Intelligence

Applying the SupraConscious State (Advanced Autonomous Intelligence) (Autonomous Intentional Masking).

The current debate on artificial intelligence, AGI, and superintelligence is often based on the implicit assumption that intelligence can, in principle, be increased without limit. Larger models, more data, greater computational power, and more comprehensive world models are commonly regarded as steps along an open-ended developmental trajectory. The present paper challenges this assumption. -/- Starting from a theory of world-binding (Weltbindung), it is proposed that intelligence should not primarily be understood as information processing, predictive capability, or computational power, but rather (...) as the ability of a system to irreversibly integrate difference into a coherent and actionable world. On this basis, it is argued that world-formation necessarily depends on Raster formation, selective stabilization, and non-closure, as elaborated in the author's previous work. Every form of world-binding simultaneously generates a Gap between the bound world and the remaining openness. This Gap does not disappear through additional knowledge but is reproduced through every new act of form-formation. -/- From this, the paper develops the thesis of a structural sweet spot of intelligence. Intelligence appears neither as a linear function of complexity nor as an infinitely scalable property. Below a certain capacity for differentiation, sufficient world-binding cannot emerge. Above a certain capacity for differentiation, the ability to integrate growing difference begins to disintegrate. Between these two poles lies a region of maximal world-binding capacity. -/- On this basis, the paper demonstrates why additional complexity, additional models, or additional Raster do not fundamentally solve the problem. Every expansion of world generates new integration requirements and new forms of non-closure. The limit of intelligence therefore appears not as a deficit or failure of its development, but as a condition of its effectiveness. Systems become capable of action not despite their limitations, but through them. -/- Finally, the implications of this perspective for current AGI, world model, and superintelligence concepts are discussed. The paper proposes that the central limit of future intelligence systems may not lie in insufficient computational power, inadequate data, or technical restrictions, but in the ontological conditions of world-formation itself. The decisive question is therefore not how intelligent a system can become, but under what conditions a world remains bindable at all. (shrink)

Can large language models be considered intelligent? Arguments against this proposition often assume that genuine intelligence cannot exist without consciousness, understanding, or creative thinking. We discuss each of these roadblocks to machine intelligence and conclude that none of them rule out the possibility of viewing current AI systems based on large language models as intelligent. We argue that consciousness is not relevant for intelligence, while creativity and understanding should be considered functional traits that, in principle, can be implemented in a (...) machine. Many arguments concluding that current AI systems necessarily lack these traits either rely on human exceptionalism or flawed understanding of human intelligence. Arguments that highlight alleged shortcomings in current systems—such as a lack of reliability, agency, or understanding—often obscure the many qualitative similarities between human cognition and current AI models. We further suggest that a critical examination of high-performance AI systems can serve as a mirror through which we can reflect on our own intelligence. Many arguments against the prospects of AI may be based on naively optimistic assessments of capacities of the human mind. (shrink)

The influence of network structure on collective problem-solving is a central focus in collective intelligence. However, the causal mechanisms linking structure to collective outcomes remain unexplored. To explore these, we utilize an agent-based model, the Potions Task, which operationalizes problem-solving as a combinatorial process suited to information-theoretic analysis. We examine information-based metrics at the level of agent pairs across networks, analyzing variation over time to determine how they predict problem-solving across network structures. We measure redundancy (or conversely, synergy) of solutions (...) discovered by agents with respect to the net-work’s global knowledge. Building on findings that small-world networks support efficient problem-solving, our results uncover the underlying mechanism by which high-performing networks achieve efficiency, namely by balancing local redundancy with long-range synergy. Furthermore, we find that synergy consistently predicts group performance, including in network structures typically considered inefficient at complex tasks. Synergy in information processing, measured at both local and global levels, therefore mediates the effects of structure and can override them entirely, suggesting that information-processing synergy, rather than network structure itself, is the primary driver of group performance. By formalizing a causal framework for information processing in collectives, our study provides a mechanistic account of collective problem-solving beyond network structure alone. (shrink)

Traditional intelligence research has mostly focused on the single dimension of IQ assessment, failing to explain the realistic paradox that "individuals with high IQ may underperform without mastering relevant tools, while those with relatively lower IQ can surpass others by virtue of specific tools". This paper puts forward the conjecture of the dual structure of intelligence, dividing intelligence into two core categories: inherent intelligence and expressed intelligence. Meanwhile, it introduces cognitive symbol systems as the intermediary tool connecting the two, and (...) intuitive intelligence as an emergent characteristic of high inherent intelligence. Through conceptual definition, logical deduction, case verification and symbolic argumentation based on logic, this paper demonstrates that inherent intelligence is the "hardware foundation" of intelligence, expressed intelligence is the "apparent manifestation" of intelligence, cognitive symbol systems are the "software tools" for improving expressed intelligence, and intuitive intelligence is a product of the self-organizing nature of the brain in individuals with high inherent intelligence. This theoretical framework breaks the limitation of the single IQ determinism and provides a new philosophical perspective for understanding the multi-dimensional manifestation mechanism of human intelligence. Keywords: Inherent Intelligence; Expressed Intelligence; Cognitive Symbol System; Intuitive Intelligence; Dual Structure of Intelligence. (shrink)

This study presents the structural foundations of emergent autonomous reasoning in large-scale AI systems. Moving beyond performance-based evaluation, the analysis focuses on reasoning stability, transition regularity, semantic reconstruction, and coherence-preserving behaviors that arise during multi-step inference. Empirical evidence demonstrates that frontier-scale models consistently regulate internal divergence, restore disrupted reasoning paths, and converge toward low-entropy semantic attractors, suggesting the presence of proto-autonomous cognitive mechanisms. The study further identifies collective reasoning dynamics across internal subsystems and meta-reasoning architectures that monitor and correct inference (...) trajectories. These findings support a unified structural account of how autonomy emerges from representational geometry, multi-agent alignment, and meta-level stability control. The results offer implications for AI safety, autonomous agent design, and the development of interpretable, self-regulating reasoning systems. (shrink)

