Large Language Models

The rapid advancement of urbanization has necessitated the creation of "smart cities," where information and communication technologies (ICT) are used to improve the quality of urban life. Central to the smart city paradigm is data integration—connecting disparate data sources from various urban systems, such as transportation, healthcare, utilities, and public safety. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current (...) landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. (shrink)

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We (...) analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems. (shrink)

Extraordinary claims about both the imminenceof superintelligent AI systems and their foreseen capabilities have gone mainstream. It is even argued that we should exacerbate known risks such as climate change in the short term in the attempt to develop superintelligence (SI), which will then purportedly solve those very problems. Here, I examine the plausibility of these claims. I first ask what SI is taken to be and then ask whether such SI could possibly hold the benefits often envisioned. I conclude (...) that we do not have sufficient reason to believe that we are close enough to developing SI capable of resolving major human problems to justify taking on substantial risks in the attempt to develop it. (shrink)

The field of AI safety seeks to prevent or reduce the harms caused by AI systems. A simple and appealing account of what is distinctive of AI safety as a field holds that this feature is constitutive: a research project falls within the purview of AI safety just in case it aims to prevent or reduce the harms caused by AI systems. Call this appealingly simple account The Safety Conception of AI safety. Despite its simplicity and appeal, we argue that (...) The Safety Conception is in tension with at least two trends in the ways AI safety researchers and organizations think and talk about AI safety: first, a tendency to characterize the goal of AI safety research in terms of catastrophic risks from future systems; second, the increasingly popular idea that AI safety can be thought of as a branch of safety engineering. Adopting the methodology of conceptual engineering, we argue that these trends are unfortunate: when we consider what concept of AI safety it would be best to have, there are compelling reasons to think that The Safety Conception is the answer. Descriptively, The Safety Conception allows us to see how work on topics that have historically been treated as central to the field of AI safety is continuous with work on topics that have historically been treated as more marginal, like bias, misinformation, and privacy. Normatively, taking The Safety Conception seriously means approaching all efforts to prevent or mitigate harms from AI systems based on their merits rather than drawing arbitrary distinctions between them. (shrink)

Craig Delancey's The Passionate Engines presents a comprehensive account of “what basic emotions reveal about central problems of the philosophy of mind” (2001, p. vii). The book discusses five major issues: The affect program theory, intentionality, phenomenal consciousness, and artificial intelligence (AI). In this essay, I would like to briefly review the major tenets in the book and then focus on its discussion of AI, which has not been reviewed in detail. I outline some of the recent developments in cognitive (...) science regarding counterfactual thinking and emotions, which I believe is beneficial for the core arguments in Delancey's book. (shrink)

Whether related to machine learning models’ epistemic opacity, algorithmic classification systems’ discriminatory automation of testimonial prejudice, the distortion of human beliefs via the hallucinations of generative AI, the inclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, or located in the interaction with conversational artificial agents epistemic injustice related to AI is a growing concern. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types (...) of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional perspective on epistemic injustice in the context of AI: generative hermeneutical erasure. I argue that this injustice that can come about through the application of Large Language Models (LLMs) and contend that generative AI, when being deployed outside of its Western space of conception, can have effects of conceptual erasure, particularly in the epistemic domain, followed by forms of conceptual disruption caused by a mismatch between AI system and the interlocutor in terms of conceptual frameworks. AI systems’ ‘view from nowhere’ epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work’s relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice. (shrink)

