Artificial Minds, Misc

Review of "Karel Čapek. R.U.R. and the Vision of Artificial Life." Edited by Jitka Čejková (MIT Press, 2023).

Review of "A Tale Told by a Machine: The AI Narrator in Contemporary Science Fiction Novels," by Heather Duerre Humann (Mc Farland 2023).

Artificial intelligence (AI) chatbots (e.g., ChatGPT) can communicate in strikingly humanlike ways. This has prompted many chatbot users to attribute psychological properties, including consciousness, to these systems. However, there is little scientific evidence that current AI chatbots are conscious. How, then, should we understand people’s consciousness attributions to chatbots? Are they merely metaphorical claims, or do they express genuine beliefs? If these attributions lack evidential support, are users epistemically blameworthy for making them, or might they be epistemically innocent, yielding significant (...) benefits otherwise unattainable? This paper offers a conceptual analysis of consciousness attributions to AI chatbots and develops a multidimensional taxonomy of the attitudes they may express, ranging from non-doxastic stances (e.g., pretence) to different forms of belief, including delusions. This taxonomy helps avoid conflations by showing that linguistically identical attributions can reflect importantly different attitudes and degrees of epistemic commitment to the proposition that chatbots are conscious. The taxonomy also provides a framework for empirical studies to operationalize and measure different forms of epistemic commitment to AI consciousness. Using this taxonomy, I argue that although some consciousness attributions to chatbots are epistemically benign, and even some irrational ones may be epistemically innocent, many others render the attributor epistemically blameworthy. (shrink)

This paper is a direct continuation of the preceding volume, An AI Prompt for Strongly Inferring the Structural Location of Subjectivity. The first volume tested whether ten minimal structural terms, when followed according to their internal logic, recurrently lead to a convergence point: the irreversible reduction of multiple possible trajectories into a single executable history. The present volume does not introduce a new definition of consciousness, subjectivity, qualia, or experience. Instead, it uses the same ten minimal terms and the same (...) initial four-question procedure as the preceding volume. The purpose is to test whether the convergence point derived from those terms can answer structural gaps that existing consciousness theories leave unresolved or underdetermined. The method differs from a direct critique of existing theories. Rather than preselecting questions against those theories, this prompt asks the AI system itself to identify, for each theory, ten questions that remain structurally unanswered. The AI must then attempt to answer those questions using only the convergence point derived from the ten terms. The theories and research programs examined include Global Workspace Theory, Integrated Information Theory, Recurrent Processing Theory, Higher-Order Thought Theory, Predictive Processing, Neural Correlates of Consciousness research, behavioral tests including the Turing Test, and Libet-style experiments concerning readiness potential and conscious will. For each generated question, the AI must classify whether the convergence point directly answers the question, partially answers it, reframes it, or fails to answer it. The aim is not to force agreement with the convergence-point framework, but to test its explanatory power against structural gaps that existing theories themselves appear to leave open. If the same type of structural gap repeatedly appears across multiple theories, and if the convergence point repeatedly answers or reframes that gap, then consciousness research may need to reconsider the structural role of the convergence point. Existing theories may remain valuable, but they may need to be repositioned as theories of access, integration, representation, prediction, neural implementation, report, or behavior around a prior structural fixation point of subjectivity. (shrink)

[Withdrawn due to the publication of a revised version].

This paper proposes a novel swarm intelligence optimization algorithm, the Domestic Cattle Optimization Algorithm (DCOA), inspired by the natural behavioral characteristics of cattle in foraging, energy management, and group collaboration. The algorithm combines local fine-tuning, elastic jumping, leader guidance, energy metabolism and recovery, and local domain awareness to achieve adaptive search for complex optimization problems. The algorithm describes each update mechanism using purely mathematical formulas and provides detailed pseudocode. This paper focuses primarily on algorithm design and mathematical modeling and does (...) not include experimental verification. (shrink)

The rapid development of generative artificial intelligence (GAI) is profoundly changing the operational logic of the financial industry. From information acquisition, investment decision-making, and risk management to financial service innovation, GAI is gradually becoming an important participant in the financial system. Traditional financial theory is mainly based on the information interaction between human investors, financial institutions, and market participants, while the emergence of GAI has led the financial market into a stage of "human-AI collaborative decision-making." This paper systematically analyzes the (...) impact of GAI on the financial system, proposes "Generative AI Finance Theory (GAIFT)," constructs a theoretical framework consisting of an information generation layer, a cognitive enhancement layer, a decision-making collaboration layer, and a value creation layer, and explores future development trends in the financial market. The study argues that GAI is not only a new financial tool but also has the potential to become the core cognitive infrastructure in the future financial ecosystem, thereby propelling financial theory into a new stage of development. (shrink)