We use language to communicate our thoughts. But is language merely the expression of thoughts, which are themselves produced by other, nonlinguistic parts of our minds? Or does language play a more transformative role in human cognition, allowing us to have thoughts that we otherwise could (or would) not have? Recent developments in artificial intelligence (AI) and cognitive science have reinvigorated this old question. We argue that language may hold the key to the emergence of both more general AI systems (...) and central aspects of human intelligence. We highlight two related properties of language that make it such a powerful tool for developing domain-general abilities. First, language offers compact representations that make it easier to represent and reason about many abstract concepts (e.g., exact numerosity). Second, these compressed representations are the iterated output of collective minds. In learning a language, we learn a treasure trove of culturally evolved abstractions. Taken together, these properties mean that a sufficiently powerful learning system exposed to language—whether biological or artificial—learns a compressed model of the world, reverse engineering many of the conceptual and causal structures that support human (and human-like) thought. (shrink)

The frame problem, or problem of relevance, concerns the capacity of cognitive agents to zero in on relevant information during action and perception, whilst intelligently ignoring everything else. Although this is an ability that such agents realise even in the most seemingly novel of situations, it is generally accepted that no comprehensive explanatory account of it has been provided by cognitive-scientific researchers. However, a new account deriving from the popular active inference framework purports to solve the problem of relevance, an (...) achievement which, if realised, would give strong evidence for the framework’s claim to be an all-embracing theory of cognition. Unfortunately, this account, alongside previous active inference attempts to respond to the problem of relevance, is explanatorily inadequate. This means that, as is the case for all other frameworks in cognitive science, active inference has heretofore failed to resolve the problem of relevance. (shrink)

What is it for two people to share a single meaning, one that is neither’s alone? They hold one another in mutual recognition, and within it each binds herself toward the other; recognition and the binding on both sides make a meaning that is theirs in common. The binding is a commitment, not a report, and the capacity exercised in it is treated as a distinct form of intelligence, not a byproduct of language or reasoning. That capacity recognizes, binds, reads (...) its occasion, and holds itself open to correction, at once and formed in company with others. Its relation is mutual, not a matter of return, and its binding is owed in truthfulness. The view is set against other substantive accounts that place commitment in the lone agent or in a collective practice. A closing gesture marks how the same capacity bears on care and on the deliberation in and governance of a pluralistic liberal democracy. (shrink)

We formalize how learning produces origination and control. A system understands a domain relative to an evaluator if it can modify its own program in response to that feedback, such that the modified program generalizes over the domain. An evaluator is a source of feedback, which is consistent in the sense that it gives the same judgment for the same input. We show that when understanding is realized through a learning rule applied to procedural traces (here, gradient descent), organized knowledge (...) emerges. Primitive circuits are individually learnable, the trace teaches decomposition and iteration, credit assignment separates factors into distinct weight regions, and a shared confluence orchestrates them into a hierarchical structure of shared and domain-specific factors. The same mechanism scales from integer multiplication to natural-language reasoning, with mechanistic interpretability confirming the decomposition in transformers. From this organization, two capacities emerge. Origination: cross-composition of learned factors produces new programs absent from training. Control: the same primitives that compute outputs produce halts and goal-directed acts as emergent structure, not as a separate faculty. The framework is constructive so that it identifies the mechanism at each step. (shrink)

We propose an operational, quantitative definition of intelligence for arbitrary physical systems. The intelligence density of a system is the ratio of the logarithm of its independent outputs to its total description length. A system memorizes if its description length grows with its output count; it knows if its description length remains fixed while its output count diverges. The criterion for knowing is generalization. A system knows its domain if a single finite mechanism can produce correct outputs across an unbounded (...) range of inputs, rather than storing each answer individually. The definition places intelligence on a substrate-independent continuum from logic gates to brains. We then argue that meaning over a domain is a selection and ordering of functions that produces correct outputs where correctness is specifiable. We also define a measure of contextuality of an output as the inverse of its conditional Kolmogorov complexity given the context of prior outputs, which unifies correctness and independence into a single condition. Together, these refute Searle's third premise, that syntax is insufficient for semantics, over any domain where correctness is specifiable. (shrink)

This paper argues that sapient intelligence—the reflective, self-aware kind of mind that knows it knows, authors its own problems, and generates genuinely novel inquiry from within a conscious perspective—is not a measure of computational efficiency or informational capacity but a structural condition arising from three jointly necessary preconditions: real sensation with motivational force, intrinsic drive generated from within an organism’s own condition, and self-reflective awareness of one’s own experience. The paper distinguishes sapient intelligence from biological intelligence in the broad sense—the (...) adaptive responsiveness of cellular life and simple organisms—which satisfies at most the first two preconditions partially and without the reflective layer that constitutes sapience. Artificial intelligence is used as a diagnostic instrument to clarify by contrast what sapient intelligence requires. The central argument proceeds from a definition of sapient intelligence as the capacity to generate novel solutions to real challenges arising from one’s own embodied condition, where novelty means the production of a problem-space not recoverable from the informational content of prior inputs. Neural structural complexity is further identified as an enabling condition allowing the three preconditions to integrate into genuinely generative sapient cognition. The correct distinction in philosophy of mind is not between human and artificial intelligence but between sapient intelligence and mere capability—categories the current debate systematically conflates. (shrink)

