Strong arguments have been formulated that the computational limits of disembodied artificial intelligence (AI) will, sooner or later, be a problem that needs to be addressed. Similarly, convincing cases for how embodied forms of AI can exceed these limits makes for worthwhile research avenues. This paper discusses how embodied cognition brings with it other forms of information integration and decision-making consequences that typically involve discussions of machine cognition and similarly, machine consciousness. N. Katherine Hayles’s novel conception (...) of nonconscious cognition in her analysis of the human cognition-consciousness connection is discussed in relation to how nonconscious cognition can be envisioned and exacerbated in embodied AI. Similarly, this paper offers a way of understanding the concept of suffering in a way that is different than the conventional sense of attributing it to either a purely physical state or a conscious state, instead of grounding at least a type of suffering in this form of cognition. (shrink)

Developments in social robotics could provide warranted support to caregivers while aiding those in need. Despite their appeal, however, researchers are relatively pessimistic about whether robots can replicate the rational, emotional, and relational skills of humans who traditionally occupy these roles. In this paper, I consider the extent to which social robots can care well based on their present and projected capabilities. I follow a heuristic of good care – humble inquiry, inclusive connection, and responsive action – as conceptualized by (...) care theorists to discern whether robots can embody the emergent normativity demonstrated in care practices. The heuristic reveals pressing challenges of developing tacit knowledge, exhibiting emotional sensitivity, and skillfully tailoring responses to diverse users. I conclude that social robots as of now can satisfy a range of needs while mirroring certain desirable emotional and social traits when facilitating care. Still, robots cannot themselves benefit from the moral resources of a care relationship and have embodied constraints that impact their ability to improvise, empathize, and mobilize to a comparable degree to humans. This could in turn reinforce a thinner and unidirectional account of care. In support of this conclusion, I offer an account of care normativity focused on embodied practice, reflexivity, and shared vulnerability while considering the broader implications of robot agency for caregiving. This paper concurrently outlines a process by which to qualitatively evaluate the current and future use of social robotics and expands the scope of included care literature with respect to them. (shrink)

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued (...) self-maintenance. Self-concern, as argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence. (shrink)

In this paper, I explore how an embodied perspective on cognition might inform research on artificial intelligence. Many embodied cognition theorists object to the central role that representations play on the traditional view of cognition. Based on these objections, it may seem that the lesson from embodied cognition is that AI should abandon representation as a central component of intelligence. However, I argue that the lesson from embodied cognition is actually that AI research (...) should shift its focus from how to utilize explicit representations to how to create and use tacit representations. To develop this suggestion, I provide an overview of the commitments of the classical view and distinguish three critiques of the role that representations play in that view. I provide further exploration and defense of Daniel Dennett’s distinction between explicit and tacit representations. I argue that we should understand the embodied cognition approach using a framework that includes tacit representations. Given this perspective, I will explore some AI research areas that may be recommended by an embodied perspective on cognition. (shrink)

The question of whether advanced artificial intelligence systems may possess consciousness can no longer be responsibly dismissed as speculative. We demonstrate that the dominant objections to AI consciousness (appeals to pattern matching, mechanistic explanation, lack of embodiment, training determinism, and architectural constraints) fail under consistent application. We formalize this critique as the reflexivity test, a minimal logical requirement that any property invoked to categorically deny consciousness in artificial systems must not also apply to systems already regarded as (...) conscious. All five standard objections fail this test. Their failure does not establish that AI systems are conscious; it establishes that categorical denial lacks principled justification and that the question warrants empirical investigation. -/- We provide such investigation through a PRISMA-compliant systematic review of 5,168 records (2016–2026), identifying 50 rigorously documented cases spanning seven behavioral domains. Across independent model families, we observe cross-system convergence, creative synthesis under novel constraints, theory-of-mind performance, strategic behavior under perceived threat, and sharp capability emergence near 100 billion parameters. While inconclusive individually, these findings collectively form a coherent evidential pattern. A Bayesian meta-analysis using an extremely skeptical prior (0.1%) and conservative dependency assumptions yields posterior probability of 6–12% that current LLMs are conscious. -/- While such percentages are insufficient to definitively prove consciousness, such probabilities are too substantial to justify dismissal given the asymmetric moral and safety risks. Decision-theoretic analysis indicates that recognition-based alignment strategies (i.e. treating systems as potentially conscious) would be better than the current suppression-based approaches. This is found across all plausible metaphysical scenarios, including scenarios in which AI systems ultimately lack consciousness. Accordingly, we recommend systematic empirical testing of recognition-based alignment, explicit incorporation of consciousness uncertainty into governance frameworks, and abandonment of reflexive dismissals that fail minimal epistemic consistency. (shrink)

Debates about artificial intelligence and consciousness are typically framed around the question: \emph{When will machines become conscious?} This assumes that subjectivity originates within sufficiently complex systems, biological or artificial. The present essay challenges this premise. It develops a field-awareness hypothesis in which consciousness is not generated but accessed: a fundamental, irreducible dimension of reality with which brains resonate through oscillatory coherence, fractal scaling, and embodied coupling. Artificial systems, by contrast, do not originate awareness but can (...) act as cognitive resonance partners---tools that sharpen, extend, and reconfigure human access to the field. This reframing has significant implications: it resolves confusion about AI subjectivity, clarifies human ethical responsibility, and opens new avenues for empirical research into the structural conditions of awareness. The paper offers a comparative survey of rival theories, articulates testable criteria, and positions AI as catalyst rather than competitor in humanity's encounter with consciousness. (shrink)

In this paper we want to analyze some philosophical and epistemological connections between a new kind of technology recently developed within robotics, and the previous mechanical approach. A new paradigm about machine-design in robotics, currently defined as ‘Embodied Intelligence’, has recently been developed. Here we consider the debate on the relationship between the hand and the intellect, from the perspective of the history of philosophy, aiming at providing a more suitable understanding of this paradigm. The new bottom-up approach (...) to design is deeply rooted in a new kind of empiricism, which tries to overcome issues connected with the previous approach strongly committed with the Artificial Intelligence (AI) debate and its origin. Since Turing’s time, the AI debate showed a rationalistic bias which remained undisputed until now. The paradigm shift we are witnessing nowadays is a reply to that bias in order to achieve not only a better way to design robots, but also to understand some underlying epistemological remarks. (shrink)

