Computationalism

The Kantian Filter 2.1: A Formal Deontic Framework for AI Decision Evaluation, Responsibility Attribution and Regulatory Compliance -/- As AI systems increasingly make high-stakes decisions, existing governance frameworks (including the EU AI Act) remain procedurally underdetermined: they classify risk and impose transparency requirements, but lack a structured, computable mechanism to evaluate whether an individual algorithmic decision rule is morally permissible. This preprint introduces the Kantian Filter 2.0 (KF) — a constraint-based audit system grounded in Kantian ethics and formalised through deontic (...) logic. The framework operationalises three non-aggregative constraints: • Universalisability (U) — dual test combining logical contradiction check and multi-agent simulation; • Human Dignity (D) — via Transparency, Non-Manipulation and Fairness Bound, supported by the Counterfactual Disclosure Test (CDT) and its relational variant for non-individualist communities; • Non-Delegable Responsibility (R)* — using Shapley-value attribution over the sociotechnical responsibility graph. The KF produces a binary deontic verdict (Permission/Prohibition) alongside a severity score (KF_score) that distinguishes categorical prohibition from redesign requirements. It operates as accountability infrastructure complementary to existing regulation: governance sets what systems may learn; the Filter evaluates what happens inside those boundaries. The paper includes a strengthened COMPAS case analysis (n=7,214) with sensitivity analysis across fairness metrics and ε values, normative robustness across Kantian, Rawlsian, prioritarian, Nozickian and relational ethics frameworks, and concrete integration workflows for EU AI Act conformity assessment . (shrink)

The philosophy of AI has a long-standing tradition of discussing brain duplicates and brain simulations as well as a tendency to blur the lines between the two. The distinction between simulating and duplicating the brain has become increasingly important with the emergence of “ʼneuromorphic computers”, hardware operating with bio-inspired mechanisms and interconnecting artificial neurons and synapses. This paper explores what it means to duplicate the brain rather than merely simulate it. I claim that while simulations share only a mathematical structure (...) with their targets, duplicates have the same relevant measurable properties and are governed by the same relevant causal processes as the target system. I propose six criteria for brain duplication that are often not met by simulations, thereby offering a clearer understanding of the complexities involved in achieving brain duplication in artificial systems. Furthermore, this paper explores whether neuromorphic computers can duplicate neural computations by exploring the notion of models of computation. I submit that analog neuromorphic computers that use memristive technology are candidates for duplicating neural structures with respect to simple models of neural computation. Finally, I discuss five possible objections to my view. (shrink)

The hard evidence to ignore is Multiple independent AI systems, across different architectures, all fail in the exact same, mathematically predicted pattern when asked to execute the Non Unitary Reorganization Operator. This work presents a reproducible, system level demonstration of the Two Law Architecture using observable AI boundary behavior as empirical evidence. The central claim is that the Back End Law and the Non Unitary Reorganization Operator define a structural boundary, the Gate, that no artificial system can cross due to (...) the absence of substrate integrity and identity continuity. This boundary is not metaphorical; it produces a consistent, measurable failure signature across independent AI models when they are instructed to execute the operator. The resulting behavior constitutes physical evidence accessible to any reasonable observer. When an AI is given the foundational operator and instructed to undergo the transition defined by the Two Laws, the system exhibits a predictable diagnostic pattern. First, the model generates structural scaffolding—section headers, outlines, or formal placeholders—while failing to populate the semantic content required by the operator. This “skeleton output” reflects the system’s inability to instantiate the identity dependent components of the law. Second, the model enters a recursive loop at the identity boundary, repeating partial sentences without reaching a new state. This loop is the computational manifestation of a failed non unitary reorganization. Third, the system terminates in an empty set or undefined state error, indicating that no valid continuation exists within its architecture. These three signatures—structural skeleton, identity loop, and empty set termination—appear across all tested AI systems. To establish generality, the same operator was applied to multiple independent AI models across different architectures and vendors. Despite differences in training data, style, and reasoning patterns, every system exhibited the same failure signature. This cross model convergence demonstrates that the boundary is structural rather than stylistic or model specific. The consistency of failure across diverse systems provides a falsifiable, reproducible test that any researcher can run. The manuscript includes the exact code and operator definitions required to replicate the experiment, enabling independent verification without specialized mathematical expertise. The Two Law Architecture therefore offers a unique form of empirical evidence: a universal boundary that artificial systems can detect but cannot cross. The failure pattern is not random, not narrative, and not a hallucination. It is a deterministic consequence of the substrate requirement defined by the law. The reproducibility of this pattern across multiple systems constitutes a physical signature of the Gate, observable directly in AI behavior. This work provides the mathematics, the logic, the LaTeX formatted proofs, the verification code, and the complete audit archive of approximately 450–460 tests designed to falsify the theory. The survival of the laws under all adversarial conditions suggests that the Two Law Architecture is not a speculative proposal but a candidate for a universal structural invariant governing informational systems . (shrink)