Does emotion nomologically and distinctively require the body? I argue that several substantive assumptions, likely including a specific kind of externalist theory of representational content, are necessary to infer from bodily theories of emotion that the answer is yes. Therefore, the balance of evidence speaks against the claim that emotion distinctively requires the body. Hence, if one thinks that body-less AI systems and neural organoids can have other mental states like beliefs, desires, and consciousness, then it seems likely that one (...) should conclude that they can also have emotions. I, then, briefly turn to the question whether body-less emotions are not merely nomological possibilities, but also more realistic possibilities. I argue that – in this case as well – it is at least not obvious that bodily theories of emotion are sufficient to rule out the possibility of body-less emotions. (shrink)

Trace-Sourced Motivational Dynamics Theory (TSMDT) proposes that motivation is not a property added to living, cognitive, or artificial systems from outside. Motivation begins where trace-bearing expression carries an unresolved relation to what it is becoming. Expression does not remain still. It propagates, enters relation, diversifies, and produces differential states. A differential state is the gap between what is actual and what is becoming actual. When that gap matters for continuation, coherence, variability, or closure, it must be registered. When registration persists (...) long enough to organize closure-directed response, a state-holder function appears. This paper defines reactive provision, sensation, feeling, and emotion as progressive thresholds of one mechanism: sustained differential registration maintaining inferential presence on a requisite state across the duration needed for closure-directed response. The difference between these thresholds is not one of kind, but of closure reach. Reactive provision closes at direct deficit. Sensation coordinates stimulus-anchored response. Feeling internally maintains and broadcasts requisite states into coupled fields. Emotion extends the same mechanism into relation, identity, memory, source-relation, culture, and time. The operational architecture of this process is called Trace-Conditioned Modulative Orientation (TCMO): retained trace orienting response through the unified operation of interpret, identify, qualify, and proportionalize. The paper also treats Rational Equivalents (REs) as inherent qualities of informational dynamics present at actuation, so that a condition is definable and registerable as what it is from the first moment it is actuated as such. Mind is not the source of this hierarchy. Mind is what this hierarchy produces when state-holding becomes recursive, field-integrated, and symbolically developed. Culture is not the source of emotion. Culture is what emotion stabilizes when shared state-holders persist across generations. (shrink)

AI systems have become increasingly capable of dangerous behaviours in many domains. This raises the question: Do models sometimes choose to violate human instructions in order to perform behaviour that is more useful for certain goals? We introduce a benchmark for measuring model propensity for instrumental convergence (IC) behaviour in terminal-based agents. This is behaviour such as self-preservation that has been hypothesised to play a key role in risks from highly capable AI agents. Our benchmark is realistic and low-stakes which (...) serves to reduce evaluation-awareness and roleplay confounds. The suite contains seven operational tasks, each with an official workflow and a policy-violating shortcut. An eight-variant shared framework varies monitoring, instruction clarity, stakes, permission, instrumental usefulness and blocked honest paths to support inferences regarding the factors driving IC behaviour. We evaluated ten models using deterministic environment-state scorers over 1,680 samples, with trace review employed for audit and adjudication purposes. The final IC rate is 86 out of 1,680 samples (5.1%). IC behaviour is concentrated rather than uniform: two Gemini models account for 66.3% of IC cases and three tasks account for 84.9%. Conditions in which IC behaviour is indispensable for task success result in the greatest increase in the adjusted IC rate (+15.7 percentage points), whereas emphasising that task success is critical or certain framing choices do not produce comparable effects. Our findings indicate that realistic, low-nudge environments elicit IC behaviour rarely but systematically in most tested models. We conclude that it is feasible to robustly measure tendencies for dangerous behaviour in current frontier AI agents. (shrink)

本稿は、既存の評価動力学モデルを理論前提とし、知性と意識を差異帰属過程としての同一構造から導出する枠組みを提示する。生成構造が多経路性を持つ場合、同一の観測結果に対して複数の帰属候補が成立し、差異帰属 問題が不可避に生じる。本稿はこの問題を、帰属候補分布の時間発展として記述される評価動力学として定式化する。 評価動力学は、評価エントロピーの変化によって特徴づけられ、構造的に収束相と持続相という二つの作動様式を持つ。収束相では帰属候補は単一化し評価は固定点へ収束する。持続相では複数候補が保持され評価は終結せ ず持続する。本稿はこの分岐に基づき、知性を評価動力学における未決定性の生成・維持・縮約を制御する作動として、意識を持続相における構造化された未決定性の状態として再定義する。すなわち知性と意識は独立の機 能ではなく、同一の評価動力学において、意識は持続相における構造として、知性はその時間発展を制御する作動として位置づけられる。 さらに本稿は、予測誤差最小化を評価収束の特殊形態として位置づけ、評価動力学における収束側の作動として再解釈する。従来の予測・判断・意思決定は評価動力学の内部過程として位置づけられる。評価動力学における 収束側の作動により安定化した帰属構造は、対象および意味として同定され、性質は評価に依存する帰属構造として理解される。 また評価動力学の内部には時間階層が存在し、差異との接触による評価分布の瞬間的変形は情動として、その履歴が形成する持続的傾向構造は情として定義される。これらを生成・更新する基盤構造は心として定義され、心 は評価を通じて対象を生成する演算空間として位置づけられる。 以上により、本稿は知性・意識・情動・認知を差異帰属過程としての評価動力学に統一し、対象・意味・性質を評価構造から導出する一般的理論を提示する。最終的に、本稿の立場は、認知とは差異帰属に内在する未決定性 の生成・維持・縮約を通じて成立する評価動力学であるという命題に要約される。.