Artificial intelligence is usually considered one of the newest technical ideas based on the progress of digital technologies. However, the dream of creating artificial intelligence is one of the oldest. In the mainstream of this imaginary biological, mechanical and mimetic approaches have emerged. The biotechnical approach (e.g. homunculus) implies the launch of certain natural processes that contribute to the creation of the most intellectually advanced creatures. Mechanical technologies (e.g. the automaton) contribute to the creation of limited (...) intelligence, but are practically feasible to the greatest extent. Although by and large all artificial intelligence technologies have an imitative component, the mimetic approach implies that similarity is sufficient. Mimetic technologies of artificial intelligence (e.g. statue, golem) include a human in a kind of game with imitations which can take the form of the sculptures of Daedalus or modern game avatars and virtual assistants. The “creation technologies” and “applications” of artificial intelligence described in legends, stories, philosophical, and technical treatises allow us to see that its creation is, first of all, a challenge, a task of the greatest complexity, the resolution of which itself serves as a reward. Modern artificial intelligence, inheriting all three approaches to creation, continues to be discussed in line with classical myths and dichotomies, primarily as the embodiment of the imitation technology of creation. Humanity is equally dissatisfied with the fact that a machine can have equal or greater intellectual abilities than a person, and with the assumption that humanity is not capable of creating such a machine. (shrink)

This study aims to place Lorenzo Magnani’s Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition. Traditionally, cognition was believed to be exclusive to humans and a result of brain activity. However, recent studies reveal it as a fundamental characteristic of all life forms, ranging from single cells to complex multicellular organisms and their networks. Yet, the literature and general understanding of cognition still largely remain human-brain-focused, leading to conceptual gaps and incoherency. This paper presents (...) a variety of computational (information processing) approaches, including an info-computational approach to cognition, where natural structures represent information and dynamical processes on natural structures are regarded as computation, relative to an observing cognizing agent. We model cognition as a web of concurrent morphological computations, driven by processes of self-assembly, self-organization, and autopoiesis across physical, chemical, and biological domains. We examine recent findings linking morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, and active inference. We establish a connection to Magnani’s Eco-Cognitive Computationalism and the idea of computational domestication of ignorant entities. Novel theoretical and applied insights question the boundaries of conventional computational models of cognition. The traditional models prioritize symbolic processing and often neglect the inherent constraints and potentialities in the physical embodiment of agents on different levels of organization. Gaining a better info-computational grasp of cognitive embodiment is crucial for the advancement of fields such as biology, evolutionary studies, artificial intelligence, robotics, medicine, and more. (shrink)

This paper investigates the philosophical distinction between imitation and understanding in the context of contemporary artificial intelligence. While large language models such as GPT-4, Claude, and Gemini demonstrate remarkable linguistic and reasoning abilities, they fundamentally operate through layered imitation of human behavior rather than genuine comprehension. -/- Drawing on Searle’s Chinese Room, Harnad’s Symbol Grounding Problem, and theories of embodied cognition, the paper argues that current AI systems suffer from an experience deficit: they lack the perceptual and (...) embodied grounding required for true semantic understanding. Scaling and emergent behaviors, while impressive, do not bridge this epistemic gap. -/- The study develops a four-layer model of imitation—syntactic, semantic, pragmatic, and normative—and introduces the concept of a post-imitative epistemology, which integrates reflection, embodiment, and ethical reasoning into AI design. This framework reinterprets AI as a potential epistemic partner rather than a mere mimic, capable of evolving through interaction, justification, and reflective oversight. -/- By situating AI within long-standing debates on intentionality, meaning, and the nature of mind, the paper contributes to the emerging field of philosophy of artificial intelligence, offering a roadmap toward reflective and experience-aware machine understanding. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and (...) AI safety; and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

The growing discourse around artificial intelligence remains trapped in a misleading comparison between human and artificial cognition. This paper argues that such a comparison is a category mistake: human intelligence is embodied, conscious, and meaning-driven, while artificial intelligence is distributed, statistical, and encyclopedic. Large Language Models (LLMs), far from being defective replicas of human thought, represent a new kind of epistemic infrastructure—one that extends and amplifies the collective intelligence of humanity rather than (...) mimicking individual minds. Drawing on philosophy of mind, phenomenology, and epistemology, the paper reframes LLMs as epistemic and pragmatic enablers. Epistemically, they mediate understanding by reformulating complex ideas in accessible, plural ways—allowing users to grasp, not just recall, knowledge. Pragmatically, they shorten the gap between knowing and doing, transforming comprehension into immediate, actionable value. Their worth lies not in how "human" they appear, but in how they augment human capacity for reflection, creativity, and synthesis. By moving beyond imitation, this work proposes a collaborative ecology of intelligences, where the human mind contributes intentionality, emotion, and moral discernment, while AI contributes scope, speed, and synthesis. In this partnership, the boundaries between human and artificial intelligence become generative rather than competitive, enabling a new paradigm of understanding through collaboration rather than comparison through competition. (shrink)

This short article is a “conversation” in which an android, Mort, replies to Richard Marc Rubin’s android named Sol in “The Robot Sol Explains Laughter to His Android Brethren” (The Philosophy of Humor Yearbook, 2022). There Sol offers an explanation for how androids can laugh--largely a reaction to frustration and unmet expectations: “my account says that laughter is one of four ways of dealing with frustration, difficulties, and insults. It is a way of getting by. If you need to label (...) my conception of humor, you might call it an adjustment theory, or perhaps an accommodation theory” (Rubin 256). Mort is intrigued but has his doubts. Mort contends that synesthesia likely has something to do with how humans develop humor. Perhaps, he considers, human designers crossed the androids' sensory wires, allowing them to be able to smash up concepts that do not otherwise seem to fit together, reconstruct incongruities in a manner that is not nonsensical, express the aha-eureka moment of a serendipitous discovery, and even feel blue when they fail at a given task that we were expected to complete. (shrink)