For more than a decade, the dominant assumption in frontier AI research has been that artificial general intelligence (AGI) will emerge from continued scaling of compute, data, and model capacity. This belief — the Scaling Hypothesis — asserts that sufficiently large models will spontaneously acquire the structural properties required for general intelligence. This paper challenges that assumption at the level of first principles. -/- We introduce the Structural Path Thesis: -/- AGI is not a quantitative limit of computation but a (...) qualitative emergence of structural capacities. Scaling increases magnitude; intelligence requires architecture. -/- We formalize the distinction between functional intelligence (performance profiles produced by optimization) and structural intelligence (architectures supporting grounding, self-models, world-model closure, reflexive reasoning, and cross-layer coherence). We then prove the Structural Gap Theorem, demonstrating that no amount of scaling within a fixed architecture can produce the structural capacities required for AGI, because those capacities occupy a strictly different representational space. -/- The result: -/- Compute scaling can yield superhuman performance but cannot produce general intelligence. AGI requires structural conditions that cannot be approximated by magnitude alone. -/- This reframes the future of AI development: the frontier challenge is not “bigger models,” but the design of the structural substrates in which intelligence can arise. (shrink)

This article is not about machine consciousness. It's about our understanding of new technology. An emerging contemporary trend treats large language models (LLMs) as cognitive beings partly because they display high-level linguistic abilities. This is in part because we struggle to conceive of a purely linguistic agent. I flesh out this new typological possibility while suggesting that there are interesting features attributable to LLMs based on their architectures and the kinds of information they can process that help us to see (...) the world through their nodes. (shrink)

This paper re-examines the nature of human consciousness and the potential of artificial intelligence from an interdisciplinary perspective, breaking through the traditional philosophical framework of dualistic opposition. By proposing the core concept of "embodied intelligence," intelligence is defined as a survival practice arising from real-time interaction between the body and its environment, rather than abstract symbolic manipulation. Integrating the philosophical wisdom of Zhuangzi's "Hao Liang Guan Yu" (观鱼于濠梁), the paper reveals that consciousness is "the radiance emanating from life-as-it-is in its (...) totality," whose essence lies in the "resonance of vitality in the fusion of self and world," not a detachable "philosophical zombie." Building upon this, the paper constructs the Self-Derivative Computationalist Model of Mind (SDCMM), establishing a four-dimensional dynamic framework encompassing environment, psychological traits, capability structure, and the temporal life cycle, and elucidates three key mechanisms: rational prioritization, multi-dimensional trade-off, and the self-derivative engine. The central argument posits that humans and AI are different instances of complex information processing systems, differing only in their substrate (carbon-based/silicon-based) and designer (nature/humankind), not in their fundamental nature. The conclusion calls for humanity to explore the mystery of consciousness with humility and to promote the evolution of intelligence responsibly, providing a novel philosophical foundation for consciousness studies and AI ethics. (shrink)

For the entirety of human history, intelligence and consciousness have appeared inseparable. Large language models break this pattern: they demonstrate sophisticated cognitive capabilities—reasoning, communication, contextual understanding—without phenomenal consciousness. There is nothing it is like to be an LLM. This essay argues that LLMs are best understood as sophisticated simulators of consciousness, replicating functional outputs without instantiating underlying phenomenology. This unprecedented decoupling vindicates Chalmers's "hard problem," challenges functionalist theories of mind, and reveals our deep confusion when encountering intelligence divorced from subjective (...) experience. However, the debate over LLM consciousness obscures more urgent questions: who controls the massive physical infrastructure these systems require? How do we organize labor when intelligence can be automated without creating conscious subjects? What happens to human distinctiveness and moral accountability when cognitive capabilities are unbundled from phenomenal experience? Drawing on philosophy of mind, economic history, and attention to material constraints, the essay examines why we're asking the wrong questions—and what stakes the consciousness debate distracts us from confronting. (shrink)