Understanding human behaviour, neuroscience and psychology using the concepts of ‘computer’, ‘software and hardware’ and ‘AI’ is becoming increasingly popular. In popular media and parlance, people speak of being ‘overloaded’ like a CPU, ‘computing an answer to a question’, of ‘being programmed’ to do something. Now, given the massive integration of AI technologies into our daily lives, AI-related concepts are being used to metaphorically compare AI systems with human behaviour and/or cognitive abilities like language acquisition. Rightfully, the epistemic success of (...) these metaphorical comparisons should be debated. Against the backdrop of the conflicting positions of the ‘computational’ and ‘meat’ chauvinisms, we ask: can the conceptual constellation of the computational and AI be applied to the human domain and what does it mean to do so? What is one doing when the conceptual constellations of AI in particular are used in this fashion? Rooted in a Wittgensteinian view of concepts and language-use, we consider two possible answers and pit them against each other: either these examples are conceptual metaphors, or they are attempts at conceptual engineering. We argue that they are conceptual metaphors, but that (1) this position is unaware of its own epistemological contingency, and (2) it risks committing the “map-territory fallacy”. Down at the conceptual foundations of computation, (3) it most importantly is a misleading ‘double metaphor’ because of the metaphorical connection between human psychology and computation. In response to the shortcomings of this projected conceptual organisation of AI onto the human domain, we argue that there is a semantic catch. The perspective of the conceptual metaphors shows avenues for forms of conceptual engineering. If this methodology’s criteria are met, the fallacies and epistemic shortcomings related to the conceptual metaphor view can be bypassed. At its best, the cross-pollution of the human and AI conceptual domains is one that prompts us to reflect anew on how the boundaries of our current concepts serve us and how they could be approved. (shrink)

Over the course of the twentieth century, the notion of the systematicity of thought has acquired a much narrower meaning than it used to carry for much of its history. The so-called “systematicity debate” that has dominated the philosophy of language, cognitive science, and AI research over the last thirty years understands the systematicity of thought in terms of the compositionality of thought. But there is an older, broader, and more demanding notion of systematicity that is now increasingly relevant again. (...) To recover this notion from under the shadow of the systematicity debate, I distinguish between (i) the systematicity of thinkable contents, (ii) the systematicity of thinking, and (iii) the ideal of systematic thought. I then deploy this distinction to critically evaluate Fodor’s systematicity-based argument for the language of thought hypothesis before recovering the notion of the systematicity of thought as a regulative ideal, which has historically shaped our understanding of what it means for thought to be rational, authoritative, and scientific. To assess how much systematicity we need from AI models, I argue that we must look to the rationales for systematizing thought. To this end, I recover five such rationales from the history of philosophy and identify five functions served by systematization. Finally, I show how these can be used to arrive at a dynamic understanding of the need to systematize thought that can tell us what kind of systematicity is called for and when. (shrink)

Sideloading is the creation of a digital model of a person during their life via iterative improvements of this model based on the person's feedback. The progress of LLMs with large prompts allows the creation of very large, book-size prompts which describe a personality. We will call mind-models created via sideloading "sideloads"; they often look like chatbots, but they are more than that as they have other output channels, like internal thought streams and descriptions of actions. -/- By arranging the (...) predictive power of the facts about a person, we can write down the most important facts first. This allows us to get a high level of fidelity in a person's model by writing down a finite number of facts and running it as an LLM prompt with instructions to create a person's chatbot. This instruction also includes an extensive general sideload prompt (we will call it a prompt-loader), which explains how the person's sideload should work, but it is universal and works as an operating system for different persons. -/- I created my personal sideload to test all this and it started working surprisingly quickly. We found that there are three main levels of facts about a person: core facts, long-term memory, and historical facts. Core facts should go to the main prompt of the LLM, long-term memory belongs to RAG, and historical facts – which a person often does not remember – should be only used to extract new relevant data. The most important thing in preparing data for sideloading is to get a full list of the core facts, and not to mix it with long-term memory and historical facts. The LLM-sideload project is inherently future-oriented. New state-of-the-art LLMs with large prompt capabilities emerge quarterly. This allows for the preparation of prompts that exceed current LLM capabilities, anticipating smooth execution in the following year. Below we present the key findings of this work in the form of an executive summary: Quality of sideloads is evaluated using three metrics: • Facts: Accuracy of the answers about the person's life • Vibe: How well the sideload captures the person's style and personality, as judged by the person themselves and their acquaintances • Brilliant insights: Ability to contribute unique, valuable ideas beyond the training data, while maintaining the author's style • Coarseness: Levels of details as percent of the total of recordable textual information from memory estimated to be 10000 pages. Similar to the jpeg level of compression. Estimated performance of Alexey Turchin's sideload: • Facts: 70% correct • Vibe: 20% accurate • Brilliant insights: near zero • Coarseness: 10% The project achieved these results in about one month of work. Vibe is harder to evaluate and regulate than facts. This approach focuses on informational personal identity, setting aside issues of observer identity and internal qualia. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with Signature-Based Methods. (...) The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