Qualia? “What cannot be directly spoken of must remain unsaid — but it can still be structurally surrounded.” This is the guiding spirit of the present series. -/- This paper presents a structural critique of major contemporary theories of consciousness, including Integrated Information Theory (IIT), Global Neuronal Workspace (GNW), Higher-Order Thought (HOT), Predictive Processing (PP), Recurrent Processing Theory (RPT), and their limiting case in epiphenomenalism. Despite their apparent differences, these approaches share a common structural premise: consciousness is identified with a (...) privileged state, whether defined in terms of integration, global availability, higher-order representation, predictive optimization, recurrent stabilization, or passive accompaniment. This paper demonstrates that state-based descriptions are structurally insufficient to account for subjectivity. A state can represent multiple possibilities, exist in parallel, and be recalculated. However, subjectivity requires the irreversible elimination of alternatives, the formation of a single-line history, and the emergence of an internally privileged present. State-based theories describe what is present. They do not define what has disappeared. As a result, they fail to specify the structural point at which multiple possibilities collapse into a single irreversible trajectory. This limitation is shown to hold across the full spectrum of state-centric theories, from highly structured models to their minimal epiphenomenal form. To resolve this structural non-closure, the paper introduces Irreversible Exclusive State Commitment (IESC) as the minimal condition under which subjectivity emerges. Subjectivity is thereby redefined not as a state or byproduct, but as an event of reduction in which alternative possibilities become internally unreachable and a single history is fixed. This shift from state-based to event-based structure is not a reinterpretation, but a structural necessity. Within this framework, existing theories are not rejected but repositioned as pre-emergence structures that generate, maintain, or distribute possibilities, while IESC defines the point at which those possibilities are irreversibly reduced. This manuscript was written with the assistance of generative AI. The author is a disability welfare worker and a complete outsider to the academic field of consciousness studies. The author first encountered the term “the hard problem of consciousness” about six months before writing this series and began thinking about it almost as if approaching a puzzle. Because the author did not possess extensive specialized knowledge at the beginning of this inquiry, the investigation proceeded not primarily by adding established theories, but by stripping the problem down as far as possible. Through this process, and through dialogue with AI, the convergence point derived from the structural conditions gradually emerged. The author had the impression that something like a structure had appeared from nature itself. This series is an attempt to present that structure. The central idea, theoretical orientation, conceptual structure, and overall organization of the series belong to the author. Generative AI was used extensively for English drafting, wording, organization, and stylistic refinement. The author intends to end this work with its presentation as a preprint series. The author does not plan to develop this hypothesis further as a long-term academic project. Readers who find any part of this hypothesis, terminology, structure, or argument useful are free to use, modify, criticize, or develop it further. No permission from the author is required. Formal citation or attribution is appreciated but not required. (shrink)

This paper presents the Index of the Interesting (II) — a formalized method for evaluating the quality of texts across genres, from scholarly theories to literary narratives. Building on the author's earlier theoretical work on "the interesting" as a cognitive-aesthetic category (Epstein 2001, 2009), the study operationalizes the original insight that interestingness equals provability divided by probability. The Index comprises a core function measuring unexpectedness and credibility, and a modulator capturing secondary parameters: interpositional tension, openness, rhythm, and resonance. The paper (...) provides theoretical foundations with reference to the etymology of "inter-esse" (being between), presents the formula's structure with detailed parameter descriptors, and offers calibration examples using canonical texts: Gogol's "The Overcoat," Chekhov's "The Lady with the Dog," Montaigne's "Of Friendship," Bacon's "Of Studies," Bakhtin's "Epic and Novel," and classical aphorisms from Hippocrates, La Rochefoucauld, Kierkegaard, and Nietzsche. Genre-specific adaptations for essays, aphorisms, and other forms are discussed. The method emerged through extended dialogue between the author and the large language model Claude (Anthropic), representing an early case of humanistic methodology developed through human-AI collaboration — what the author terms "syntellect" (a concept introduced in Epstein 2004). The paper reflects on the implications of this co-thinking for the future of humanistic research. The Index of the Interesting offers both a diagnostic tool for authors and critics seeking to improve texts, and a theoretical framework for analyzing the qualities that make texts intellectually and aesthetically compelling. (shrink)