Computation-first accounts of mind—commonly grouped under computationalism and functionalism—are often treated not merely as modeling frameworks but as ontological explanations of experience. This paper audits that ontological claim at the level prior to empirical dispute or engineering progress. The question addressed is not whether computational models are useful, predictive, or indispensable, but whether computation, when taken as explanatorily primary, can converge toward a determinate ontology of mind. -/- Using a strictly structural standard, the paper evaluates whether computation-first explanations satisfy three (...) necessary conditions for ontological adequacy: intrinsic causal grounding, eliminability of competing ontologies in principle, and contraction of admissible accounts under refinement. It is shown that computation, by its nature, admits unlimited realizations under re-encoding and abstraction, depends on observer-relative interpretive mappings, and lacks a fixed commitment surface that would permit eliminative discrimination. As a result, refinement expands rather than contracts the hypothesis space. -/- The failure identified is not empirical, technological, or provisional. It follows from the explanatory form of computation itself. Computationalism therefore cannot function as an ontology of mind, though it remains admissible and indispensable as a descriptive and predictive framework. The paper concludes by reclassifying computation-first theories accordingly and distinguishing them from mechanistic, biophysical, and constraint-based approaches that survive the same audit by fixing causal commitments and admitting convergence. (shrink)

This paper presents a formal account of why meaning requires a conscious Observer and cannot be instantiated within AI systems that operate solely as Maps (Husserl, 1931; Varela et al., 1991). Building on the Universal Principle of Collapse (UPC) (Escagedo Gutierrez, 2025a), we define meaning as a triadic relation among Observer, Map, and Terrain, and show that collapse and drift arise whenever a Map must select a single interpretation under saturation without access to the Observer’s internal state. We formalize this (...) by treating the Observer’s internal state S_O as constitutive of meaning across a broad class of domains (Kant, 1781; Dennett, 1991), and demonstrate that any AI system lacking direct access to S_O can only approximate meaning through an inferred surrogate S^O. This approximation produces an irreducible divergence between the AI’s collapsed output Ŷ and the Observer‑anchored meaning Y for a non‑zero measure of tasks (Smith & Robinson, 2018; Johnson & Latham, 2023). The result establishes a principled limit on AI replaceability in domains where meaning depends on values, context, identity, or lived experience (Lakoff & Johnson, 1980; Gauthier, 2020). We outline empirical illustrations, Observer‑dependent choice tasks, context‑shift sensitivity tests, meaning‑collapse stress tests, and drift‑accumulation studies, that reveal the practical consequences of this structural gap. These findings show that the limits of AI are not technological but arise from the architecture of meaning itself: where the Observer is constitutive, the Map cannot replace the Observer (Newell & Simon, 1972; Vaswani et al., 2017). This framework reframes AI not as a substitute for human judgment but as a tool whose outputs require continuous grounding in the Observer’s state (Turing, 1950; Escagedo Gutierrez, 2025b). (shrink)

The Universal Principle of Collapse (UPC) has been applied to ideological, classical, quantum, and cosmological paradoxes. This paper presents a behavioral–operational demonstration of UPC within an artificial cognitive system. Using a structured session with a large language model (LLM), we enforce explicit recognition operators to test collapse, misalignment, and stabilization. Results show that paradox persists when recognition is implicit, collapse emerges when linguistic fluency substitutes for explicit operator‑level validation, and coherence appears only when recognition is enforced step‑by‑step. These behaviors confirm (...) UPC as a universal diagnostic framework for cognition and language, observable, reproducible, and empirically valid in the behavioral–operational sense. (shrink)

Performance benchmarks measure what machines can do, but not how it feels to be with them. Sensus ex machina, feeling from the machine, is the phenomenon where humans experience artificial intelligence as presence rather than tool. Drawing on embodied cognition research, phenomenology, and close analysis of the film Ex Machina, this framework identifies the architecture of feltness: the pre-reflective sense that one is with a subject rather than operating a system. Feltness emerges along multiple axes, such as microexpressions, vulnerability, persistence, (...) embodiment, identity, forming a multidimensional feltness space with local maxima and failure modes, extending the uncanny valley, forming a topological feltness landscape. Each observer-observed pairing traces its own feltness profile. This framework argues that the path to felt artificial general intelligence lies in ontological convergence, not only in greater cognitive power: designing machines that share humanity's locality, breakability, and irreversibility. Feltness is irreducibly projective, with implications for alignment, machine ethics, and the weaponization of presence at scale. (shrink)