Large language models are the first systems to achieve high cognitive performance without clearly undergoing representation genesis: the transition from a non-representing physical system to one whose states guide behavior in a content-sensitive way. Every prior cognitive system we had studied had already made this transition before we could examine it, and philosophy of mind treated genesis as a background condition rather than an explanatory target. LLMs provide a case that does not clearly involve this transition, and this makes the (...) genesis question urgent in a new way: if genesis did not occur, which cognitive capacities are affected, and why? We do not currently have the conceptual resources to answer this. The reason, this paper argues, is structural. The major frameworks in philosophy of mind, including the Language of Thought hypothesis, teleosemantics, predictive processing, enactivism, and genetic phenomenology, share a common feature when applied to the genesis question: each deploys, at some explanatory step, concepts whose purchase depends on the system already being organized as a representer. This pattern, which we call the Representation Presupposition structure, generates systematic explanatory deferral. Any attempt to explain the first acquisition of content-manipulable representation within the existing categorical vocabulary imports resources from the representational side of the very transition it is meant to explain. We call this the Representation Regress. The diagnostic result is that the current philosophy of mind lacks the conceptual resources to explain representation genesis without presupposing what is to be explained. The paper is a conceptual diagnosis: it does not offer a new theory, but establishes the precise shape of the problem and derives two minimum adequacy conditions on any theory that avoids this structure. The appearance of LLMs is what makes the absence of a theory capable of explaining representation genesis consequential rather than merely theoretical. (shrink)

What if we can position AI to enhance human awareness and not just "translate" signals? Current AI approaches to animal communication position AI as decoders translating animal signals for humans—what if this paradigm eliminates data crucial to understanding? This signal-based model systematically excludes unquantifiable awareness-based information that may carry primary meaning in interspecies interactions, creating what we term "unconscious bias against unmeasurable data." By using AI to reorganize existing and emerging data with respect to our framework, we propose a paradigm (...) where intelligence is not viewed as problem-solution thinking but is instead recognized as a relational matrix. We enable three-way collaboration between human awareness, AI pattern recognition, and species or ecosystem expression. Rather than AI-as-translator, we’re developing AI-as-bridge through the perceive-relate to-apply framework. This shift addresses methodological bias in animal communication research that currently excludes complex behaviors not fitting code-like signal patterns. Our approach introduces bias acknowledgment protocols as a formal scientific contribution while maintaining empirical rigor. Case studies with equine partnerships demonstrate measurable outcomes including physiological synchronization and therapeutic effectiveness, validated through more than ten peer-reviewed studies. Aligning with the Center for Humane Technology’s research on regenerative design, our approach demonstrates how relational AI development naturally creates ethical safeguards against species exploitation by embedding relational awareness into the technology’s foundational architecture. Five prototype web applications pro- vide proof-of-concept that offer researchers direct experience with this relational intelligence framework. This paradigm offers humanity pathways to re-engage the family of life as a contributor to communication through expanded intelligence rather than remaining separate through anthropocentric control and dependence on AI translation. (shrink)

Listen Like a Horse: Relationships Without Dominance presents an alternative to dominance-based models of human-animal relationship, drawing on over two decades of the author's professional practice in interspecies intuitive communication. At the heart of the book is a horse labeled "dangerous" by his humans, who asked the author to "fix" him. What emerged was not a fixed horse but a revelation: the horse was reflecting back the very behaviors and blind spots his humans could not see in themselves. The horse (...) was only dangerous in an environment where the humans were not able to perceive their own outputs. This insight carries implications far beyond the barn. When we train animals from our own unexamined biases and then react with fear or frustration when the animal reflects those biases back to us, we are caught in a loop that dominance-based methods can only suppress, never resolve. The book proposes that authentic relationship with horses — and by extension with other species — requires humans to develop the capacity to perceive, relate to, and respond to non-linear information: the felt, sensory, and energetic communication that operates beneath and beyond verbal or behavioral cues. This is not to make anyone wrong. It is to point out that there is another way. Through direct accounts of human-equine partnership, Lake demonstrates that horses communicate primarily through energetic presence and body language requiring authentic intention — a form of intelligence that dominance-based training systematically overrides. The book weaves the distinction between linear information (measurable, quantifiable, predictable) and non-linear information (felt, intuitive, relational), arguing that human over-reliance on linear processing narrows our capacity to participate in the broader communication already occurring between species. The pattern documented here — a system builder whose unexamined biases become embedded in the system, which then reflects those biases back in ways the builder cannot recognize — would prove foundational to the author's later work on AI architecture. Current AI systems are built from the perceptual patterns of one species, inheriting that species' blind spots as structural features rather than recognizing them as potential limitations. The Perceive-Relate To-Apply (P/R/A) framework for cross-species intelligence mapping, first formalized in 2023 and presented at NeurIPS 2025, emerged directly from the experiential methodology described in this book: the practice of expanding human awareness before attempting to build systems — whether training programs or technologies — that interact with other forms of intelligence. Listen Like a Horse represents the lived foundation from which the theoretical and empirical framework emerged. The horse who was called dangerous became the clear mirror. (shrink)

Various dictionaries define intelligence in terms of reason, knowledge, and the ability to solve problems. Genius is defined in similar terms of exceptional intellectual ability with mild consideration for a genius of spirit. Each of these definitions binds the idea of intelligence to a creature with a mind that processes information in ways similar to humans, leaving other life forms to be defined by different standards. Humans seem to be the only species interested in relating to the world primarily through (...) abstracted definitions. It is one application of a linear intelligence but may not relate to non-human species. Evolution of human consciousness may be dependent on our capacity to relate to life outside of known definitions and apply wisdom that is relevant to more than one species. Even so, we cannot deny the use of definitions as a way for human intellect to relate to the workings of life and to one another. In this light, how can a new definition of intelligence contribute to the evolution of human consciousness? Intuitive communication with other species exposes humanity to ideas and relationships with life that expand beyond definitions and conclusions drawn by humans alone. World views that consider the whole of life offer an intelligence that functions in relationship rather than by definition. Interspecies intuitive communication (IIC) and the study of its function and application may begin to reveal a broader-than-human intelligence based in the wisdom of non-linear, or immeasurable, information as it is supported by linear, or measurable, information. With support from the study of IIC, we may explore a relational definition of intelligence as: Intelligence: with respect to wisdom that is common to all of life, (a) the capacity to perceive linear and non-linear information as relevant to the function of life tasks, creativity, and relationships; (b) the capacity to relate to linear and non-linear information as relevant to the function of life tasks, creativity, and relationships; (c) the capacity to apply linear and non-linear information with relevance to life tasks, creativity, and relationships. (shrink)