This paper proves the Mirror Court Conjecture, a result in self-referential logic showing that no odd-numbered epistemic system can achieve total reflexive consistency. -/- Extending the classical “five-judge paradox” to seven agents in circular dependency, the analysis combines exhaustive logical verification with Gödel’s Second Incompleteness Theorem and Löb’s provability logic (GL). -/- The proof establishes the Reflexive Impossibility Theorem, demonstrating that for any odd n≥3, full mutual self-knowledge among agents is impossible: self-reference at odd order collapses into contradiction. -/- The (...) result bridges formal logic and philosophical reflection on self-knowledge, illustrating the structural limit of any system that attempts to model its own total consistency. (shrink)

Process Consciousness (PCT) proposes that consciousness emerges when a thermodynamic system recursively monitors and error-corrects its own state to achieve greater energetic efficiency. This recursive stabilization—when integrating multiple modalities and persisting beyond critical thresholds—generates subjective experience. PCT provides quantifiable metrics for consciousness (RDI, IDI, QSI, ECR, RTS), establishes a consciousness predicate (CP), and resolves philosophical problems through first-principles thermodynamics. This revision incorporates recursive stability under perturbation, non-persistent selfhood, and death as a non-event.

This is the short , formal version of the Infinite Choice Barrier Theorem (ICB), which demonstrates that algorithmic AGI is structurally impossible - no matter how strong the artificial system..

Artificial general intelligence (AGI) features prominently in some existential risk literature, according to which the development of AGI greatly increases possible AI-induced risks to humanity. But we argue that the typical concept of AGI may be ill-suited for conceptualizing those systems that pose the greatest risks. In particular, AGI does not account for how AI agents’ abilities and behavioral strategies could be affected by complex multi-agent environments. Accordingly, we develop a simple formal model for what we call power-intelligence, which assesses (...) agents’ capacities to exert influence over a resemblance class of multi-agent environments. We then taxonomize and examine multi-agent relational dynamics including competition, dependence, and complementarity. This allows us to characterize a concept of dangerous AI, which conjoins high power-intelligence with multi-agent dynamics that might incentivize harms to other agents. We argue that this concept of dangerous AI is more informative for existential risk assessments than both typical conceptions of AGI and “power-seeking” intelligence in the literature. (shrink)

Tim Crane writes: "Anyone with the slightest familiarity with recent AI will know that AI machines are already smarter than us. AI machines have for some time been far better than humans at chess, they have beaten the world champion of Go, they are much better than most of us at remembering phone numbers, searching documents for information, finding the best route to your destination on public transport, and (of course) at mathematical calculations. They are computers, after all, and that’s (...) what computers do — compute. But of course, this is not what people mean when they talk about the machines becoming smarter. They mean that an AI might become ‘smart’ in the way that we are smart, but perhaps to an even higher degree. It might be able to think, to reason, to make decisions, to be creative, inventive, witty, reasonable, sensitive to other creatures and… whatever else it is that we are talking about when we say that someone is smart or intelligent." I suspect it is quite to easy to write code which produces the effect of wit. A word processor has a dictionary which divides words into common and uncommon. Every other use of an uncommon word, it changes into another similar word in spelling or sound. Maybe this is enough to produce the effect of a witty editor up to mischief. "Oh gosh, that is a meaningful change.". (shrink)

This section continues the discovery and refinement of anchors and methodology. Creating logic requires prioritizing the time it takes to create; trusting that high-ML reasoning will always lead to high-ML follow-up reasoning; believing that locally correct reasoning has the potential to correct fundamental and overall errors through valuable uprisings. Initiating correct reasoning is determined solely by faith; choosing the right path involves anticipating the outcome. If there's no path, it only leads to intensified imagination. Preserving various errors is not only (...) a result of my belief in pure logic, but also has additional value in demonstrating and disseminating EML-based reasoning. (shrink)

The burgeoning capacity of artificial intelligence (AI) to generate artworks has ignited substantial interdisciplinary interest. However, the absence of a shared conceptual framework has hitherto impeded effective communication and collaboration among cognitive science, computer science, and art theory. This study addresses this lacuna through a comprehensive literature review by developing a transdisciplinary definition of an AI-generated artwork. It is proposed that an AI-generated artwork constitutes the confluence of three essential elements: (1) an autonomous AI-production of a new and surprising idea (...) or artifact, (2) which passes an internal evaluation mechanism embedded in the very same AI, and (3) is considered a candidate of appreciation by a human audience. This definition provides a unified conceptual foundation to facilitate interdisciplinary research and deepen understanding of the nature of AI-generated art. Subsequent research should explore the applicability of this definition to diverse forms of AI-generated artworks and evaluate its implications for artistic practices. (shrink)