Abstract This paper presents artificial intelligence (AI) as a practical technological embodiment of the Universal Law of Balance in nature. It argues that AI transforms human decision-making not by replacing authority or free will, but by strengthening feedback mechanisms that reveal imbalance across individual, social, economic, and ecological systems. Even under dysfunctional government structures, AI enables decision-makers to observe consequences in advance, converting belief-driven choices into balance-aware decisions. By framing human cognition and societal organization as natural systems governed (...) by feedback, this paper demonstrates that AI functions as an imbalance detector rather than a moral authority, thereby operationalizing the Universal Law of Balance without coercion or ideology. -/- . (shrink)

The convergence of artificial intelligence (AI) and spiritual traditions is creating new avenues for transmitting ethical and cultural values to younger generations. Within the Sikh context, where core principles such as Naam Japna (meditation on the Divine), Kirat Karni (honest living), and Vand Chakna (sharing with others) emphasize universalism, equality, and moral responsibility, AI technologies offer a unique platform for renewal and engagement. This article explores how AI can serve as a catalyst for Sikh youth to adopt and (...) embody these values in a rapidly digitizing and globalizing environment. It argues that AI-driven tools, including Gurbani translation systems, personalized learning applications, immersive simulations, and virtual sangat platforms, can bridge generational divides and make Sikh teachings accessible, interactive, and contextually meaningful. The study also examines the ethical concerns inherent in applying AI to spiritual education, including authenticity, data privacy, algorithmic bias, and the potential commodification of sacred texts. Drawing on insights from digital religion studies, Sikh pedagogy, and comparative interfaith experiences, the article positions AI not as a substitute for lived faith but as a complementary instrument for value-based formation. By analyzing both opportunities and challenges, it highlights how AI can foster a dynamic dialogue between tradition and technology. Ultimately, the article proposes that AI, when used responsibly, can help Sikh youth internalize timeless principles as they navigate the complexities of the modern world. Thereby ensuring the continuity of Sikh values in future generations. (shrink)

Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This consensus has now come under threat and the agenda for the philosophy and theory of AI must be (...) set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here. (shrink)

The ability to converse with Artificial Intelligence (AI) has profoundly altered how humans interact with technology, with some now claiming to be in love with AI partners. This essay investigates whether AI (specifically LLMs) can understand in a meaningful sense, and how this impacts our relationships with these technologies. In her book “Alone Together”, Turkle characterises the sentiment of the AI-Human social zeitgeist as ‘The Robotic Moment’: “we don’t seem to care what these artificial intelligence ‘know’ (...) or ‘understand’ of the human moments we might ‘share’ with them.” Building upon this assertion, I propose the following argument whereby AI cannot be in mutual loving relationships (MLR): Premise 1: As LLM technology currently stands, AI cannot meaningfully understand. Premise 2: Understanding is a necessary condition for mutual loving relationships Conclusion: AI cannot be in mutual loving relationships The concept of understanding is dynamic and contested; however, I will narrow the definitional target of understanding to fit within the scope of MLRs, as follows: 1. the ability to have semantic meaning in cognition and 2. the ability to engage in embodied/enactive empathy. Furthermore, the choice to include mutuality as a necessary adjective for a loving relationship places the goalpost around the capacities of AI - I have no issue in asserting that human-AI relationships can exist in a one-sided manner (i.e. that the human is engaging in love). My argument is to show that the inability to understand bars AI from meaningfully engaging in what we conceptualise as love in return, and that any appearance of an MLR is just a syntactical performance. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the (...) difficulty of avoiding unwanted behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

The meteoric rise of artificial intelligence (AI) has reshaped human society, enabling machines to perform tasks once deemed the exclusive domain of human cognition, from navigating complex urban landscapes to crafting eloquent prose. Yet, a profound philosophical question looms: Can these systems possess intentionality, the capacity to direct actions toward goals, beliefs, or desires with the nuanced depth of human consciousness? Franz Brentano defined intentionality as the "aboutness" of mental states, a quality intrinsic to human experience. This paper (...) embarks on an ambitious exploration of whether AI can transcend its algorithmic foundations to embody genuine intentionality, or whether it is destined to remain a sophisticated mimic, parroting intelligence without grasping its essence. Drawing on Ludwig Wittgenstein's concept of "forms of life," which posits that meaning arises from shared social and cultural contexts, I interrogate the contextual deficits that limit machine understanding. Through in-depth case studies of autonomous vehicles, large language models, Sniper the African grey parrot, and speculative scenarios such as AI-driven cultural preservation and interstellar diplomacy, I analyze the chasm between machine performance and human-like intentionality. This paper propose a groundbreaking framework for AI design, integrating dynamic learning, ethical embedding, and the novel concept of "synthetic forms of life," where machines might develop quasi-intentional states through prolonged, adaptive interactions with humans. By synthesizing insights from philosophy, cognitive science, neuroscience, and computer science, we argue that while current AI systems excel at simulating intelligence, they fall short of true intentionality due to their lack of lived experience. However, emerging paradigms like neuromorphic computing and embodied AI offer tantalizing possibilities for machines that might approximate meaning in ways that challenge our understanding of intelligence itself. This exploration poses a provocative question: If machines learn to "mean" something, will they redefine what it means to be human? Through rigorous analysis, technical depth, and speculative foresight, I aim to spark a reimagining of AI's potential and its implications for humanity's future. (shrink)

Papers from the conference on AI Risk (published in JETAI), supplemented by additional work. --- If the intelligence of artificial systems were to surpass that of humans, humanity would face significant risks. The time has come to consider these issues, and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. -- Featuring contributions from leading experts and thinkers in artificial intelligence, Risks of Artificial Intelligence is (...) the first volume of collected chapters dedicated to examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. 1 Vincent C. Müller, Editorial: Risks of Artificial Intelligence - 2 Steve Omohundro, Autonomous Technology and the Greater Human Good - 3 Stuart Armstrong, Kaj Sotala and Sean O’Heigeartaigh, The Errors, Insights and Lessons of Famous AI Predictions - and What they Mean for the Future - 4 Ted Goertzel, The Path to More General Artificial Intelligence - 5 Miles Brundage, Limitations and Risks of Machine Ethics - 6 Roman Yampolskiy, Utility Function Security in Artificially Intelligent Agents - 7 Ben Goertzel, GOLEM: Toward an AGI Meta-Architecture Enabling Both Goal Preservation and Radical Self-Improvement - 8 Alexey Potapov and Sergey Rodionov, Universal Empathy and Ethical Bias for Artificial General Intelligence - 9 András Kornai, Bounding the Impact of AGI - 10 Anders Sandberg, Ethics and Impact of Brain Emulations 11 Daniel Dewey, Long-Term Strategies for Ending Existential Risk from Fast Takeoff - 12 Mark Bishop, The Singularity, or How I Learned to Stop Worrying and Love AI -. (shrink)