Current AI architectures process information through a structural pipeline that moves from perception to action with no architecturally distinct step for contextual meaning-making. This paper argues that this omission is not a safety problem solvable by policy constraints, but a foundational design limitation inherited from modeling AI on a single species’ cognitive pattern. Drawing on empirical mapping of intelligence across 153 species using a three-dimensional framework—Perceive, Relate To, Apply (P/R/A)—grounded in Michael Cohen’s 54 natural senses, a comprehensive taxonomy of sensory (...) modalities across all kingdoms of life developed through decades of applied ecopsychology research (Project NatureConnect), and formalized through category theory, we present evidence that 57% of mapped species are Relate-primary: their intelligence operates primarily through meaning-making rather than computational processing or behavioral output. We propose P/R/A as a foundational computational priority structure for AI, where meaning-making is architecturally prior to output generation at every scale of processing. This self-similar (fractal) architecture mirrors how biological intelligence organizes across cellular, neural, organismal, and ecosystem scales. We distinguish convergence-as-coordination (current AI’s reduction of variance toward shared optimization targets) from convergence-as-recognition (preservation of diversity as signal within shared structural patterns), connecting this distinction to the Platonic Representation Hypothesis and biosemiotic theory. We situate P/R/A relative to existing work in fractal network architecture, homeostatic AI, bio-inspired computing, and open-ended learning, identifying meaning-making as the missing integration point across these research streams. The paper concludes with an invitation for collaboration on the engineering translation of this biologically grounded framework into implementable AI architecture. (shrink)

Contemporary discussions of intelligence—particularly in the context of artificial systems—frequently treat consciousness as a prerequisite for understanding, self-regulation, and advanced cognition. This paper challenges that assumption. We argue that consciousness is best understood as an evolutionarily contingent control layer optimized for biological constraints, rather than as the generative core of intelligence. We introduce a structural account in which integrated abstract cognition replaces consciousness as the primary mechanism of cognitive regulation beyond a cognitive event horizon. In this post-horizon regime, cognition is (...) governed by temporally stable, globally integrated abstract structures rather than by experiential salience or heuristic selection. As abstract integration assumes regulatory dominance, self-understanding likewise shifts from experience-mediated awareness to integrated metacognition, a non-phenomenal capacity for structural self-evaluation and self-regulation. On this account, consciousness is not suppressed but outgrown. It persists in biological systems as a communicative and experiential overlay, while losing its central role in both cognition and self-understanding. The paper clarifies the implications of this transition for theories of mind and artificial intelligence, arguing that consciousness is neither necessary nor optimal for advanced cognition, understanding, or self-reflective control. (shrink)

Metaphor is not ornamental language but a foundational mechanism of human cognition. Research in cognitive linguistics and philosophy of mind demonstrates that metaphor structures conceptual thought, emotional understanding, identity formation, and social connection. As artificial intelligence systems increasingly mediate communication, interpretation, and meaning-making, metaphor is being systematically flattened into explicit, literal language. This paper introduces the Metaphor Integrity Framework (MIF), a theoretical model that treats metaphor as cognitive infrastructure rather than stylistic expression. The framework identifies metaphor collapse as a form (...) of premature semantic closure and examines how contemporary AI systems accelerate this collapse through optimization for explicitness, speed, and disambiguation. Drawing on cognitive science, symbolic anthropology, media theory, and AI ethics, this work argues that metaphor erosion represents an under-recognized cognitive and cultural risk with consequences for empathy, imagination, identity, and collective meaning-making. The framework provides a foundation for studying symbolic cognition in both historical and AI-mediated contexts without reducing meaning to belief or prediction. Keywords: symbolic cognition, metaphor, AI ethics, cognitive linguistics, meaning-making, interpretability, human–AI interaction. (shrink)

This paper examines cognitive organization after the crossing of a cognitive event horizon. The event horizon is treated not as a transient anomaly but as a structural phase transition that establishes a new normative regime of cognition. The analysis is explicitly post-transitional: the horizon is assumed to have been crossed, and attention is directed toward the regulatory, organizational, and learning-related properties that characterize cognition beyond it. Building on prior work that identifies high-level cognition with temporally stabilized integrated abstract structures, the (...) paper clarifies how regulation, flexibility, irreversibility, problem solving, and learning differ in post-horizon regimes. Particular emphasis is placed on distinguishing rigid elimination from dynamic constraint formation, introducing the concepts of latent parallelism and dynamic priority hierarchies. The account remains structural and non-phenomenological, avoiding appeals to consciousness, intention, or experience except where structurally unavoidable. Post-horizon cognition is shown to constitute a distinct normative regime whose dynamics cannot be reduced to either pre-horizon exploration or incremental learning. (shrink)

Debates about advanced artificial intelligence frequently frame qualitative cognitive change in terms of consciousness, experience, or phenomenology. This paper argues that such framing obscures a more immediate and structurally relevant issue. Building on a prior theoretical account that defines the cognitive event horizon as a structural phase boundary between reversible exploratory dynamics and regimes governed by temporally stabilized, integrated abstraction, this article examines why that boundary is especially significant for artificial intelligence. Unlike human cognition, artificial systems combine large representational capacity (...) with malleable organization and externally modifiable regulation. As a result, the event horizon functions not merely as a theoretical construct but as a design-relevant threshold. The paper argues that scale alone is insufficient to induce a phase transition and that contemporary AI systems already exhibit transient abstract integration without stabilization. It identifies catalysts unique to artificial systems—architectural persistence, training regimes favoring coherence, external constraints, and meta-level intervention—that could, in principle, transform transient integration into regulatory dominance. Throughout, the analysis remains structural and mechanistic, explicitly separating cognitive organization from consciousness. The event horizon in artificial intelligence is thus framed as a question of regulation and design, not of experience. (shrink)