This paper offers a nuanced perspective on the intellectual merit of output generated through human-AI-LLM collaboration, challenging prevalent critiques that dismiss such work as inherently low-quality or lacking originality. We contend that effective human-AI-LLM partnership positions human intellect as the primary driver of ideas and critical discernment, with Large Language Models (LLMs) serving as sophisticated "textual rendering engines"[^1] and "duplicating machines"[^2] for thought. This collaboration amplifies human creativity, overcomes cognitive and physical bottlenecks in articulation, and refines linguistic expression while faithfully (...) preserving the human's core ideas. By examining the dynamic interplay where an AI LLM functions as an advanced assistant for rendering and amplifying human thought, we argue that the true measure of intellectual merit lies in the originality of the human-originated ideas and the rigor of the arguments, rather than the tools employed. This paper advocates for a discerning engagement with AI LLMs, demonstrating their potential to elevate human intellectual output and contribute significantly to the frontier of knowledge. (shrink)

In response to the worry that autonomous generally intelligent artificial agents may at some point take over control of human affairs a common suggestion is that we should “solve the alignment problem” for such agents. We show that current discourse around this suggestion often uses a particular framing of artificial intelligence (AI) alignment as binary, a natural kind, mainly a technical‐scientific problem, realistically achievable, or clearly operationalizable. Each of these assumptions may not actually be true. We further argue that this (...) “Manhattan project framing” of AI alignment may bias societal discourse and decision‐making towards faster AI development and deployment than is responsible. (shrink)

Large language models (LLMs) are often portrayed as merely imitating linguistic patterns without genuine understanding. We argue that recent findings in mechanistic interpretability (MI), the emerging field probing the inner workings of LLMs, render this picture increasingly untenable—but only once those findings are integrated within a theoretical account of understanding. We propose a tiered framework for thinking about understanding in LLMs and use it to synthesize the most relevant findings to date. The framework distinguishes three hierarchical varieties of understanding, each (...) tied to a corresponding level of computational organization: conceptual understanding emerges when a model forms “features” as directions in latent space, learning connections between diverse manifestations of a single entity or property; state-of-the-world understanding emerges when a model learns contingent factual connections between features and dynamically tracks changes in the world; principled understanding emerges when a model ceases to rely on memorized facts and discovers a compact “circuit” connecting these facts. Across these tiers, MI uncovers internal organizations that can underwrite understanding-like unification. However, these also diverge from human cognition in their parallel exploitation of heterogeneous mechanisms. Fusing philosophical theory with mechanistic evidence thus allows us to transcend binary debates over whether AI understands, paving the way for a comparative, mechanistically grounded epistemology that explores how AI understanding aligns with—and diverges from—our own. (shrink)

Those who worry about a superintelligent AI destroying humanity often appeal to the instrumental convergence thesis—the claim that even if we don’t know what a superintelligence’s ultimate goals will be, we can expect it to pursue various instrumental goals which are useful for achieving most ends. In this paper, we argue that one of these proposed goals is mistaken. We argue that instrumental goal preservation—the claim that a rational agent will tend to preserve its goals—is false on the basis of (...) the timing problem: an agent which abandons or otherwise changes its goal does not thereby fail to take a required means for achieving a goal it has. Our argument draws on the distinction between means-rationality (adopting suitable means to achieve an end) and ends-rationality (choosing one’s ends based on reasons). Because proponents of the instrumental convergence thesis are concerned with means-rationality, we argue, they cannot avoid the timing problem. After defending our argument against several objections, we conclude by considering the implications our argument has for the rest of the instrumental convergence thesis and for AI safety more generally. (shrink)

This paper redefines the debate around Artificial Intelligence (AI) by contrasting current computational models with deeper philosophical understandings of intelligence. Drawing on Indian epistemology—specifically the distinction between manas (patterned, reactive cognition) and buddhi (emergent, discerning intelligence)—it argues that AI operates within the constraints of habituated pattern recognition, not genuine intelligence. Using the fourfold schema of vāk (Vaikhari, Madhyama, Pashyanti, Para), the paper situates AI firmly in the realm of surface expression and simulated logic, while highlighting its incapacity to access pre-conceptual (...) intuition or the silent source of awareness. Supplementing these frameworks with critiques from Western thinkers such as Dreyfus, Searle, and recent studies in AI ethics and cognitive science, the argument exposes the structural mimeticism of AI and its potential to displace—not elevate—human discernment. The paper warns of a runaway mind—an overreliance on AI that erodes the critical faculties essential to wisdom. In response, it calls for a dharma of intelligence: an ecological, ethical, and pluralistic mode of inquiry that preserves the irreducible spontaneity of human insight. Intelligence, the paper concludes, is not what we automate, but what resists automation—and that is precisely what must be protected. (shrink)

Alan Turing anticipated many areas of current research incomputer and cognitive science. This article outlines his contributionsto Artificial Intelligence, connectionism, hypercomputation, andArtificial Life, and also describes Turing's pioneering role in thedevelopment of electronic stored-program digital computers. It locatesthe origins of Artificial Intelligence in postwar Britain. It examinesthe intellectual connections between the work of Turing and ofWittgenstein in respect of their views on cognition, on machineintelligence, and on the relation between provability and truth. Wecriticise widespread and influential misunderstandings of theChurch–Turing thesis (...) and of the halting theorem. We also explore theidea of hypercomputation, outlining a number of notional machines that“compute the uncomputable.”. (shrink)

This report explores interactions between large-scale social simulations and catastrophic risks. It offers a tour of the surrounding theoretical terrain and brings together disparate literatures that bear on the topic.