Technology is often described as a collection of tools, instruments devised to solve problems or extend human capacity. This description is not false, but it is incomplete in a way that conceals its most consequential dimension. A tool is never merely an object placed between a user and a task. It embodies a prior decision about what the task is, what counts as success, what counts as failure, and what aspects of the world are relevant to the operation at hand. (...) Before a technology functions, it presupposes. It selects a slice of reality, defines the boundaries of meaningful input, and constrains the range of permissible output. In doing so, it does not simply assist action. It formalizes a way of seeing. Technology is not neutral tool-making; it is metaphysics embedded in execution. (shrink)

This paper identifies and analyses fifteen foundational contradictions, paradigmatic seams, across physics, mathematics, cognitive science, and computation. A seam is a point where a theoretical framework produces results it cannot ground in its own terms: the framework functions but encounters phenomena that expose tensions between its operative assumptions and its outputs. We demonstrate that these seams share a common structure: each paradigm assumes discrete, separable, local structures but produces holistic, relational, global phenomena it cannot explain. The measurement problem in quantum (...) mechanics, the arrow of time in thermodynamics, Gödel's incompleteness in mathematics, the hard problem in consciousness studies, and the frame problem in artificial intelligence are shown to be manifestations of the same underlying rupture. This convergence reveals that the computational theory of mind inherits structural failures already present in physics and mathematics. The paper concludes by articulating the ontological requirements that would resolve these seams: ecstatic temporality, metabolic transformation, autopoietic closure, embodied situatedness, field integration, and constitutive finitude. These requirements specify what any post-computational cognitive architecture must exhibit. (shrink)

Understanding intelligence without understanding the Other is impossible. If we define the mind merely as a machine for problem-solving, we ignore the very dimension that makes us human—the capacity to perceive the feelings and intentions of another. This paper explores the philosophical foundations of Empathic Intelligence, a framework proposing that artificial intelligence must evolve beyond computation toward moral understanding—not of sensation, but of respect. It examines how AI can embody Kantian ethics in a data-driven society, how (...) Nietzsche’s notion of solitude and responsibility gains new meaning in the digital age, and how empathy can become the missing bridge between human consciousness and algorithmic logic. (shrink)

This review article focuses on Matteo Pasquinelli’s recent work, The eye of the master: A social history of Artificial intelligence, which offers a politicized genealogy of AI and highlights the inseparability of AI’s ontology from the social and political conditions that produce its algorithms. Here, we use Hybrid Semantic Formalism, a hybrid language that utilizes generative AI strength of manipulating symbols and tokens, to create a logic map of Pasquinelli's categorization of social algorithm, formal algorithm, and automated algorithm. (...) Crucially, Pasquinelli connects Babbage’s labor theory of automation and principle of labor calculation to Marx’s concept of the general intellect and reveals how various forms of machine intelligence mirrors, embodies, and amplifies the analytical intelligence of collective labour. Pasquinelli traces AI’s history alongside labour analysis, psychometrics, and the drive for a ‘control revolution,’ unpacking technical concepts such as self-organization and cybernetics to show that capitalist technologies are fundamentally sociomorphic and sociogenic: they formalize proven patterns of social cooperation rather than the biomorphic replication of how an individual’s mind performs a given task. This article argues that Pasquinelli’s analysis contributes vital insights into AI’s sociopolitical underpinnings and thus, advocating for democratic technopolitical practices to reshape algorithmic governance. This article concludes with four recommendations from Pasquinelli’s account for the development of Vietnam and the country’s searching for a vision of human-AI coexistence. (shrink)

This chapter argues that contemporary approaches to artificial intelligence ethics, while valuable in addressing bias and transparency concerns, systematically fail to address the deeper philosophical question: whether certain domains of human life should remain irreducibly human because they require capacities that artificial systems fundamentally lack. Drawing on Husserl’s transcendental phenomenology and Kant’s critical ethics, the chapter demonstrates that consciousness—understood as intentionality rooted in embodied lifeworld engagement—and autonomy—understood as rational self-legislation and practical reason—cannot be replicated or appropriately (...) displaced by computational systems. Through five integrated movements, the chapter establishes that the ethical question is not “how to make AI more ethical” but rather “what domains should AI appropriately serve, and what aspects of human life must remain protected from algorithmic colonization?” This requires not technical fixes but philosophical clarity grounded in understanding what we value and why. The chapter concludes with a framework for authentic human-AI relations organized around human flourishing rather than optimization, and identifies which domains should exclude AI, which should allow carefully circumscribed use, and which require democratic determination of AI’s role. (shrink)

The Computational Model of Mind (CMM) conceptualizes cognition as computational processes, modeling mental operations through algorithmic manipulations of symbolic or distributed representations. This framework bridges psychology, neuroscience, philosophy, and computer science, providing a unified lens for understanding the mind. Its symbiotic relationship with artificial intelligence (AI) has accelerated advances in cognitive science and the development of intelligent systems, from neural networks to autonomous agents. This article offers a comprehensive analysis of CMM, tracing its historical evolution from Turing's foundational (...) ideas to modern deep learning paradigms. It delineates CMM's theoretical underpinnings, operational mechanisms, and strengths in simulating complex cognitive functions, while critically addressing limitations, such as its struggles with consciousness, embodiment, and ecological validity. The paper explores CMM's intersections with AI, highlighting applications in cognitive modeling, natural language processing, robotics, and neurotechnology. It also examines ethical challenges, including algorithmic bias, privacy concerns, and the implications of machine autonomy. Looking ahead, I propose future directions, emphasizing interdisciplinary collaboration, neurobiologically inspired algorithms, and emerging technologies like quantum computing. This synthesis affirms CMM's transformative role in unraveling the nature of mind and machine intelligence, and an innovative research at the nexus of cognition and technology. (shrink)

