audio NAS, presenting Mouja+, a work combining real-time NAS with embodied sketches and AI-generated audio from Fluxus scores. Based on the experience of composing and performing Mouja+, we show how AI’s statistical processing of language introduces interpretative gaps between the human understanding of the scores and the model’s output and propose prompting strategies to streamline the use of text scores with text-to-audio generative AI. We continue by discussing how NAS adds to the open work through algorithmic processes that coalesce into an elusive and deferring sense of presence. Through Derrida’s notion of hauntology, we thus extend the open work into what we term the ‘haunted work’, an epistemological metaphor encompassing a growing corpus of works engaging with the tension between presence and absence as a source of openness.

This paper analyzes the 2024 Mellichamp Mind and Machine Initiative at UCSB, a pioneering AI-inclusive literary competition accepting human, AI, and hybrid works. As Head Judge, I explore key questions confronting the panel: Will AI render human writing obsolete? Can machine-generated literature exhibit creativity or remain mechanical? Are AI and human writing distinguishable? What does authorship mean in an era of AI collaboration? The paper examines judges’ interpretive frameworks, biases, and expectations, contrasting them with outcomes observed in submissions. It also considers how AI challenges traditional notions of the author and fosters new creative possibilities.

The consensus on the need to regulate artificial intelligence is clear, but the how remains elusive. Private regulation, as proposed by the tech industry itself, and state regulation, as embodied in the recent EU Artificial Intelligence Act, are two common forms of governance. We advance a third option that has received very little attention to date: professional regulation. Professional regulation is modeled after hybrid public-private regulatory structures found in medicine, such as those put forth by the American Medical Association. Such governance schemes develop both technical and ethical standards, shaping professional training, continuing knowledge, and conduct. We contend that it is the most practical means of ensuring the development of human-centered AI in an era of rapid technological change and intensely opposing views of what regulation ought to do. This article places the responsibility of acting ethically on the group that knows the technology best and can anticipate its effects: AI developers. But unlike other voluntary standards, professional regulation articulates and enforces standards to certify individuals. Professional licensing is an alternative that provides public protections based on privately developed standards that ensure the safety of AI prior to their release.

This talk examines how corpus linguistics and artificial intelligence treasure the potential to reshape contemporary language learning ecologies. It argues that the rapid normalisation of generative AI has intensified the need for pedagogical models that combine low-friction access to language support with transparent methods grounded in attested usage. Drawing on ecological perspectives and recent empirical research, the talk shows how AI-driven environments expand opportunities for language learning while creating risks related to opacity and over-reliance. Corpus linguistics, data-driven learning and corpus literacy offer a complementary foundation by providing traceable evidence, reproducible analyses, and practices that foster learners’ critical judgement. Two convergence scenarios are proposed: AI as an extension of DDL, and corpus literacy as the operational core of critical AI literacy. Together, these scenarios illustrate how open-box pedagogies can reconcile responsiveness and accountability, ensuring that AI-mediated learning remains anchored in transparent processes and empirically grounded language knowledge.

This article critically examines the integration of artificial intelligence (AI) into nuclear decision-making processes and its implications for deterrence strategies in the Third Nuclear Age. While realist deterrence logic assumes that the threat of mutual destruction compels rational actors to act cautiously, AI disrupts this by adding speed, opacity and algorithmic biases to decision-making processes. The article focuses on the case of Russia to explore how different understandings of deterrence among nuclear powers could increase the risk of misperceptions and inadvertent escalation in an AI-influenced strategic environment. I argue that AI does not operate in a conceptual vacuum: the effects of its integration depend on the strategic assumptions guiding its use. As such, divergent interpretations of deterrence may render AI-supported decision making more unpredictable, particularly in high-stakes nuclear contexts. I also consider how these risks intersect with broader arms race dynamics. Specifically, the pursuit of AI-enabled capabilities by global powers is not only accelerating military modernisation but also intensifying the security dilemma, as each side fears falling behind. In light of these challenges, this article calls for greater attention to conceptual divergence in deterrence thinking, alongside transparency protocols and confidence-building measures aimed at mitigating misunderstandings and promoting stability in an increasingly automated military landscape.

This article investigates the profound impact of artificial intelligence (AI) and big data on political and military deliberations concerning the decision to wage war. By conceptualising AI as part of a broader, interconnected technology ecosystem – encompassing data, connectivity, energy, compute capacity and workforce – the article introduces the notion of “architectures of AI” to describe the underlying infrastructure shaping contemporary security and sovereignty. It demonstrates how these architectures concentrate power within a select number of technology companies, which increasingly function as national security actors capable of influencing state decisions on the resort to force. The article identifies three critical factors that collectively alter the calculus of war: (i) the concentration of power across the architectures of AI, (ii) the diffusion of national security decision making, and (iii) the role of AI in shaping public opinion. It argues that, as technology companies amass unprecedented control over digital infrastructure and information flows, most nation states – particularly smaller or less technologically advanced ones – experience diminished autonomy in decisions to use force. The article specifically examines how technology companies can coerce, influence or incentivise the resort-to-force decision making of smaller states, thereby challenging traditional notions of state sovereignty and international security.

The Council of Europe has very recently adopted the Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law. This article provides an initial analysis of the CoE AI Convention. It emphasises the necessity of understanding the CoE AI Convention within the context of its adoption as an international treaty negotiated within the Council of Europe. This context has affected its scope in terms of how the treaty includes the regulation of the usage of AI systems by both public authorities and private actors. The detailed review of the available negotiation documents reveals that the concrete level of protection offered by the Convention has been lowered. This includes the risk-based approach, which shapes the obligations undertaken by States under the treaty. This approach is explained and contrasted with the approach under the EU AI Act. The argument that emerges is that the absence of categorisation of risk levels in the treaty is related to its higher level of abstraction, which does not necessarily imply less robust obligations. The content of these obligations is also clarified in light of the requirement imposed by the treaty of consistency with human rights law. An argument is advanced that the principles formulated in the treaty – human dignity and individual autonomy, transparency and oversight, accountability, non-discrimination, data protection, reliability, risk-management – can offer interpretative guidance for the development of human rights standards.