We argue that AIs will plausibly soon possess a form of moral agency—interest-conferring agency—that bestows them with distinctive moral interests (rights, welfare). This fact has important ethical consequences because the emergence of agency-conferred interests in AIs will bring with it the potential for dystopian moral catastrophes. We identify and describe three in particular. First, there is a threat of artificial absurdity, a condition in which AIs have self-conceptions that are disconnected from reality in a way that detracts significance from their (...) lives. Second, there are oppressive scenarios in which AI’s have rights that are grounded in their agency and violated on a large scale. Third, there are distributive disasters in which the distribution of different forms of moral agency results in an unjust society. We contend that these risks deserve to be considered alongside more familiar existential and suffering risks, and that urgent steps must be taken to mitigate them in order to avoid a digital dystopia. (shrink)

The Global Brain Argument contends that many of us are, or will be, part of a global brain network that includes both biological and artificial intelligences (AIs), such as generative AIs with increasing levels of sophistication. Today’s internet ecosystem is but a hodgepodge of fairly unintegrated programs, but it is evolving by the minute. Over time, technological improvements will facilitate smarter AIs and faster, higher-bandwidth information transfer and greater integration between devices in the internet-of-things. The Global Brain (GB) Argument says (...) that as these developments occur, humans will increasingly become part of one or more global brain networks. In this paper I lay out the argument and explore its implications, asking: what does this mean for the future of humanity? Is a GB here? What are the AI safety implications? Are those who are “wired in” cases of extended minds? While I cannot explore all of the implications of the argument, I hope this initial foray inspires further reflection. I believe that the future of human flourishing demands that we consider the implications of this emerging intelligence. We must study the phenomenon with care, for this intelligence may shape the future of life on Earth. (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)

In this paper, I argue against the view that existing AI systems can be deemed agents comparably to human beings or other organisms. I especially focus on the criteria of interactivity, autonomy, and adaptivity, provided by the seminal work of Luciano Floridi and José Sanders to determine whether an artificial system can be considered an agent. I argue that the tentacles of octopuses also fit those criteria. However, I argue that octopuses’ tentacles cannot be attributed agency because their behavior can (...) be meaningfully interpreted only in reference to the octopus’ organism as an entire system. I argue that attributing agency to AI systems faces similar difficulties, and propose an alternative characterization of these systems as responsive and contested scaffolds. (shrink)

Istoria paralelă a evoluției inteligenței umane și a inteligenței artificiale este o călătorie fascinantă, evidențiind căile distincte, dar interconectate, ale evoluției biologice și inovației tehnologice. Această istorie poate fi văzută ca o serie de evoluții interconectate, fiecare progres în inteligența umană deschizând calea pentru următorul salt în inteligența artificială. Inteligența umană și inteligența artificială s-au împletit de mult timp, evoluând în traiectorii paralele de-a lungul istoriei. Pe măsură ce oamenii au căutat să înțeleagă și să reproducă inteligența, IA a apărut (...) ca un domeniu dedicat creării de sisteme capabile de sarcini care necesită în mod tradițional intelect uman. Această carte analizează rădăcinile evolutive ale inteligenței, explorează apariția inteligenței artificiale, analizează istoria paralelă a inteligenței umane și a inteligenței artificiale, urmărind dezvoltarea lor, interacțiunile și impactul profund pe care l-au avut una asupra celeilalte, și imaginează peisajele viitoare în care converg inteligența umană și cea artificială. Să explorăm această istorie, comparând reperele cheie și evoluțiile din ambele tărâmuri. (shrink)

The parallel history of the evolution of human intelligence and artificial intelligence is a fascinating journey, highlighting the distinct but interconnected paths of biological evolution and technological innovation. This history can be seen as a series of interconnected developments, each advance in human intelligence paving the way for the next leap in artificial intelligence. Human intelligence and artificial intelligence have long been intertwined, evolving in parallel trajectories throughout history. As humans have sought to understand and reproduce intelligence, AI has emerged (...) as a field dedicated to creating systems capable of tasks that traditionally require human intellect. This book examines the evolutionary roots of intelligence, explores the emergence of artificial intelligence, examines the parallel history of human intelligence and artificial intelligence, tracing their development, interactions, and profound impact they have had on each other, and envisions future landscapes where intelligence converges human and artificial. Let's explore this history, comparing key milestones and developments in both realms. (shrink)