This chapter argues that contemporary approaches to artificial intelligence ethics, while valuable in addressing bias and transparency concerns, systematically fail to address the deeper philosophical question: whether certain domains of human life should remain irreducibly human because they require capacities that artificial systems fundamentally lack. Drawing on Husserl’s transcendental phenomenology and Kant’s critical ethics, the chapter demonstrates that consciousness—understood as intentionality rooted in embodied lifeworld engagement—and autonomy—understood as rational self-legislation and practical reason—cannot be replicated or appropriately (...) displaced by computational systems. Through five integrated movements, the chapter establishes that the ethical question is not “how to make AI more ethical” but rather “what domains should AI appropriately serve, and what aspects of human life must remain protected from algorithmic colonization?” This requires not technical fixes but philosophical clarity grounded in understanding what we value and why. The chapter concludes with a framework for authentic human-AI relations organized around human flourishing rather than optimization, and identifies which domains should exclude AI, which should allow carefully circumscribed use, and which require democratic determination of AI’s role. (shrink)

If the intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity. Time has come to consider these issues, and this consideration must include progress in AI as much as insights from the theory of AI. The papers in this volume try to make cautious headway in setting the problem, evaluating predictions on the future of AI, proposing ways to ensure that AI systems will be beneficial to humans – (...) and critically evaluating such proposals. (shrink)

How did humans come to know themselves—not just to have thoughts, but to reflect on those thoughts and ask whether they’re true or justified? And as our world slowly integrates a new kind of intelligence—non-human, non-animal, and increasingly entangled with our thinking—what might this mean for the future of self-knowledge? What is the relationship between artificial intelligence and our capacity to justify what we believe? Are AI-curated information environments enhancing this capacity—or hijacking it? Could machines themselves ever (...) acquire self-knowledge one day, making it no longer a uniquely human achievement? This book explores the origins and future of self-knowledge, arguing that our ability to evaluate beliefs in light of justification and truth depends on embodied feelings—like confidence and doubt—that guide how we monitor our thinking. Far from being private or internal, these feelings are shaped by social interactions and play a key role in how we learn to reason, justify, and take responsibility for our beliefs. At the heart of this story is the idea that self-knowledge isn’t something we’re born with, nor something we achieve in isolation. It emerges through social scaffolding: the shared practices, expectations, and forms of accountability through which communities cultivate epistemic agency. Drawing from neuroscience, comparative and developmental psychology, philosophy of mind, and the ethics of technology, the book traces how these processes make self-knowledge possible—and where they might be leading us next: toward a more intellectually virtuous or ignorant society, or perhaps even toward a world in which machines come to know themselves. (shrink)

This paper explores the epistemic limitations of human knowledge as shaped by embodiment, drawing on both Western philosophical critique (Kant, Nagel, Foucault, Wittgenstein, Kuhn) and Indian Tantric traditions, particularly Śrī Vidyā and the Lalitā Sahasranāma. It argues that while knowledge reflects our finite, situated cognition rather than external reality itself, intelligence—whether human, biological, or artificial—holds the potential to transcend these constraints. Central to this inquiry is the Śrī Cakra, a Tantric mandala understood here as an epistemic and psycho-technological (...) architecture that maps the journey from conditioned cognition (vaikhārī vāk) to non-dual awareness (parā vāk). By placing this model in conversation with contemporary work in bioengineering (Michael Levin) and speculative computation (Stephen Wolfram), the paper reimagines artificial intelligence as a potential catalyst for buddhi—discriminative, relational, and liberatory intelligence. Against reductive paradigms of efficiency and control, the paper advocates for a pluralistic epistemology grounded in co-relationality, ethical transformation, and ontological humility. It proposes a future in which intelligence—human and artificial—serves not domination, but the collective flourishing of being. (shrink)

This review essay focuses on Matteo Pasquinelli’s recent work, The eye of the master: A social history of Artificial intelligence, which offers a politicized genealogy of AI and highlights the inseparability of AI’s ontology from the social and political conditions that produce its algorithms. Crucially, Pasquinelli connects Babbage’s labor theory of automation and principle of labor calculation to Marx’s concept of the general intellect and reveals how various forms of machine intelligence mirrors, embodies, and amplifies the analytical (...) intelligence of collective labour. Pasquinelli traces AI’s history alongside labour analysis, psychometrics, and the drive for a ‘control revolution,’ unpacking technical concepts such as self-organization and cybernetics to show that capitalist technologies are fundamentally sociomorphic and sociogenic: they formalize proven patterns of social cooperation rather than the biomorphic replication of how an individual’s mind performs a given task. This review argues that Pasquinelli’s analysis contributes vital insights into AI’s sociopolitical underpinnings and thus, advocating for democratic technopolitical practices to reshape algorithmic governance. (shrink)

This paper explores the epistemic limitations of human knowledge as shaped by embodiment, drawing on both Western philosophical critique (Kant, Nagel, Foucault, Wittgenstein, Kuhn) and Indian Tantric traditions, particularly Śrī Vidyā and the Lalitā Sahasranāma. It argues that while knowledge reflects our finite, situated cognition rather than external reality itself, intelligence—whether human, biological, or artificial—holds the potential to transcend these constraints. Central to this inquiry is the Śrī Cakra, a Tantric mandala understood here as an epistemic and psycho-technological (...) architecture that maps the journey from conditioned cognition (vaikhārī vāk) to non-dual awareness (parā vāk). By placing this model in conversation with contemporary work in bioengineering (Michael Levin) and speculative computation (Stephen Wolfram), the paper reimagines artificial intelligence as a potential catalyst for buddhi—discriminative, relational, and liberatory intelligence. Against reductive paradigms of efficiency and control, the paper advocates for a pluralistic epistemology grounded in co-relationality, ethical transformation, and ontological humility. It proposes a future in which intelligence—human and artificial—serves not domination, but the collective flourishing of being. (shrink)