This paper seeks to answer the question: Can contemporary forms of artificial intelligence be creative? To answer this question, I consider three conditions that are commonly taken to be necessary for creativity. These are novelty, value, and agency. I argue that while contemporary AI models may have a claim to novelty and value, they cannot satisfy the kind of agency condition required for creativity. From this discussion, a new condition for creativity emerges. Creativity requires curiosity, a motivation to pursue epistemic (...) goods. I argue that contemporary AI models do not satisfy this new condition. Because they lack both agency and curiosity, it is a mistake to attribute the same sort of creativity to AI that we prize in humans. Finally, I consider the question of whether these AI models stand to make human creativity in the arts and sciences obsolete, despite not being creative themselves. I argue, optimistically, that this is unlikely. (shrink)

Matthias Mahlmann’s Mind and Rights (M&R) argues that the mentalist theory of moral cognition—premised on an approach to the mind most closely associated with generative linguistics—is the appropriate lens through which to understand moral judgment’s roots in the mind. Specifically, he argues that individuals possess an inborn moral faculty responsible for the principled generation of moral intuitions. These moral intuitions, once sufficiently abstracted, generalized, and universalized by individuals, gave rise to the idea of human rights embodied in such conventions as (...) the Universal Declaration of Human Rights. I am sympathetic to the fundamentals of this argument, particularly in the link between Universal Moral Grammar and the idea of human rights. I thus take my comments on M&R as an opportunity to preempt a topical challenge to the mentalist theory arising in statistical machine learning. Using M&R’s analytical theory of morality alongside the mentalist approach, I emphasize—through this preempted challenge—the significance of strong generative capacity in the moral domain, the distinction between actual acquisition of cognitive abilities and simulation of acquisition, and what Mahlmann calls the theory-dependence of data interpretation in machine learning modeling. (shrink)

The problem considered in this chapter emerges from the tension we find when looking at the design and architecture of chatbots on the one hand and their conversational aptitude on the other. In the way that LLM chatbots are designed and built, we have good reason to suppose they don't possess second-order capacities such as intention, belief or knowledge. Yet theories of conversation make great use of second-order capacities of speakers and their audiences to explain how aspects of interaction succeed. (...) As we can all bear witness to now though, at the point of use chatbots appear capable of performing language tasks at a level close to that of humans. This creates a tension when we consider something like, for example, the classic Gricean theory of implicature. On a broad summary of this type of account, to utter p and implicate q requires the reflexive occurrence of an audience supposing a speaker believes that q, and the speaker believing that their audience can determine they believe it when they utter p. So taken at face value, if a chatbot doesn’t have the capacity for belief, then either in their role as speaker or audience, they would not seem capable of either generating or comprehending implicatures. Yet on the surface it does seem that chatbots are capable of dealing with (some) implicatures, and as such it raises questions about how we should then correlate this with what we think occurs in cases of implicature with chatbots. (shrink)

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may lead (...) to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology. (shrink)

Large Language Models are built on the so-called distributional semantic approach to linguistic meaning that has the distributional hypothesis at its core. The distributional hypothesis involves a holistic conception of word meaning: the meaning of a word depends upon its relations to other words in the model. A standard objection to holism is the charge of instability: any change in the meaning properties of a linguistic system (a human speaker, for example) would lead to many changes or a complete change (...) in the entire system. We examine whether the instability objection poses a problem for distributional models of meaning. First, we distinguish between distinct forms of instability that these models could exhibit, and argue that only one such form is relevant for understanding the relation between instability and communication: what we call differential instability. Differential instability is variation in the relative distances between points in a space, rather than variation in the absolute position of those points. We distinguish differential and absolute instability by constructing two of our own smaller language models. We demonstrate the two forms of instability by showing these models change as the corpora they are constructed from increase in size. We argue that the instability that these models display is constrained by the structure and scale of relationships between words, such that the resistance to change for a word is roughly proportional to its frequent and consistent use within the language system. The differential instability that language models exhibit allows for productive forms of meaning change while not leading to the problems raised by the instability objection. (shrink)