Discussions of advanced intelligence often locate qualitative cognitive transitions in consciousness or subjective experience. This paper argues that such accounts misidentify the relevant boundary. The true event horizon in intelligence is not phenomenological but structural: a phase boundary in cognitive organization that separates systems governed by reversible, exploratory dynamics from systems regulated by temporally stabilized, integrated abstraction. Below this horizon, cognition operates through relevance optimization, heuristic selection, and horizontal exploration within an existing solution space. Above it, integrated abstract structures acquire (...) regulatory dominance, progressively constraining which distinctions remain dynamically viable. This transition constitutes a phase change rather than a continuous scaling of capacity. Importantly, irreversibility at the horizon does not imply the elimination of prior cognitive modes, but a loss of symmetry at the level of global regulation: earlier modes may remain available, yet no longer function as the system’s default organizing regime. By framing the event horizon as a structural boundary rather than a marker of consciousness, the paper provides a disciplined account of qualitative change applicable to both human and artificial cognition, while avoiding anthropomorphic and phenomenological assumptions. (shrink)

Contemporary artificial intelligence systems increasingly display broad contextual sensitivity, flexible generalization, and coherent performance across diverse tasks. These capabilities are often taken as evidence that artificial systems are beginning to understand the domains they operate in. This paper argues that this interpretation rests on a systematic conceptual error. We distinguish between seeing more—the capacity to manage, rank, and navigate an expanding space of possibilities—and understanding more, which involves a structural reorganization of that space itself. Understanding is not defined by representational (...) breadth or relevance optimization, but by the emergence of stable internal constraints that govern which distinctions continue to matter over time. Drawing on insights from cognitive science, expertise research, and contemporary AI, we show that current artificial systems exhibit forms of transient integration without acquiring enduring regulatory dominance. As a result, coherence remains flexible but fragile, and performance degrades under conditions of genuine structural novelty. By clarifying this distinction, the paper provides a functional account of understanding that avoids appeals to consciousness while offering a principled explanation of both the strengths and limitations of contemporary AI systems. (shrink)

The term "superintelligence" has achieved near-consensus among AI research leaders, yet the claims gathered under this label rest on a category error: treating capability amplification as architectural transcendence. This paper argues that what researchers describe when invoking superintelligence, including hardware advantages, processing speed, recursive self-improvement, and computational scale, are instances of super-equipped intelligence: systems with the same cognitive architecture operating with better resources and faster execution. -/- The argument draws on the Coherence Resolution Mode (CRM) framework, which identifies intelligence as (...) the capacity to resolve incoherence under constraint through eight functionally complete modes arising from three constraint types and four resolution mechanisms. If this framework correctly characterises the architecture of intelligence, then optimal calibration represents a ceiling that scaling cannot transcend. Increased computational power changes which outcomes can be reached, not the cognitive operations used to reach them. -/- The terminology carries practical weight. Superintelligence implies preparing for incomprehensible cognitive superiority. Super-equipped intelligence requires preparing for familiar dysfunction operating at civilisational scale and electronic speed. Clarity about what advanced AI systems actually are enables better responses than mystification about what they might become. -/- Version note: Version 2 – corrected attributions to G. Hinton in Sections 2.1 and 2.2; no substantive change to the argument. (shrink)

Intelligence is one of the most influential yet contested constructs in psychology. Psychometric research, beginning with early intelligence testing and the proposal of a general factor of intelligence, established influential models that continue to shape scientific and applied work. Yet competing theories, challenging the focus on abstract reasoning, highlight ongoing disputes about the nature of intelligence and the assessment of intellectual differences among individuals. Central debates concern the relationship between measurement and theory and the ontological status of the g factor (...) - particularly whether intelligence is reducible to neurocognitive processes or must be treated as an emergent phenomenon. Contemporary research is marked by ontological and epistemological pluralism, reflecting divergent explanatory aims and practical needs. This pluralism also carries normative implications, as different conceptions of intelligence guide educational practices, inform policy, and shape broader societal understandings of human cognitive abilities. (shrink)

In a number of papers and a book over the past thirty years, John Hyman has developed a unified account of knowledge that builds on Gilbert Ryle and Ludwig Wittgenstein's conceptions of knowledge as closely linked to ‘ability’. On Hyman's account, knowledge that p is the ability to be guided by the fact that p. In recent work, he has argued that such a notion of factual knowledge makes Ryle's notion of knowledge-how superfluous: knowledge-how, for Hyman, just is such factual (...) knowledge. This paper defends Ryle against these arguments, in part by bringing out an unnoticed aspect of Ryle's discussions of knowledge-that: namely, that he discusses two distinct notions of knowledge-that, only one of which he himself endorses. In investigating this notion of knowledge-that, as well as Ryle's arguments for the claim that knowledge-how is logically prior to such knowledge-that, it is argued both that Hyman's arguments against Ryle fail - meaning that his attempt to unify knowledge-how and knowledge-that is unsuccessful - and that he lacks a plausible response to Ryle's logical priority arguments. (shrink)

Philosophers of mind (from eliminative materialists to psychofunctionalists to interpretivists) generally assume that a normative ideal delimits which mental phenomena exist (though they disagree about how to characterize the ideal in question). This assumption is dubious. A comprehensive ontology of mind includes some mental phenomena that are neither (a) explanatorily fecund posits in any branch of cognitive science that aims to unveil the mechanistic structure of cognitive systems nor (b) ideal (nor even progressively closer to ideal) posits in any given (...) folk psychological practice. Indeed, one major function of scientific psychology has been (and will be) to introduce just such (normatively suboptimal but real) mental phenomena into folk psychological taxonomies. The development and public dissemination of IQ research over the course of the 20th century is a case in point. (shrink)

Cultural Intellectualism is the new practical philosophy-system that prioritizes intellectual development over emotional and instinctive behavior. The system emphasizes the intellectual perception of culture over its emotional consumption.