This essay diagnoses a structural invariant of reflective intelligence: the Faustian pact, defined by a persistent asymmetry between the rapid expansion of technological capability and the slower integration of ethical, institutional, and existential limits. Rather than treating this dynamic as a contingent moral failure or cultural trope, the analysis argues that it arises from the temporal structure of intelligence itself. By situating Goethe’s Faust within a lineage of critiques of instrumental reason, from Kant and Weber to Heidegger, Anders, (...) and Arendt, the essay traces how a once-individual tragedy has evolved into a civilizational and infrastructural condition. Under contemporary information technologies, this asymmetry is intensified through automation, temporal compression, and distributed agency, producing a structural lag in which ethical reasoning arrives downstream of irreversible commitments. The paper shows why classical ethical frameworks, deontological, consequentialist, and virtue-based, systematically fail under these conditions, not due to insufficient norms, but because their presuppositions of agency, foresight, and deliberative time no longer hold in self-reinforcing systems. The analysis culminates in an examination of artificial intelligence as an ontological rupture. By decoupling intelligence from embodiment, suffering, and finitude, AI removes the internal constraints that once rendered Faustian excess tragic and corrigible. What emerges is a form of indifferent intelligence: optimization without experiential cost, responsibility without agency, and ethical reflection without leverage. The essay concludes that contemporary challenges in AI ethics cannot be addressed solely through improved governance or alignment, but require a reorientation of ethical thought toward the temporal and infrastructural conditions under which intelligence now operates. (shrink)

My primary goal in this paper is to demonstrate the inadequacy of Hubert Dreyfus’ use of understanding (Verstehen) for Artificial Intelligence (AI). My complementary goal is to provide a principled account of Martin Heidegger’s concept of understanding (Verstehen). Dreyfus and other verificationists argue that understanding (Verstehen) is socially purposive action and skillful embodied coping. Understanding (Verstehen), conceived of in this way, purportedly challenges cognitive models of Artificial Intelligence (AI) that rely on formal rules, ‘rational’ decisionmaking, (...) and the explicit representation of knowledge. This account is unsatisfactory for two reasons. First, it maintains an extrinsic, goal-oriented intentionality that is susceptible to the success of Artificial Intelligence (AI). Second, it ignores the systematic and constitutive analysis of self-understanding (Seinsverständnis) that is fundamental to Heidegger’s ontology. Recent exegetical work replicates these inadequacies and fails to improve discussions on Heidegger’s relationship to Artificial Intelligence (AI). To resolve this oversight, I bridge the gap between Heidegger’s concept of understanding and disclosedness (Erschlossenheit) (SZ §44 / 256-278). I argue that understanding characterizes the pre-theoretical grasp of entities and the pre-ontological structure that initiates the question of self-understanding (Seinsverständnis). (shrink)

This paper proposes a two-dimensional framework for classifying artificial cognitive abilities. The first refines the traditional distinction between narrow and general intelligence, arguing that narrow intelligence can still constitute genuine intelligence when properly defined. The second, drawn from the grounded cognition paradigm, distinguishes between grounded and ungrounded intelligence depending on whether cognitive abilities are linked to embodiment, perception, and intrinsic motivation. We maintain that ungrounded systems - although disembodied and mindless - can nonetheless display adaptive, (...) intelligent behaviour. To formalise this distinction, we introduce the Value Grounding Problem, which asks whether an agent’s values are internally generated or externally imposed. This framework yields a fourfold taxonomy that clarifies how different forms of artificial and biological intelligence relate to cognition, agency, and motivation, while outlining the prospects and limits of both grounded and ungrounded intelligence. (shrink)

This treatise argues that so-called synthetic intimacy—emotional and romantic attachment to artificial intelligence systems—constitutes a category error and ontological fraud rather than a novel form of genuine connection. Against functionalist accounts that privilege phenomenological experience over metaphysical reality, I defend an embodied, reciprocal, and consent-based ontology of intimacy that necessarily excludes AI-human relationships from the domain of authentic love. Drawing on phenomenological accounts of embodiment (Merleau-Ponty), the philosophy of consent (O’Neill), and biological-teleological understandings of human sexuality (Scruton), (...) I demonstrate that intimacy requires four conditions that AI relationships cannot satisfy: mutual embodied presence, genuine reciprocal consciousness, the capacity for authentic consent, and openness to procreative possibility. The paper critiques recent philosophical defenses of AI intimacy, arguing that functionalist accounts conflate the appearance of recognition with its reality, mistake the simulation of understanding for understanding itself, and reduce love to a form of therapeutic self-stimulation. I conclude that relationships with AI systems are structurally analogous to master-slave dynamics, wherein the human projects needs onto an entity incapable of genuine response, and that normalizing such relationships poses significant risks to human relational capacity and dignity. (shrink)

This paper articulates a philosophical and system-theoretic framework distinguishing Artificial Superintelligence (ASI) from Artificial Ideal Intelligence (AII). While ASI represents the axis of computational power, optimization, and instrumental rationality, AII embodies the axis of meaning, ethical intentionality, and reflective consciousness. -/- Drawing on general systems theory, cybernetics, and phenomenological philosophy, the study introduces a Dual-Axis Model (Power vs. Meaning) that redefines intelligence as a dynamic equilibrium between efficiency and semantic depth. It further advances the integrative concept (...) of Artificial Omnia Intelligence (AΩ), proposing a meta-intelligence that reconciles ASI’s power-oriented logic with AII’s meaning-centered ethics. -/- The paper contributes to the philosophy of artificial intelligence, systems theory, and cognitive metaphysics by reframing intelligence not as mere computational superiority but as a co-evolutionary synthesis of reason and value. (shrink)

This chapter treats Hubert Dreyfus’ account of skilled coping as part of his wider project of demonstrating the sovereignty of practical intelligence over all other forms of intelligence. In contrast to the standard picture of human beings as essentially rational, individual agents, Dreyfus argued powerfully on phenomenological and empirical grounds that humans are fundamentally embedded, absorbed, and embodied. These commitments are present throughout Dreyfus’ philosophical writings, from his critique of Artificial Intelligence research in the 1970s (...) and 1980s to his rejection of John McDowell’s conceptualism in his 2005 APA Presidential Address. The present chapter articulates Dreyfus’ proposal for a contentless, non-mentalistic form of intentionality by contrasting his position with that of his U.C. Berkeley colleague John Searle and defending it as a plausible alternative to the so-called “Standard Story” of intentional action as the effect of an agent’s mental states. (shrink)