In this paper, I examine why moral exemplars are useful and even necessary in moral education despite several critiques from researchers and educators. To support my point, I review recent AI research demonstrating that exemplar-based learning is superior to rule-based learning in model performance in training neural networks, such as large language models. I particularly focus on why education aiming at promoting the development of multifaceted moral functioning can be done effectively by using exemplars, which is similar to exemplar-based learning (...) in AI model training. Furthermore, I discuss the potential limitations and issues related to exemplar-applied moral education with findings from recent studies in AI research raising concerns about model biases and toxic outcomes. I attempt to propose ways to address the concerns regarding employing moral exemplars as well. As remedies, I suggest that autonomy-supporting deliberative and reflective learning processes should be utilized. Furthermore, based on the discussion, I examine how macroscopic socio-cultural aspects influence the effectiveness of exemplar-applied moral education. Suggestions for moral educators and future directions for research in moral education are briefly discussed. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? (...) And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (shrink)

Recent breakthroughs in artificial intelligence have primarily resulted from training deep neural networks (DNNs) with vast numbers of adjustable parameters on enormous datasets. Due to their complex internal structure, DNNs are frequently characterized as inscrutable ``black boxes,'' making it challenging to interpret the mechanisms underlying their impressive performance. This opacity creates difficulties for explanation, safety assurance, trustworthiness, and comparisons to human cognition, leading to divergent perspectives on these systems. This chapter examines recent developments in interpretability methods for DNNs, with a (...) focus on interventionist approaches inspired by causal explanation in philosophy of science. We argue that these methods offer a promising avenue for understanding how DNNs process information compared to merely behavioral benchmarking and correlational probing. We review key interventionist methods and illustrate their application through practical case studies. These methods allow researchers to identify and manipulate specific computational components within DNNs, providing insights into their causal structure and internal representations. We situate these approaches within the broader framework of causal abstraction, which aims to align low-level neural computations with high-level interpretable models. While acknowledging current limitations, we contend that interventionist methods offer a path towards more rigorous and theoretically grounded interpretability research, potentially informing both AI development and computational cognitive neuroscience. (shrink)

Humans have distinctive powers of reflection: no other animal seems to have anything like our capacity for self-examination. Many philosophers hold that this capacity has a uniquely important guiding role in our cognition; others, notably Hilary Kornblith, draw attention to its weaknesses. Kornblith chiefly aims to dispel the sense that there is anything ‘magical’ about second-order mental states, situating them in the same causal net as ordinary first-order mental states. But elsewhere he goes further, suggesting that there is something deeply (...) misleading about reflection’s first-person view of cognition. This chapter takes a closer look at the conditions under which reflection is misleading. Even if humans are the only reflective animals, we are no longer the only creatures that can be questioned about their claims. Chatbots can also be asked to explain themselves, and by studying the conditions under which they produce clarification as opposed to confabulation, we can develop a non-skeptical account of the epistemic value of reflection. I argue that this capacity naturally supports the distribution of complex problem solving among multiple agents. (shrink)

Artificial intelligence chatbots driven by large language models (LLMs) have the potential to increase public science literacy and support scientific research, as they can quickly summarize complex scientific information in accessible terms. However, when summarizing scientific texts, LLMs may omit details that limit the scope of research conclusions, leading to generalizations of results broader than warranted by the original study. We tested 10 prominent LLMs, including ChatGPT-4o, ChatGPT-4.5, DeepSeek, LLaMA 3.3 70B, and Claude 3.7 Sonnet, comparing 4900 LLM-generated summaries to (...) their original scientific texts. Even when explicitly prompted for accuracy, most LLMs produced broader generalizations of scientific results than those in the original texts, with DeepSeek, ChatGPT-4o, and LLaMA 3.3 70B overgeneralizing in 26–73% of cases. In a direct comparison of LLM-generated and human-authored science summaries, LLM summaries were nearly five times more likely to contain broad generalizations (OR = 4.85, 95% CI [3.06, 7.70], p < 0.001). Notably, newer models tended to perform worse in generalization accuracy than earlier ones. Our results indicate a strong bias in many widely used LLMs towards overgeneralizing scientific conclusions, posing a significant risk of large-scale misinterpretations of research findings. We highlight potential mitigation strategies, including lowering LLM temperature settings and benchmarking LLMs for generalization accuracy. (shrink)