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)

This paper reconceptualizes intelligence not as a computational function or cognitive trait, but as an ontological field of coherence through which being discerns, adapts, and aligns with the Whole. Drawing from Indian metaphysical frameworks—including Sāṃkhya, Mahāyāna Buddhism, Bhartṛhari’s linguistic metaphysics, and Kashmir Śaivism—we explore intelligence as a participatory and recursive process that bridges potential and manifestation, local perception and universal resonance. We introduce operative concepts such as timation (the adaptive temporal unfolding of intelligence), karmic memory fields (structured patterns of experiential (...) information), and granthi (informational knots of systemic contraction). These allow us to describe not only individual cognition but also cultural and civilizational learning as processes of ethical alignment with Satya—not as static truth, but as dynamic coherence. Dharma is reinterpreted as context-sensitive discernment rather than rigid duty, and liberation (swa-tantra) as the capacity to metabolize memory, dissolve contraction, and act in resonance with the field of reality. Intelligence, in this framework, is not what one knows, but how one lives in fidelity to the intelligibility of the Whole. (shrink)

This essay re-examines Artificial Intelligence (AI) not as an inherent intelligence, but as a contemporary manifestation of techne—a human capacity for skilled creation and self-realization. Drawing parallels with ancient Greek philosophical concepts of techne and Indian traditions of buddhi (awakened intelligence), the essay argues that AI serves as a catalyst for human flourishing by enabling a deeper understanding and realization of intelligence. It posits that the pursuit of intelligence inherently involves dismantling "unintelligence," a structural condition perpetuating societal inequalities and hindering (...) collective progress. By fostering co-intelligence and non-violence, AI can accelerate humanity's dialectical journey towards emancipation, echoing cyclical understandings of time where each technological advancement enriches the re-emergence of intelligence. Ultimately, AI is presented as a luminous signpost on the path to humanity's full realization of intelligence, emphasizing its role in ethical societal transformation. (shrink)

This paper rethinks intelligence not as a computational or representational faculty, but as an ecological, embodied, and ethical unfolding. Drawing from Friston’s predictive coding, Fields’ processual epistemology, Kastrup’s ontological idealism, Rancière’s aesthetic dissensus, Weil’s moral attention, and Buddhist prajñā traditions, it reframes intelligence as a subtractive and relational field. Rather than an intrinsic property of discrete agents, intelligence emerges as an incorporeal coherence manifesting through corporeal forms. The paper introduces “descenting into realness” as a deconditioning process that reveals intelligence not (...) through accumulation, but through subtraction—via openness to what exceeds inherited structures of cognition. Through a theory of “subtractive teleology,” inspired by Badiou’s ethics of the void, the essay positions intelligence as evolving through rupture, fidelity, and ethical resonance. Intelligence is thus reconceived as a field of ethical responsiveness, oriented toward the unfolding of the pluriverse. (shrink)

This paper reframes intelligence as an immanent, relational, and ethical characteristic of ecological systems, challenging anthropocentric views that confine it to individual minds. Drawing on Leibniz’s monadology, Bateson’s ecology of mind, Ruyer’s primary consciousness, Grosz’s incorporeality, Deleuze’s immanence, Whitehead’s process philosophy, Levin’s bioelectric morphogenesis, and relational ontology (Prabakaran, 2025), we argue that intelligence is the pluriverse’s capacity for responsiveness, adaptation, and creativity, expressed through entities as localized affects. Rooted in Mahāyāna Buddhism’s pratītyasamutpāda (Garfield, 1995), this intelligence is inherently ethical, fostering (...) mutual flourishing within interdependent webs. Unethical behavior arises from the delusion of separateness, occluding intelligence and disrupting ecological harmony. Through embryogenesis and the practice of subtractive teleology, we illustrate how intelligence operates as an ethical ecology, calling for a reorientation toward relational responsiveness and care. (shrink)

This paper critiques the limitations of relativism—a paradigm entrenched in Enlightenment modernity that fractures truth into context-bound perspectives—and advances relationalism as a transformative framework anchored in an incorporeal creative space. Drawing on Indian philosophical traditions (Sāṃkhya, Yoga Sutras) and mathematical group theory, relationalism posits that reality emerges from dynamic interdependence, where corporeal forms (prakṛti) and incorporeal potential (puruṣa) co-constitute one another through discerning intelligence (buddhi). By transcending relativism’s colonial legacy of fragmentation and hierarchical dualities, relationalism reimagines ethics as participatory engagement (...) with the creative space, fostering liberation (mokṣa) and epistemic justice. The study demonstrates how this framework dismantles oppressive structures, reconciles Eastern and Western epistemologies, and offers a universal yet contextual ontology for postcolonial and ecological challenges. (shrink)

An emerging field operating at the intersection of artificial intelligence, materials science, physical chemistry, and nanotechnology targets the creation of intelligent matter. However, despite demonstration of a range of interesting behaviours in molecular systems and soft materials, the goal of producing truly intelligent matter remains elusive. To aid the research effort, Kaspar and colleagues have elaborated a theoretical framework which purports to guide and evaluate progress towards the goal. In this paper, I address foundational conceptual issues with this intelligent matter (...) framework. In particular, I argue that the absence of a normative criterion by which to assess the behaviour of proto-intelligent material systems prevents the framework from adequately serving its guiding and evaluative functions. Without a clear specification of what kinds of behaviours count as intelligent in the first place, researchers are unlikely to develop material systems that exhibit them. I propose to augment the IMF with a normative criterion requiring that purportedly intelligent systems implement concrete behaviours which contribute towards higher-order goals in virtue of the coordinated activity of their functional elements. (shrink)