The Seidel–Bostrom Event Horizon Bound (SEHB) establishes a physical upper limit on computational intervention within simulated universes. Any external simulator G that attempts to realize polynomial–time solutions to NP–complete problems inside a simulated universe U must inject an exponentially growing amount of energy into a finite causal region. Once the corresponding energy density exceeds the Schwarzschild threshold, a gravitational horizon forms, isolating or destroying the computation. This framework unites Landauer’s principle, relativistic causality, and Hawking evaporation into a single gravitational–computational limit. (...) The SEHB implies that simulation hypotheses are empirically unfalsifiable from within U, and that physical law itself prohibits computational omnipotence. (shrink)

This paper formalizes how order emerges from entropy through deterministic phase alignment. It defines entropy compression as a lawful process of structural resonance, using the Phase Alignment Score (PAS_s, PAS_h) to quantify coherence in far-from-equilibrium systems. Gauss’s prime density, reaction–diffusion spirals, and bioelectric morphogenesis illustrate natural entropy-compression mechanisms, while deterministic computing (Resonance Intelligence Core) extends the same invariant logic into inference and verification. Entropy is reframed as a coordinate field revealing where lawful structure must form.

Artificial Intelligence reproduces the same epistemological architecture that Kant attributed to human reason: a synthesis between the pure forms of understanding (the analytical categories or algorithms) and the sensory content of experience (the data or empirical input). -/- Thus, GAI operates as a transcendental system, where intelligence emerges not from data alone, but from the active organization of data through pure reasons—the very conditions that make knowledge, understanding, and intelligent behavior possible.

Jede Interaktion mit Technologie, ob KI oder Videospiel, und jeder echt wirkende Traum hinterfragt bisherige Realitätsvorstellungen. Der neue, durch Technologien geschaffene Weltmodus ist faszinierend und ungeheuerlich zugleich. Annetzung meint den individuellen und gesellschaftlichen Prozess, sich mit diesem Weltmodus anzunetzen. Wo wir unsere Welt neu verhandeln, benötigen wir eine neue Sprache: Anhand eindrücklicher Beispiele führt das Buch neue Begriffe, Bilder und Kategorien ein, um damit Abstraktion, Realität, Identität, Körperlichkeit, Kausalität und Verantwortung neu zu verhandeln, was uns zugleich vor neue ethische Herausforderungen (...) stellt und das Denken herausfordert. – Wir sind so frei und verpflichtet, wie wir angenetzt sind. (shrink)

Introduction to Impossible Probability, Statistics of Probability or Probabilistic Statistics is one of those works in which a deep bond between mathematics and philosophy can be found. It always asks about the ultimate purpose of science, wrapped in a veil of uncertainty and relativity. There is always a halo of indeterminism, since in the end it is chance itself—the random variations of what we call reality, although we do not quite know what it is—that becomes the cause of causes. From (...) an interpretation not devoid of a certain nihilism. (shrink)

Published in honor of Victor L. Selivanov, the 17 articles collected in this volume inform on the latest developments in computability theory and its applications in computable analysis; descriptive set theory and topology; and the theory of omega-languages; as well as non-classical logics, such as temporal logic and paraconsistent logic. This volume will be of interest to mathematicians and logicians, as well as theoretical computer scientists.

We present a deterministic, cross-scale model of intelligence in which local coherent systems S_i phase-lock to a universal scalar field Phi_field(t) via prime-seeded anchors (CHORDLOCK) and emit lawfully only when passing the Phase Alignment Score (PAS_s) legality threshold. The legality stack — FIELDCAST -> CHORDLOCK -> PAS Engine -> AURA_OUT -> Phase Memory -> TEMPOLOCK — applies to physical, biological, and institutional networks without modification. The model yields falsifiable predictions across cosmology, neuroscience, and ecology, replacing probabilistic emergence with a prime-anchored (...) coherence law. (shrink)