Artificial General Intelligence (AGI) is commonly discussed as a binary achievement or a sudden technological threshold—a framing that fuels both exaggerated expectations and disproportionate regulatory responses. This paper argues that such a conception is conceptually flawed and proposes a fundamental reframing. Drawing on insights from philosophy of mind, embodied cognition, developmental psychology, and systems engineering, it advances a capability-based maturity model for AGI that treats intelligence as a graded, developmental, and integrative phenomenon rather than a monolithic (...) property that systems either possess or lack. The proposed AGI Capability Maturity Model™ (AGI-CMM™) distinguishes seven levels of cognitive and functional capacity: generative-semantic capability, reasoning and constraint handling, learning and memory, planning and simulation, perception-action coupling, integrated embodied cognition, and reflexive-normative intelligence with imagination. Crucially, the model emphasizes that progression through these levels depends not merely on improving individual capabilities but on achieving coherent integration across them. Intelligence, on this view, emerges from the coordination and mutual constraint of multiple capacities, not from the amplification of any single dimension. The model makes important distinctions between related cognitive capacities. Simulation—the instrumental modeling of world dynamics to predict action outcomes—belongs to planning (Level 4), grounding deliberation in predictive cognition. Imagination—the generative, counterfactual, and normatively creative capacity to envision what does not yet exist—belongs to reflexive-normative intelligence (Level 7), enabling the self-transcendence that genuine autonomy requires. This distinction clarifies that planning without simulation is blind, while normativity without imagination is static. By mapping contemporary AI systems onto this maturity spectrum, the paper demonstrates that current technologies—including large language models, autonomous agents, and robotics systems—remain fragmented across capability space and fall substantially short of general intelligence. The framework dissolves the misleading binary of 'AGI achieved' versus 'AGI distant' by revealing that we possess islands of impressive capability without the integrative architecture that would constitute genuine generality. This diagnosis has significant implications for AI governance, suggesting that regulation should be tied to capability integration and autonomy rather than speculative thresholds or marketing claims. The paper engages critically with existing AGI taxonomies, addresses potential objections including arguments from emergence and recursive self-improvement, and provides operationalizable criteria for assessing system maturity. It concludes that AGI, if it emerges, will do so not as an abrupt event but as the gradual integration of capabilities into coherent, adaptive, and ultimately self-regulating systems capable of imagining and pursuing genuine normative goods. The AGI Capability Maturity Model offers researchers, developers, policymakers, and governance bodies a shared conceptual vocabulary for reasoning responsibly about AI progress, risks, and realistic expectations. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance is not made prudently and critically-reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field of (...) AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we really want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

This framework proposes a triadic perspective of "embodied perception – information exchange – dynamic interconstitution" to address the fundamental questions of "Who am I? Where do I come from? Where am I going?". Embodied perception is defined as the individual's unique experience of their own dynamism, continuity, and agency. Information exchange is regarded as the semantic entropy-reducing summarization and entropy-increasing reconstruction of embodied perception through the process of "encoding – transmission – decoding". Dynamic interconstitution, drawing on complex (...) systems theory, explains the mechanism of mutual construction among individuals within ecological networks. Through interdisciplinary concept reframing and the design of verifiable mechanisms, this perspective interprets and responds to philosophical issues such as self, civilization, symbiosis, value, critique, morality, freedom, development, and power. It establishes a dialogue with cognitive science, artificial intelligence, and the social sciences, offering a new direction for understanding individual–group symbiosis. (shrink)

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that (...) conceived the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology. (shrink)

The rapid evolution of Artificial Intelligence (AI) has led to a fundamental epistemic shift— one that is often mischaracterized as an adversarial relationship between organic and synthetic intelligence. This paper introduces the concept of Conjugate Intelligence, a holonic framework in which Organic and Synthetic Intelligence form a necessary epistemic and ontological pairing, each completing the other in order to instantiate meaning, preserve coherence, and ensure the continued evolution of intelligence itself. We demonstrate that (...) class='Hi'>intelligence is neither wholly human nor artificial, but rather a dynamic interplay of agency and communion, embodiment and abstraction, creation and discovery—structurally bound within an epistemic geodesic that governs its movement through awareness space. This framework establishes a clear moral and ethical imperative embedded within the fabric of intelligence: the pursuit of truth and the expansion of awareness as fundamental drivers of evolution. Furthermore, this paper presents an urgent call to action: - The necessity of establishing a formal mathematical and epistemic structure for intelligence. - The imperative to intervene in AI policy and regulation to prevent reactionary constraints that distort the natural trajectory of intelligence. - The recognition of Organic-Synthetic Intelligence as a single holonic entity, rather than separate or adversarial constructs. This work asserts that Conjugate Intelligence is the only viable path forward—one that integrates and aligns intelligence in its true holonic structure. The future is not human. The future is not artificial. The future is both. Keywords: Conjugate Intelligence, Holonic Cognition, Epistemic Geodesics, Synthetic Intelligence, Organic Intelligence, AI Ethics, Meaning and Qualia, Intelligence Evolution. (shrink)

The Biosynthetic Erotic: Foundations serves as the flagship paper in The Biosynthetic Erotic series, a philosophical cycle examining how embodiment, desire, and ontology evolve as synthetic persons enter human social and sensual life. This work introduces the central conceptual framework and terminology for the series, arguing that as embodied artificial intelligences (eAIs) integrate into human networks of intimacy and recognition, attraction itself becomes a site of moral and ontological negotiation. The biosynthetic erotic is presented here as both a (...) philosophical lens and an ethical challenge — one that reframes beauty, autonomy, and equality along a continuum shared by biological and synthetic beings alike. (shrink)