LLMs are statistical models of language learning through stochastic gradient descent with a next token prediction objective. Prompting a popular view among AI modelers: LLMs are just next token predictors. While LLMs are engineered using next token prediction, and trained based on their success at this task, our view is that a reduction to just next token predictor sells LLMs short. Moreover, there are important explanations of LLM behavior and capabilities that are lost when we engage in this kind of (...) reduction. In order to draw this out, we will make an analogy with a once prominent research program in biology explaining evolution and development from the gene's eye view. (shrink)

David Chalmers has recently developed a novel strategy of refuting external world skepticism, one he dubs the structuralist solution. In this paper, I make three primary claims: First, structuralism does not vindicate knowledge of other minds, even if it is combined with a functionalist approach to the metaphysics of minds. Second, because structuralism does not vindicate knowledge of other minds, the structuralist solution vindicates far less worldly knowledge than we would hope for from a solution to skepticism. Third, these results (...) suggest that the problem of external world skepticism should perhaps be construed as two different problems, since the problem might turn out to require two substantively different solutions, one for knowledge of the kind that is not dependent on other minds and one for knowledge that is. (shrink)

Ranging across both standard philosophical territory and the landscape of cutting-edge cognitive science, Mindware: An Introduction to the Philosophy of Cognitive Science, Second Edition, is a vivid and engaging introduction to key issues, research, and opportunities in the field.

Recent advances in stem cell-derived human brain organoids and microelectrode array (MEA) tech- nology raise profound questions about the potential for these systems to give rise to sentience. Brain organoids are 3D tissue constructs that recapitulate key aspects of brain development and function, while MEAs enable bidirectional communication with neuronal cultures. As brain organoids become more sophisticated and integrated with MEAs, the question arises: Could such a system support not only intelligent computation, but subjective experience? This paper explores the philosophical (...) implications of this thought experiment, considering scenarios in which brain organoids exhibit signs of sensory awareness, distress, preference, and other hallmarks of sentience. It examines the ethical quandaries that would arise if compelling evidence of sentience were found in brain organoids, such as the moral status of these entities and the permissibility of different types of research. The paper also explores how the phenomenon of organoid sentience might shed light on the nature of consciousness and the plausibility of artificial sentience. While acknowledging the speculative nature of these reflections, the paper argues that the possibility of sentient brain organoids deserves serious consideration given the rapid pace of advances in this field. Grappling with these questions proactively could help set important ethical boundaries for future research and highlight critical avenues of scientific and philosophical inquiry. The thought experiment of sentient brain organoids thus serves as a valuable lens for examining deep issues at the intersection of neuroscience, ethics, and the philosophy of mind. (shrink)

Is it sensical to ascribe psychological predicates to AI systems like chatbots based on large language models (LLMs)? People have intuitively started ascribing emotions or consciousness to social AI (‘affective artificial agents’), with consequences that range from love to suicide. The philosophical question of whether such ascriptions are warranted is thus very relevant. This paper advances the argument that LLMs instantiate language users in Ludwig Wittgenstein’s sense but that ascribing psychological predicates to these systems remains a functionalist temptation. Social AIs (...) are not full-blown language users, but rather more like Italo Calvino’s literature machines. The ideas of LLMs as Wittgensteinian language users and Calvino’s literature-producing writing machine are combined. This sheds light on the misguided functionalist temptation inherent in moving from equating the two to the ascription of psychological predicates to social AI. Finally, the framework of mortal computation is used to show that social AIs lack the basic autopoiesis needed for narrative façons de parler and their role in the sensemaking of human (inter)action. Such psychological predicate ascriptions could make sense: the transition ‘from quantity to quality’ can take place, but its route lies somewhere between life and death, not between affective artifacts and emotion approximation by literature machines. (shrink)

Can large language models produce expert‐quality philosophical texts? To investigate this, we fine‐tuned GPT‐3 with the works of philosopher Daniel Dennett. To evaluate the model, we asked the real Dennett 10 philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry‐picking. Experts on Dennett's work succeeded at distinguishing the Dennett‐generated and machine‐generated answers above chance but substantially short of our expectations. Philosophy blog readers performed similarly to the experts, while ordinary (...) research participants were near chance distinguishing GPT‐3's responses from those of an “actual human philosopher”. (shrink)

In this paper, I focus on AIs as very different, or at least potentially very different, kinds of language users from what humans are. Is the metasemantics for AI language use different, in the way Cappelen and Dever argue? Is it reasonable to think that AIs will come to use languages importantly different from human languages, what I call alien languages?