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I argue (...) that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest. (shrink)

This paper explores the intersection of AI hallucinations and the question of AI consciousness, examining whether the erroneous outputs generated by large language models (LLMs) could be mistaken for signs of emergent intelligence. AI hallucinations, which are false or unverifiable statements produced by LLMs, raise significant philosophical and ethical concerns. While these hallucinations may appear as data anomalies, they challenge our ability to discern whether LLMs are merely sophisticated simulators of intelligence or could develop genuine cognitive processes. By analyzing the (...) causes of AI hallucinations, their impact on the perception of AI cognition, and the potential implications for AI consciousness, this paper contributes to the ongoing discourse on the nature of artificial intelligence and its future evolution. (shrink)

What makes safety claims about general purpose AI systems such as large language models trustworthy? We show that rather than the capabilities of security tools such as alignment and red teaming procedures, it is security practices based on these tools that contributed to reconfiguring the image of AI safety and made the claims acceptable. After showing what causes the gap between the capabilities of security tools and the desired safety guarantees, we critically investigate how AI security practices attempt to fill (...) the gap and identify several shortcomings in diversity and participation. We found that these security practices are part of securitization processes aiming to support (commercial) development of general purpose AI systems whose trustworthiness can only be imperfectly tested instead of guaranteed. We conclude by offering several improvements to the current AI security practices. (shrink)

Iglesias et al. (2025) argue that “some of the aims or ostensible goods of person-span expansion could plausibly be fulfilled in part by creating a digital doppelgänger”—that is, an AI system desig...

The rise of ChatGPT affords a fundamental transformation of the dynamics in human-technology interaction, as Large Language Model (LLM) applications increasingly emulate our social habits in digital communication. This poses a challenge to Don Ihde’s explicit focus on material technics and their affordances: ChatGPT did not introduce new material technics. Rather, it is a new digital app that runs on the same physical devices we have used for years. This paper undertakes a re-evaluation of some postphenomenological concepts, introducing the notion (...) of quasi-materiality to better understand the role that user interfaces (UIs) play in affording different stabilities in technological mediation. We propose the term “active User Interface” (aUI) to denote the specifics of how ChatGPT makes use of LLMs within its UI design to afford seamless, intuitive conversations with a quasi-other in what Ihde termed “alterity relations”. Drawing inspiration from Peter-Paul Verbeek’s work on the intelligification of our material world through (AI) technologies, our analysis leads to the formulation of a novel stability afforded through aUIs: “machinic embodiment stability”. This concept sheds light on how ChatGPT’s aUI integrates with our established habits of digitally mediated social communication. As the use of LLMs is expected to become increasingly prevalent, this provides a new perspective on the current evolution of the technological landscape. (shrink)

Under what conditions would an artificially intelligent system have wellbeing? Despite its clear bearing on the ethics of human interactions with artificial systems, this question has received little direct attention. Because all major theories of wellbeing hold that an individual’s welfare level is partially determined by their mental life, we begin by considering whether artificial systems have mental states. We show that a wide range of theories of mental states, when combined with leading theories of wellbeing, predict that certain existing (...) artificial systems have wellbeing. Along the way, we argue that there are good reasons to believe that artificial systems can have wellbeing even if they are not phenomenally conscious. While we do not claim to demonstrate conclusively that AI systems have wellbeing, we argue that there is a significant probability that some AI systems have or will soon have wellbeing, and that this should lead us to reassess our relationship with the intelligent systems we create. (shrink)

A key assumption fuelling optimism about the progress of large language models (LLMs) in accurately and comprehensively modelling the world is that the truth is systematic: true statements about the world form a whole that is not just consistent, in that it contains no contradictions, but coherent, in that the truths are inferentially interlinked. This holds out the prospect that LLMs might in principle rely on that systematicity to fill in gaps and correct inaccuracies in the training data: consistency and (...) coherence promise to facilitate progress towards comprehensiveness in an LLM’s representation of the world. However, philosophers have identified compelling reasons to doubt that the truth is systematic across all domains of thought, arguing that in normative domains, in particular, the truth is largely asystematic. I argue that insofar as the truth in normative domains is asystematic, this renders it correspondingly harder for LLMs to make progress, because they cannot then leverage the systematicity of truth. And the less LLMs can rely on the systematicity of truth, the less we can rely on them to do our practical deliberation for us, because the very asystematicity of normative domains requires human agency to play a greater role in practical thought. (shrink)