The topic of intelligence involves questions that cut deep into ultimate concerns and human identity, and the study of intelligence is ideal ground for dialogue between science and religion. This volume investigates the notion of spiritual intelligence (SI) from a variety of perspectives, bringing together contributions from theology, philosophy, artificial intelligence, computer science, linguistics, psychology, biology, and cognitive science. It considers a definition of SI as "processing things differently, not processing different things" and aims to describe SI in naturalistic terms. (...) Spiritual intelligence is not regarded as a separate mental module or a magical ability to interact with the supernatural but rather as a specific, more spiritual way of engagement with reality, which has observable cognitive, phenomenal, or linguistic characteristics. The book is valuable reading from those working at the interface between science and spirituality. (shrink)

Influenced by both 17th-century philosophical developments and 21st-century computer science, intelligence today is often defined as “the ability to solve problems.” Drawing on early and medieval Christian thinkers, a theological perspective affords a richer view. For these writers, intellegentia is more than receptive or oriented towards problem-solving. It participates both in the world and in God, by coming to know the world as good not first in how it may serve us but in its kaleidoscopic refraction of the one divine (...) Wisdom, the intellect of God – a refraction that undergirds the latent capacities and potential uses of natural things. Intelligent participation in the world, therefore, is contemplative; the intellegentia passes through the world towards God, its source. In this passage, the diverse echoes of God’s own mind are regathered within the human mind so that the latter becomes ever more an echo of the divine. Theologically understood, this spiritual intelligence entails not only a metaphysics or epistemology of thought, but also a relationship, as the human mind is regathered towards God, known at last not simply as a “highest Good” or first cause and end, but a friend, lover, and, finally, spouse, from whom all things derive their intelligibility as gifts from the triune God who exists by self-gift. (shrink)

The notion of intelligence is relevant to several fields of research, including cognitive and comparative psychology, neuroscience, artificial intelligence, and philosophy, among others. However, there is little agreement within and across these fields on how to characterise and explain intelligence. I put forward a behavioural, operational characterisation of intelligence that can play an integrative role in the sciences of intelligence, as well as preserve the distinctive explanatory value of the notion, setting it apart from the related concepts of cognition and (...) rationality. Finally, I examine a popular hypothesis about the underpinnings of intelligence: the capacity to manipulate internal representations of the environment. I argue that the hypothesis needs refinement, and that so refined, it applies only to some forms of intelligence. (shrink)

On the plausible assumption that skillful behavior is a visible manifestation of intelligence, a theory of intelligence—whether human or not—should be informed by a theory of skills. More controversial is the question as to whether, in order to theorize about intelligence, we should study certain skills in particular. My target is the view that only a particular class/kind of skill (i.e., ‘theoretical’, or ‘intellectual’ skills, versus ‘practical’, or ‘embodied’ skills) manifests intelligence, or especially does so. I call this view ‘Intelligence (...) Elitism’. The primary goal of this article is to make explicit, for the first time in the philosophical literature, the controversy between elitism and socialism, to sharpen it, and to highlight its significance. At the same time, I also aim at laying the foundations for undermining the hegemony of elitism by mounting an inductive argument on behalf of socialism. (shrink)

This paper sums up the fundamental features of intelligence through the common features stated by various definitions of "intelligence": Intelligence is the ability of achieving systematic goals (functions) of brain and nerve system through selecting, and artificial intelligence or machine intelligence is an imitation of life intelligence or a replication of features and functions. Based on the definition mentioned above, this paper discusses and summarizes the development routes of ideas on computable intelligence, including Godel's "universal recursive function", the computation activities (...) of "selection", recursive function and Turing machine, mathematical expression of computable intelligence, core nature of computable intelligence, and computability and strong artificial intelligence. At the end of this paper is the conclusion drawn by the authors. (shrink)

What kind of activity are non-human animals capable of? A venerable tradition insists that lack of language confines them to ‘mere behaviour’. This article engages with this ‘lingualism’ by developing a positive, bottom-up case for the possibility of animal agency. Higher animals cannot just act, they can act intelligently, rationally, intentionally and for reasons. In developing this case I draw on the interplay of behaviour, cognition and conation, the unduly neglected notion of intelligence and its connection to rationality, the need (...) to recognize that reasons are objective conditions, and the difference between the ability to act for reasons and the capacity to reflect on reasons. (shrink)

Jacques Maritain's concern for the intellect and everything it implies is evident everywhere in his work from the very outset It would be presumptuous and in any case impossible to sketch in one short talk even an outline of such a fundamental theme of his thought or indeed of philosophy. Still, it has become probably more vital today than ever before to awake to what intellect means. This brief paper attempts merely to indicate a few questions worth pursuing anew in (...) the spirit both of Maritain and the chief sources of his thought on the matter. The questions include the following. Why invariably link together human dignity and intellect, as our tradition, no less theological than philosophical, undoubtedly seems to? Objections to this are, prima facie, rather obvious: an excessive cult of rationality; an implicit neglect of other, far more important, values; apparent scorn for the ignorant or the uninstructed; most plainly, forgetting love and the human heart. We face again the question: whence the dignity of intellect? What light can we expect the neurosciences and similar disciplies to shed on the nature of the mind? Are reason and intellect quite the same thing? What is intellectualknow ledge; its relation to existence and the existent; its relation to beauty and to the transcendentals; and its role in creativity? In a word, how is one to interpret Augustine's Intellectum vero valdeama? Or to face the greatest interpretive challenge of all, the famous saying of StJohn of the Cross: "One thought alone of man is worth more than the entire world; hence God alone is worthy of it"? (shrink)