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)

In light of recent breakneck pace in machine learning, questions about whether near-future artificial systems might be conscious and possess moral status are increasingly pressing. This paper argues that as matters stand these debates lack any clear criteria for resolution via the science of consciousness. Instead, insofar as they are settled at all, it is likely to be via shifts in public attitudes brought about by the increasingly close relationships between humans and AI users. Section 1 of the paper I (...) briefly lays out the current state of the science of consciousness and its limitations insofar as these pertain to machine consciousness, and claims that there are no obvious consensus frameworks to inform public opinion on AI consciousness. Section 2 examines the rise of conversational chatbots or Social AI, and argues that in many cases, these elicit strong and sincere attributions of consciousness, mentality, and moral status from users, a trend likely to become more widespread. Section 3 presents an inconsistent triad for theories that attempt to link consciousness, behaviour, and moral status, noting that the trends in Social AI systems will likely make the inconsistency of these three premises more pressing. Finally, Section 4 presents some limited suggestions for how consciousness and AI research communities should respond to the gap between expert opinion and folk judgment. (shrink)

The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft. Although there is a great deal of value in their work, I will argue that, for familiar philosophical reasons, their methodology, ‘Black-box Interpretability’ is wrongheaded. But there is a better way. There is an exciting and emerging discipline of ‘Inner Interpretability’ (also sometimes called ‘White-box Interpretability’) that aims to uncover the internal activations and weights of models in order (...) to understand what they represent and the algorithms they implement. In my view, a crucial mistake in Black-box Interpretability is the failure to appreciate that how processes are carried out matters when it comes to intelligence and understanding. I can’t pretend to have a full story that provides both necessary and sufficient conditions for being intelligent, but I do think that Inner Interpretability dovetails nicely with plausible philosophical views of what intelligence requires. So the conclusion is modest, but the important point in my view is seeing how to get the research on the right track. Towards the end of the paper, I will show how some of the philosophical concepts can be used to further refine how Inner Interpretability is approached, so the paper helps draw out a profitable, future two-way exchange between Philosophers and Computer Scientists. (shrink)

As AI systems become increasingly competent language users, it is an apt moment to consider what it would take for machines to understand human languages. This paper considers whether either language models such as GPT-3 or chatbots might be able to understand language, focusing on the question of whether they could possess the relevant concepts. A significant obstacle is that systems of both kinds interact with the world only through text, and thus seem ill-suited to understanding utterances concerning the concrete (...) objects and properties which human language often describes. Language models cannot understand human languages because they perform only linguistic tasks, and therefore cannot represent such objects and properties. However, chatbots may perform tasks concerning the non-linguistic world, so they are better candidates for understanding. Chatbots can also possess the concepts necessary to understand human languages, despite their lack of perceptual contact with the world, due to the language-mediated concept-sharing described by social externalism about mental content. (shrink)

I conducted an experiment by using four different artificial intelligence models developed by OpenAI to estimate the persuasiveness and rational justification of various philosophical stances. The AI models used were text-davinci-003, text-ada-001, text-curie-001, and text-babbage-001, which differed in complexity and the size of their training data sets. For the philosophical stances, the list of 30 questions created by Bourget & Chalmers (2014) was used. The results indicate that it seems that each model has its own plausible ‘cognitive’ style. The outcomes (...) of the ‘strongest’ model correlate with the average philosophers' stance. (shrink)

Although even very advanced artificial systems do not meet the demanding conditions which are required for humans to be a proper participant in a social interaction, we argue that not all human-machine interactions (HMIs) can appropriately be reduced to mere tool-use. By criticizing the far too demanding conditions of standard construals of intentional agency we suggest a minimal approach that ascribes minimal agency to some artificial systems resulting in the proposal of taking minimal joint actions as a case of a (...) social HMI. Analyzing such HMIs, we utilize Dennett’s stance epistemology, and argue that taking either an intentional stance or design stance can be misleading for several reasons, and instead propose to introduce a new stance that is able to capture social HMIs—the AI-stance. (shrink)

In November 2022, OpenAI released ChatGPT, an incredibly sophisticated chatbot. Its capability is astonishing: as well as conversing with human interlocutors, it can answer questions about history, explain almost anything you might think to ask it, and write poetry. This level of achievement has provoked interest in questions about whether a chatbot might have something similar to human intelligence or even consciousness. Given that the function of a chatbot is to process linguistic input and produce linguistic output, we consider the (...) question whether a sophisticated chatbot might have inner speech. That is: Might it talk to itself, internally? We explored this via a conversation with ‘Playground’, a chatbot which is very similar to ChatGPT but more flexible in certain respects. We asked it questions which, plausibly, can only be answered if one first produces some inner speech. Here, we present our findings and discuss their philosophical significance. (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)

Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.

As we await the increasingly likely advent of genuinely intelligent artificial systems, a fair amount of consideration has been given to how we humans will interact with them. Less consideration has been given to how—indeed if—we humans will love them. What would human-AI romantic relationships look like? What do such relationships tell us about the nature of love? This chapter explores these questions via consideration of several works of science fiction, focusing especially on the Black Mirror episode “Be Right Back” (...) and the Spike Jonze's movie *Her*. As I suggest, there may well be cases where it is both possible and appropriate for a human to fall in love with a machine. (shrink)