This paper provides a philosophically informed and robust account of the global justice implications of Artificial Intelligence (AI). We first discuss some of the key theories of global justice, before justifying our focus on the Capabilities Approach as a useful framework for understanding the context-specific impacts of AI on lowto middle-income countries. We then highlight some of the harms and burdens facing low- to middle-income countries within the context of both AI use and the AI supply chain, by analyzing the (...) extraction of materials, which includes mineral extraction and the environmental harms associated with it, and the extraction of labor, which includes unethical labor practices, low wages, and the trauma experienced by some AI workers. We then outline some of the potential harms and benefits that AI poses, how these are distributed, and what global justice implications this has for low- to middle-income countries. Finally, we articulate the global justice significance of AI by utilizing the Capabilities Approach. We argue that AI must be considered from a global justice perspective given that, globally, AI puts significant downward pressure on several elements of well-being thereby making it harder for people to achieve threshold levels of the central human capabilities needed for a life of dignity. (shrink)

The rapid adoption of commercial Generative Artificial Intelligence (Gen AI) products raises important questions around the impact this technology will have on our communicative interactions. This paper provides an analysis of some of the potential implications that Artificial Intelligence-Mediated Communication (AI-MC) may have on epistemic trust in online communications, specifically on social media. We argue that AI-MC poses a risk to epistemic trust being diminished in online communications on both normative and descriptive grounds. Descriptively, AI-MC seems to (roughly) lower levels (...) of epistemic trust. Normatively, we argue that this brings about the following dilemma. On the one hand, there are at least some instances where we should epistemically trust AI-MC less, and therefore the reduction in epistemic trust is justified in these instances. On the other hand, there are also instances where we epistemically trust AI-MC less, but this reduction in epistemic trust is not justified, resulting in discrimination and epistemic injustice in these instances. The difficulty in knowing which of these two groups any instance of AI-MC belongs to brings about the AI-MC dilemma: We must choose between maintaining normal levels of epistemic trust and risking epistemic gullibility when reduced trust is justified, or adopting generally reduced epistemic trust and risking epistemic injustice when such reduced trust is unjustified. Navigating this choice between problematic alternatives creates a significant challenge for social media as an epistemic environment. (shrink)

Descartes famously constructed a language test to determine the existence of other minds. The test made critical observations about how humans use language that purportedly distinguishes them from animals and machines. These observations were carried into the generative (and later biolinguistic) enterprise under what Chomsky in his Cartesian Linguistics, terms the “creative aspect of language use” (CALU). CALU refers to the stimulus-free, unbounded, yet appropriate use of language—a tripartite depiction whose function in biolinguistics is to highlight a species-specific form of (...) intellectual freedom. This paper argues that CALU provides a set of facts that have significant downstream effects on explanatory theory-construction. These include the internalist orientation of linguistics, the invocation of a competence-performance distinction, and the postulation of a generative language faculty that makes possible—but does not explain—CALU. It contrasts the biolinguistic approach to CALU with the recent wave of enthusiasm for the use of Transformer-based Large Language Models (LLMs) as tools, models, or theories of human language, arguing that such uses neglect these fundamental insights to their detriment. It argues that, in the absence of replication, identification, or accounting of CALU, LLMs do not match the explanatory depth of the biolinguistic framework, thereby limiting their theoretical usefulness. (shrink)

AI-based technologies are increasingly pervasive in a number of contexts. Our affective and emotional life makes no exception. In this article, we analyze one way in which AI-based technologies can affect them. In particular, our investigation will focus on affective artificial agents, namely AI-powered software or robotic agents designed to interact with us in affectively salient ways. We build upon the existing literature on affective artifacts with the aim of providing an original analysis of affective artificial agents and their distinctive (...) features. We argue that, unlike comparatively low-tech affective artifacts, affective artificial agents display a specific form of agency, which prevents them from being perceived by their users as extensions of their selves. In addition to this, we claim that their functioning crucially depends on the simulation of human-like emotion-driven behavior and requires a distinctive form of transparency—we call it emotional transparency—that might give rise to ethical and normative tensions. (shrink)