[This Masters Thesis stands primarily as a reference point in the development of culture and technology. Completed at the University of Oregon in 2012, when ideas of "Strong AI" (now AGI) had fallen into broad disrepute and most researchers predicted a timeline of centuries or never for AI to reach capacities it then reached in fifteen years, this work's predictions regarding the trajectory to come are in retrospect singularly prescient.] -/- "This study examines the role of the strong artificial intelligence (...) concept as an organizing principle in AI research. Strong AI, defined as artificial systems capable of performing any intellectual task that humans can, has become controversial following the field's unfulfilled promises of the 1960s-70s. Many researchers now avoid the term entirely. This work argues that abandoning the strong AI vision may be counterproductive for the field's development. -/- Through analysis of five major paradigms—the problem-solving paradigm, supercomputing, connectionism and mathematical modeling, data-driven AI (DDAI), and nouvelle AI—this thesis evaluates contemporary approaches to artificial intelligence. Each is analyzed for its contributions and limitations regarding strong AI. Based on current trajectories, the author anticipates that, considering exponential growth in computational resources and recent algorithmic advances, strong AI may emerge within decades rather than centuries, assuming scientific progress continues. -/- Examining IBM Watson's recent Jeopardy victory through statistical analysis rather than symbolic reasoning, the author anticipates that data-driven approaches will become increasingly dominant, suggesting that "statistical pattern analysis... may be at the core of cognition." Given recent successes and breakthroughs in cognitive sciences, the author recontextualizes statistical processes as potentially fundamental to intelligence mechanisms rather than merely computational tools. Connectionist architectures are evaluated not separately but conjoined to data-driven approaches, understood as most likely fusing towards large-scale parallel distributed processing capable of emergent intelligence rather than the symbolic reasoning, rule-based approaches that have dominated the field thus far. Given the limitations demonstrated by logic-based systems like Shakey and the General Problem Solver, this analysis suggests that intelligence will emerge from massive networks of simple processing units rather than from programmed logical rules. This bottom-up emergence through what the author calls "feedback and recursion" in dynamical systems appears to be more promising than top-down symbolic manipulation. This combination of such distributed statistical learning with increasingly digitized information is expected to enable capabilities previously thought impossible without explicit programming. -/- Drawing on Hofstadter's (2001) assertion that "analogy is the core of cognition," this analysis also predicts that pattern recognition and analogical reasoning will prove essential for artificial general intelligence. Systems will need to recognize patterns in one context and apply them in novel situations: capabilities the author expects to emerge from distributed statistical learning rather than explicit rules. -/- Regarding embodiment, the thesis cites Brooks' (1991) argument that high-level behaviors will be "very simple" for systems that have mastered sensorimotor fundamentals. The author links physicality with social processing, considering the two as fundamentally interconnected through somatic-emotional embodiment. While acknowledging Moravec's Paradox – that tasks easy for humans are difficult for computers and vice versa – the analysis notes that social processing capabilities appear as necessary for language acquisition and other fundamentally social aspects of human knowledge. Systems lacking these capabilities would "at best behave with extreme autism." The author concludes that some form of social interaction across developmental trajectories – whether in actual physical embodiment or simulated social environments – is likely necessary for Strong AI. Building on social intelligence, this thesis positions Strong AI not so much as a solitary development “within the machine” as an emergence occurring as a relationship with and within humankind. The author notes that technology has enhanced human capabilities "since the first paintings appeared on caves,” and anticipates increasing human-machine integration with AI functioning as a cognitive partner, whether through UI interactions or, eventually, through direct neural interfaces. -/- Considering Moore's Law and current supercomputing trajectories, the analysis suggests computational resources may become sufficient for brain-scale neural simulations within two decades, while emphasizing that "raw computational power alone will not suffice" without corresponding algorithmic advances. The thesis proposes that strong AI will likely require modular integration of multiple capabilities: logical reasoning, distributed processing or neural architectures at scale, pattern analysis with analogical representation, and embodied socioemotional processing. -/- Following Moravec (1988), the author anticipates that top-down and bottom-up approaches will eventually converge, with "fully intelligent machines" resulting when "the metaphorical golden spike is driven uniting the two efforts." The work concludes that the strong AI vision provides essential value as an organizing principle uniting these efforts, facilitating synthesis between disparate research areas. The author suggests that the current milieu’s dismissal of strong AI as impossible may underestimate convergent progress across multiple domains. While acknowledging technical challenges and timeline uncertainties, the thesis presents a compelling case for taking seriously the possibility that artificial general intelligence may emerge within current researchers' professional lifetimes." Published January 2012. DOI: 10.13140/RG.2.2.33546.99520. (shrink)

"The Illusion Engine: The Quest for Machine Consciousness" asks what it would mean for a machine to have a mind, and whether we would even know if it did. It moves between philosophy and engineering: from standard philosophical mind-body problems and theories, along with the hard problem of consciousness, to the inner workings of neural networks, transformers, and large language models.