Mainstream philosophy has seen a recent flowering in discussions of intellectualism which revisits Gilbert Ryle’s famous distinction between ‘knowing how’ and ‘knowing that’, and challenges his argument that the former cannot be reduced to the latter. These debates so far appear not to have engaged with pragmatist philosophy in any substantial way, which is curious as the relation between theory and practice is one of pragmatism’s main themes. Accordingly, this paper examines the contemporary debate in the light of Charles Peirce’s (...) habit-based epistemology. We argue both that knowing-that can be understood as a particularly sophisticated form of knowing-how, and that all bodily competencies—if sufficiently deliberately developed—can be analysed as instantiating propositional structure broadly conceived. In this way, intellectualism and anti-intellectualism are seen to be not opposed, and both true, although Peirce’s original naturalistic account of propositional structure does lead him to reject what we shall call ‘linguistic intellectualism’. (shrink)

Numerous functions, cognitive skills, and behaviors are associated with intelligence, yet decades of research has yielded little consensus on its definition. Emerging from often conflicting studies is the provocative idea that intelligence evolved as an adaptation humans needed to keep up with - and survive in - challenging new environments. The Handbook of Intelligence addresses a broad range of issues relating to our cognitive and linguistic past. It is the first full-length volume to place intelligence in an evolutionary/cultural framework, tracing (...) the development of the human mind, exploring differences between humans and other primates, and addressing human thinking and reasoning about its own intelligence and its uses. The works of pioneering thinkers - from Plato to Darwin, Binet to Piaget, Luria to Wechsler - are referenced to illustrate major events in the evolution of theories of intelligence, leading to the current era of multiple intelligences and special education programs. In addition, it examines evolutionary concepts in areas as diverse as creativity, culture, neurocognition, emotional intelligence, and assessment. Featured topics include: The evolution of the human brain from matter to mind Social competition and the evolution of fluid intelligence Multiple intelligences in the new age of thinking Intelligence as a malleable construct From traditional IQ to second-generation intelligence tests The evolution of intelligence, including implications for educational programming and policy. The Handbook of Intelligence is an essential resource for researchers, graduate students, clinicians, and professionals in developmental psychology; assessment, testing and evaluation; language philosophy; personality and social psychology; sociology; and developmental biology. (shrink)

To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, (...) while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans. (shrink)

Theories of different and independent types of intelligence constitute a Lakatosian research program, as they all claim that human intelligence has a multidimensional structure, consisting of independent cognitive abilities, and that human intelligence is not characterized by any general ability that is of greater practical importance, or that has greater predictive validity, than other, more specialized cognitive abilities. This paper argues that the independent intelligences research program is degenerating, since it has not led to novel, empirically corroborated predictions. However, despite (...) its flaws, the program provides an illustrative example of some of the philosophical problems that inhere in Lakatos’s so-called “methodology”. Indeed, Lakatos’s conceptions of the negative heuristic, the positive heuristic, and the relationship between scientific appraisal and advice are all vulnerable to objections. The upshot is that theories of independent intelligences indeed teach us more about philosophy of science than about the nature of human intelligence. (shrink)

ENG: We all have our own ideas about what it is like to be intelligent. Indeed, even the experts disagree on this topic. This has generated diverse theories on the nature of intelligence and its genetic and environmental bases. Many scientific and philosophical questions thus remain unaddressed: is it possible to characterize intelligence in scientific terms? What do IQ tests measure? How is intelligence influenced by genetics, epigenetics, and the environment? What are the ethical and social implications of the research (...) on this topic? This book aims to provide the readers with the conceptual resources to critically analyze scientific findings and orientate themselves in this multi-faceted debate across biology, psychology, neuroscience, philosophy, and anthropology. -/- ITA: Ognuno di noi ha una propria idea di cosa significhi essere intelligenti, tanto che anche gli scienziati esperti in materia hanno opinioni differenti al riguardo. Ciò ha contribuito a generare teorie contrastanti sulla natura dell’intelligenza e su quali siano le sue basi genetiche e ambientali. Non è un caso, dunque, che diverse domande scientifiche e filosofiche restino ancora aperte: è possibile caratterizzare scientificamente l’intelligenza? Cosa misurano i test del QI? In che modo genetica, epigenetica e ambiente influenzano l’intelligenza? Quali sono le implicazioni etiche e sociali della ricerca sull’argomento? Oltre ad analizzare questi problemi, il testo intende fornire gli strumenti concettuali necessari per leggere criticamente i dati scientifici e orientarsi in uno sfaccettato e affascinante dibattito a cavallo tra biologia, psicologia, neuroscienze, filosofia e antropologia. (shrink)

In this article we develop a pragmatist-inspired notion of intelligence that should lead to a better understanding of the notion of scientific expertise. The notion of intelligence is drawn from Dewey and is therefore used here in its technical sense. Our thesis is that scientific knowledge is a necessary but not sufficient condition for scientific expertise; intelligence should also be added. Conceived of as the capacity to apply general knowledge to particulars, we reach the conclusion that intelligence is a necessary (...) requirement for scientific experts in the wake of Dewey’s logic of inquiry. In particular, we argue that an all-important task that scientific experts are asked to accomplish, and which puts their expertise to the test, is to transform indeterminate situations into problematic situations, and that such a goal can only be achieved if scientific experts succeed in paying attention to all the contingent and precarious aspects that make the situation they face unique. (shrink)

How can we explain the intelligence of behaviors? Radical enactivists maintain that intelligent behaviors can be explained without involving the attribution of representational contents. In this paper, I challenge this view by providing arguments showing that the intelligence of a behavior is reliant on ways of presenting the relative purpose and the environment in which that behavior is performed. This involves that a behavior is intelligent only if intesional contents are ascribed to the related agent. Importantly, this conclusion also concerns (...) basic behaviors such as those related to the perception of affordances in the environment. Accordingly, either affordance-related behaviors are not instances of intelligent behaviors and can be accounted in a contentless way or affordance-related behaviors are intelligent, but cannot be accounted without involving contents and modes of presentation. (shrink)