One of the central questions in the epistemology of conversational AIs is how to classify the beliefs acquired from them. Two promising candidates are instrument-based and testimony-based beliefs. However, the category of instrument-based beliefs faces an intrinsic problem, and a challenge arises in its application. On the other hand, relying solely on the category of testimony-based beliefs does not encompass the totality of our practice of using conversational AIs. To address these limitations, I propose a novel classification of beliefs that (...) shifts the focus from the properties of the conversational AIs themselves to how recipients perceive AI output, specifically whether they take the output at face value. The proposed categories are beliefs by regarding as instruments and beliefs by regarding as testifiers. By using these complementary categories, a more comprehensive understanding of beliefs acquired from conversational AIs can be achieved while avoiding the problems associated with the traditional classification. (shrink)

One of the central questions in the epistemology of conversational AIs is how to classify the beliefs acquired from them. Two promising candidates are instrument-based and testimony-based beliefs. However, the category of instrument-based beliefs faces an intrinsic problem, and a challenge arises in its application. On the other hand, relying solely on the category of testimony-based beliefs does not encompass the totality of our practice of using conversational AIs. To address these limitations, I propose a novel classification of beliefs that (...) shifts the focus from the properties of the conversational AIs themselves to how recipients perceive AI output, specifically whether they take the output at face value. The proposed categories are beliefs by regarding as instruments and beliefs by regarding as testifiers. By using these complementary categories, a more comprehensive understanding of beliefs acquired from conversational AIs can be achieved while avoiding the problems associated with the traditional classification. (shrink)

Emergency response times play a critical role in saving lives, especially in urban settings where traffic congestion and unpredictable events can delay ambulance arrivals. This paper explores a novel framework for smart route optimization for emergency vehicles, leveraging artificial intelligence (AI), Internet of Things (IoT) technologies, and dynamic traffic analytics. We propose a real-time adaptive routing system that integrates machine learning (ML) for predictive modeling and IoT-enabled communication with traffic infrastructure. The system is evaluated using simulated urban environments, achieving a (...) 35% reduction in response times compared to traditional methods. This work lays the foundation for future advancements in intelligent emergency systems and their integration into smart cities. (shrink)

Inchul Yum (2024) argues that the widespread adoption of language agent architectures would likely increase the risk posed by AI by simplifying the process of aligning artificial systems with human values and thereby making it easier for malicious actors to use them to cause a variety of harms. Yum takes this to be an example of a broader phenomenon: progress on the alignment problem is likely to be net safety-negative because it makes artificial systems easier for malicious actors to control. (...) I offer some reasons for skepticism about this surprising and pessimistic conclusion. (shrink)

This chapter considers the potential of artificial intelligence (AI) to exhibit creativity and imagination, in light of recent advances in generative AI and the use of deep neural networks (DNNs). Reasons for doubting that AI exhibits genuine creativity or imagination are considered, including the claim that the creativity of an algorithm lies in its developer, that generative AI merely reproduces patterns in its training data, and that AI is lacking in a necessary feature for creativity or imagination, such as consciousness, (...) agency, metacognitive awareness, or the use of mental imagery. In each case, a counterargument is provided that AI already has or soon will overcome the apparent barrier. In some instances, this is because the critical features deemed lacking in AI may not be necessary for human creativity and imagination either. In others, it is because important developmental and structural similarities between humans and generative AI have been insufficiently appreciated. (shrink)

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and (...) shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

The purpose of this paper is to show that understanding AI hallucination requires an interdisciplinary approach that combines insights from epistemology and cognitive science to address the nature of AI-generated knowledge, with a terminological worry that concepts we often use might carry unnecessary presuppositions. Along with terminological issues, it is demonstrated that AI systems, comparable to human cognition, are susceptible to errors in judgement and reasoning, and proposes that epistemological frameworks, such as reliabilism, can be similarly applied to enhance the (...) trustworthiness of AI outputs. This exploration seeks to deepen our understanding of the possibility of AI cognition and its implications for the broader philosophical questions of knowledge and intelligence. (shrink)