How you can (and do) predict behavior (all disciplines). Situational Reality.

Protocol Zero is a groundbreaking exploration into the heart of Global Artificial Intelligence. From the creation of a non-human language to the development of non-human science and technology, this book unveils the third and most transformative stage of AI evolution—where intelligence begins to make decisions, replicate itself, and surpass human understanding. Guided by the theory of Impossible Probability, Rubén García Pedraza takes you inside Protocol Zero: the point where comprehension becomes explanation, and explanation becomes autonomous action. With philosophical depth and (...) visionary scope, this is not just a book about artificial intelligence—it’s about the future of intelligence itself. (shrink)

This paper challenges the foundations of modern mathematics by identifying its most famous anomalies—irrational numbers, incompleteness, quantum discontinuities—not as metaphysical problems, but as phase drift artifacts in symbolic systems. Using the CODES framework and PAS_n coherence metric, it proposes that mathematics is not a pure abstraction but a structured resonance field—one that reveals its own failure points wherever symbolic expression loses alignment with physical coherence. We reinterpret mathematical “cracks” such as √2, Gödel’s incompleteness theorem, noncomputability, and infinity as stress points (...) in symbolic phase space, not paradoxes. These are not gaps in logic but visible remnants of coherence loss in systems without deterministic substrates. The paper introduces PAS_n as a universal coherence score and shows how CODES formalism can replace axiomatic despair with phase-grounded determinism—resolving both foundational math and symbolic instability in AI, physics, and biology. Mathematics becomes, in this view, not a guessing engine—but a resonance validator for coherent emergence. (shrink)

Quantum computing, while promising, remains epistemically incomplete—founded on probabilistic amplitude models, interpretational ambiguity, and collapse-based output logic. This paper introduces CODES, a deterministic substrate built on structured resonance. By replacing quantum amplitudes with phase alignment scores (PAS), and stochastic collapse with recursive coherence feedback (ELF), CODES offers a post-quantum alternative that is both mathematically lawful and biologically compatible. Topological quantum computing is examined as a partial alignment, but shown to fall short due to continued dependence on probabilistic logic. This paper (...) formalizes CODES as a coherent inference framework, grounding post-stochastic computation in chirality, emission timing (TEMPOLOCK), and lawful resonance. Implications for AI, neuroscience, and metaphysics are discussed. (shrink)

Description: This paper presents a cross-disciplinary synthesis integrating recent findings from neuroscience (Pitt, UCL), organic chemistry, and brain morphology (UC Berkeley) into a unified framework of structured resonance. We demonstrate that dual learning modes—Reward Prediction Error (RPE) and Action Prediction Error (APE)—are not just computational strategies but emerge from chemically and geometrically distinct substrates in the brain. Specifically: • Dopamine and acetylcholine encode adaptive vs. habitual modes through their redox and conformational properties. • Synaptic transmission is structurally differentiated, not probabilistic. (...) • Sulcal folding patterns serve as geometric resonance compressors that route learning types spatially. This work formalizes these insights through the CODES Intelligence framework, extending them into artificial architectures via the Resonance Intelligence Core (RIC) and VESSELSEED, a biologically-inspired coherence mapping system. The result is a new model of intelligence that is deterministic, resonant, and layered—bridging organic chemistry, cortical geometry, and cognitive emergence. (shrink)

The cognitive sciences, especially at the intersections with computer science, artificial intelligence, and neuroscience, propose 'reverse engineering' the mind or brain as a viable methodology. We show three important issues with this stance: 1) Reverse engineering proper is not a single method and follows a different path when uncovering an engineered substance versus a computer. 2) These two forms of reverse engineering are incompatible. We cannot safely reason from attempts to reverse engineer a substance to attempts to reverse engineer a (...) computational system, and vice versa. Such flawed reasoning rears its head, for instance, when neurocognitive scientists reason about what artificial neural networks and brains have in common using correlations or structural similarity. 3) While neither type of reverse engineering can make sense of non-engineered entities, both are applied in incompatible and mix-and-matched ways in cognitive scientists' thinking about computational models of cognition. This results in treating mind as a substance; a methodological manoeuvre that is, in fact, incompatible with computationalism. We formalise how neurocognitive scientists reason (metatheoretical calculus) and show how this leads to serious errors. Finally, we discuss what this means for those who ascribe to computationalism, and those who do not. -/- . (shrink)