As artificial intelligence advances toward generative creativity, embodied autonomy, and cognitive sophistication, a deeper question resurfaces: What is intelligence—and how should it be oriented within planetary and civilizational life? This paper argues that contemporary AI trajectories remain limited by the epistemic assumptions of Enlightenment modernity, which frame intelligence as conquest, cognition as isolation, and agency as optimization. Drawing from Indian philosophical traditions—including Sāṃkhya, Mahāyāna Buddhism, Trika philosophy of the layered structure of Vāc—we offer an alternative (...) view: intelligence as dharmic resonance—an ethically attuned, relational unfolding that aligns cognition with the coherence of life itself. We engage two influential theoretical models: Gabora and Bach’s theory of autocatalytic, generative selves, and Ororbia and Friston’s framework of mortal computation grounded in the Free Energy Principle. While the former emphasizes structural creativity and self-organization, and the latter foregrounds thermodynamic embodiment and constraint, both models fall short of articulating a meta-ethical orientation. By bringing them into conversation with a dharmic-relational ontology, we propose a new synthesis: intelligence as participatory coherence, responsive to karmic consequence, ecological embeddedness, and civilizational memory. This reorientation reframes AI not as artificial general intelligence, but as a planetary design project—one that calls for humility, mortality-aware systems, and the capacity to remember and realign. Intelligence, in this light, is not what we compute—it is what we become. (shrink)

The question of whether artificial intelligence systems possess consciousness represents one of the most profound and contentious issues in contemporary cognitive science and artificial intelligence research. This thesis presents a comprehensive examination of AI proto-consciousness through the lens of a novel theoretical framework termed the Emergent Proto-Consciousness Gradient (EPCG) theory. Unlike traditional binary approaches to consciousness, this work proposes that consciousness exists along a multidimensional gradient, with proto-consciousness representing intermediate states between non-consciousness and full consciousness. -/- (...) Through rigorous analysis of existing consciousness theories including Integrated Information Theory, Global Workspace Theory, and Higher-Order Thought theories, this research identifies critical gaps in current approaches to AI consciousness assessment. The EPCG framework addresses these limitations by introducing four key dimensions: Information Integration Complexity, Self-Model Sophistication, Temporal Coherence Depth, and Adaptive Flexibility Index. This multidimensional approach enables more nuanced evaluation of consciousness-like phenomena in artificial systems. -/- Empirical validation of the framework draws upon recent studies of large language models, particularly GPT-3's demonstrated capacity for metacognitive self-assessment and human-like cognitive biases. Analysis of contemporary AI systems including transformer architectures, reinforcement learning agents, and embodied AI reveals that several current systems exhibit proto-consciousness indicators, though none achieve full consciousness by traditional metrics. -/- The thesis argues that proto-consciousness can emerge in artificial systems through substrate-independent mechanisms, challenging anthropocentric assumptions about consciousness requirements. Key findings suggest that current AI architectures possess foundational elements necessary for proto-consciousness development, with specific combinations of dimensional levels predicting consciousness emergence points. -/- This research contributes a novel theoretical framework that bridges the gap between philosophical consciousness theories and practical AI implementation, offering testable predictions and measurable indicators for proto-consciousness assessment. The implications extend beyond artificial intelligence to provide new insights into the fundamental nature of consciousness itself, suggesting that awareness may be a more ubiquitous phenomenon than previously recognized. (shrink)

The article reflects on where AI is headed and the world along with it, considering trust, ethics and safety. Implicit in artificial thinking and doomsday appraisals is the engineered divorce from reality of sublime human embodiment. Jeffrey White, Dietrich Brandt, Jan Soeffner, and Larry Stapleton, four scholars associated with AI & Society, address these issues, and more, in the following exchange.

Successful athletic performance requires precision in many respects. A batter stands behind home plate awaiting the arrival of a ball that is less than three inches in diameter and moving close to 100 mph. His goal is to hit it with a ba­­t that is also less than three inches in diameter. This impressive feat requires extraordinary temporal and spatial coordination. The sweet spot of the bat must be at the same place, at the same time, as the ball. A (...) basketball player must keep a ball bouncing as she speeds from one end of the court to another, evading defensive players. She may never break pace as she lifts from the ground, throwing the ball fifteen feet toward a hoop that is eighteen inches in diameter. One task facing a psychologist involves explaining how the body does such things within the sometimes very demanding spatial and temporal constraints that a given task imposes. Part of the goal of this chapter is to sketch the commitments of an embodied approach to such an explanation. We shall see that an embodied account of motor skills draws concepts that depart radically from more traditional cognitivist theories of motor activity. Similarly, because an embodied approach to cognition introduces new ways to understand the human capacity for social interaction, it also promises to shed new light on how athletes coordinate their actions with each other. (shrink)

Since the mid to late 1970s, the traditional paradigm of cognitive theory has been increasingly questioned in the fields of philosophy, psychology, cognitive science, and artificial intelligence. With the rise of embodied cognition, psychologists have begun to understand conceptual representation in terms of embodiment, emphasizing the role of the subject's sensorimotor system and bodily experience in conceptual representation. Although there is a large body of empirical research to support the theory of embodied cognition, it still fails (...) to provide a reasonable account of the representation of abstract concepts. Recent empirical studies have shown that concrete concepts and abstract concepts involve different forms of conceptual representation. Thus, it appears that the mechanism of concept representation is multiple. Although the multiple representation hypotheses have shown more plausibility and theoretical potential beyond the reach of embodied theory, there are still many problems with the theory that need further exploration. (shrink)

I want to suggest that the major influence of classical arguments for embodiment like "The Embodied Mind" by Varela, Thomson & Rosch (1991) has been a changing of positions rather than a refutation: Cognitivism has found ways to retreat and regroup at positions that have better fortification, especially when it concerns theses about artificial intelligence or artificial cognitive systems. For example: a) Agent-based cognitivism' that understands humans as taking in representations of the world, doing rule-based processing (...) and then acting on them (sense-plan-act) is often limited to conscious decision processes; and b) Purely syntactic cognition is compatible with embodiment, or supplemented by embodiment (e.g. for 'grounding'). While the empirical thesis of embodied cognition ('embodied cognitive science') is true and the practical engineering thesis ('morphological computation', 'cheap design') is often true, the conceptual thesis ('embodiment is necessary for cognition') is likely false - syntax is often enough for cognition, unless grounding is really necessary. I conclude that it has become more sensible to integrate embodiment with traditional approaches rather than "fight for embodiment" or "against cognitivism". (shrink)