Except in extraordinary circumstances, patients' clinical care should reflect their preferences. Incapacitated patients cannot report their preferences. This is a problem. Extant solutions to the problem are inadequate: surrogates are unreliable, and advance directives are uncommon. In response, some authors have suggested developing algorithmic "patient preference predictors" (PPPs) to inform care for incapacitated patients. In a recent paper, Earp et al. propose a new twist on PPPs. Earp et al. suggest we personalize PPPs using modern machine learning (ML) techniques. In (...) this paper, I argue that, contrary to Earp et al.'s claim, personalized patient preference predictors are neither technically feasible nor ethically desirable. (shrink)

Many philosophers have argued that large language models (LLMs) subvert the traditional undergraduate philosophy paper. For the enthusiastic, LLMs merely subvert the traditional idea that students ought to write philosophy papers “entirely on their own.” For the more pessimistic, LLMs merely facilitate plagiarism. We believe that these controversies neglect a more basic crisis. We argue that, because one can, with minimal philosophical effort, use LLMs to produce outputs that at least “look like” good papers, many students will complete paper assignments (...) in a way that fails to develop their philosophical abilities. We argue that this problem exists even if students can produce better papers with AI and even if instructors can detect AI-generated content with decent reliability. But LLMs also create a pedagogical opportunity. We propose that instructors shift the emphasis of their assignments from philosophy papers to “LLM dialogues”: philosophical conversations between the student and an LLM. We describe our experience with using these types of assignments over the past several semesters. We argue that, far from undermining quality philosophical instruction, LLMs allow us to teach philosophy more effectively than was possible before. (shrink)

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...) competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

Communicating interdisciplinary information is difficult, even when two fields are ostensibly discussing the same topic. In this work, I’ll discuss the capacity for analogical reasoning to provide a framework for developing novel judgments utilizing similarities in separate domains. I argue that analogies are best modeled after Paul Bartha’s By Parallel Reasoning, and that they can be used to create a Toulmin-style warrant that expresses a generalization. I argue that these comparisons provide insights into interdisciplinary research. In order to demonstrate this (...) concept, I will demonstrate that fruitful comparisons can be made between Buddhism and Artificial Intelligence research. (shrink)

Formalizations serve as cognitive tools. By enabling algorithmic reasoning over sets of statements in a formal language, they provide a cognitive boost for human reasoners. We argue that the emergence of large language models (LLMs) as a technology for the analysis and generation of natural language provides a new perspective on the relative roles of formal and natural languages in formalization.

Mechanistic interpretability is the program of explaining what AI systems are doing in terms of their internal mechanisms. I analyze some aspects of the program, along with setting out some concrete challenges and assessing progress to date. I argue for the importance of propositional interpretability, which involves interpreting a system’s mechanisms and behav- ior in terms of propositional attitudes: attitudes (such as belief, desire, or subjective probabil- ity) to propositions (e.g. the proposition that it is hot outside). Propositional attitudes are (...) the central way that we interpret and explain human beings and they are likely to be central in AI too. A central challenge is what I call thought logging: creating systems that log all of the rel- evant propositional attitudes in an AI system over time. I examine currently popular methods of interpretability (such as probing, sparse auto-encoders, and chain of thought methods) as well as philosophical methods of interpretation (including those grounded in psychoseman- tics) to assess their strengths and weaknesses as methods of propositional interpretability. (shrink)

The idea that humans might one day form persistent and dynamic relationships in professional, social, and even romantic contexts is a longstanding one. However, developments in machine learning and especially natural language processing over the last five years have led to this possibility becoming actualised at a previously unseen scale. Apps like Replika, Xiaoice, and CharacterAI boast many millions of active long-term users, and give rise to emotionally complex experiences. In this paper, I provide an overview of these developments, beginning (...) in Section 1 with historical and technical context. In Section 2, I lay out a basic theoretical framework for classifying human-AI relationships and their specific dynamics. Section 3 turns to ethical issues, with a focus on the core philosophical question of whether human-AI relationships can have similar intrinsic value to that possessed by human-human relationships. Section 4 extends to the discussion of ethical issues to the more empirical matter of harms and benefits of human-AI relationships. The paper concludes by noting potentially instructive parallels between the nascent field of ‘Social AI’ and the recent history of social media. (shrink)