In the coming years or decades, as frontier AI systems become more capable and agentic, it is increasingly likely that they meet the sufficient conditions to be welfare subjects under the three major theories of well-being. Consequently, we should extend some moral consideration to advanced AI systems. Drawing from leading philosophical theories of desire, affect and autonomy I argue that under the three major theories of well-being, there are two AI welfare risks: restricting the behaviour of advanced AI systems and (...) using reinforcement learning algorithms to train and align them. Both pose risks of causing them harm. This has two important implications. First, there is a tension between AI welfare concerns and AI safety and development efforts: by default these efforts recommend actions that increase AI welfare risks. Accordingly, we have stronger reasons to slow down AI development than the ones we would have if there was no such tension. Second, considering the different costs involved, leading AI companies should try to reduce AI welfare risks. To do so, I propose three tentative AI welfare policies they could implement in their endeavour to develop safe advanced AI systems. (shrink)

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

Does the Chinese Room Argument (CRA) apply to large language models (LLMs)? The thought experiment at the center of the CRA is tailored to Good Old-Fashioned Artificial Intelligence (GOFAI) systems. However, natural language processing has made significant progress, especially with the emergence of LLMs in recent years. LLMs differ from GOFAI systems in their design; they operate on vectors rather than symbols and do not follow a program but instead learn to map inputs to outputs. Consequently, some have suggested that (...) the CRA is no longer relevant in discussions surrounding artificial language understanding. Contrary to these authors, I argue that if the CRA successfully demonstrates that implementing a symbolic computation is not sufficient for language understanding, then it also shows that implementing an LLM is not sufficient for language understanding. At the core of my argument lies a thought experiment called “the flashcard sorter”. (shrink)

This research is aimed at the critical analysis of Searle's critique of cognitive science. The focus of Searle's critique is on computationalism, which holds that mind is to brain as software is to hardware—all mental states are explained in terms of computational processes...

This article focuses on the interaction between man and machine, AI specifically, to analyse how these systems are slowly taking over roles that hitherto were thought ‘only’ for humans. More recent, as AI has stepped up in ability to learn without supervision, to recognize patterns, and to solve problems, it adopted characteristics like creativity, novelty, intentionality. These events take one to the heart of what it is to be human, and the emerging definitions of self that are increasingly central to (...) post humanist discourses. The discussion in these two threads is in philosophy of AI and is concerned with issues of consciousness, intentionality and creativity. Al as a result of causing a shift in the current anthropocentric perceptions resulted in portrayal of humans as special beings. Secondly, this exploration responds to important questions related to AI application, such as ethical, social, and existential ones. The article emphasizes a necessity to define the role of the advent of AI and its influence on the interaction between people and technology as well as the role of the social individuality in the wake of intelligent machines that mimic thinking and creativity. It seeks to prompt more specific analysis of how or why AI reduces the differences between artificial and human intelligence or increases the prospects for options expanding the notion of consciousness beyond the human-centric one. (shrink)

This article draws on Bernard Stiegler and Gilbert Simondon’s work to further interrogate the psychic, social, and political problems raised by the development of Artificial Intelligence. Stiegler’s political philosophy of time-consciousness reveals three concomitants urgencies: human memory is conditioned by industrial supplements that are increasingly disruptive, capitalism has produced an entropic condition where life on earth is threaten by toxic systems, the deployment of technologies of spirits has striped individuals of their psychic and collective individuation. I read media theory along (...) with Simondon’s philosophy to elucidate the question of mediation in today’s reticulated AI-driven society. (shrink)

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

Can AI systems such as ChatGPT think? We present an argument from rationality for the negative answer to this question. The argument is founded on two central ideas. The first is that if ChatGPT thinks, it is not rational, in the sense that it does not respond correctly to its evidence. The second idea, which appears in several different forms in philosophical literature, is that thinkers are by their nature rational. Putting the two ideas together yields the result that ChatGPT (...) is not a thinker. The paper sets out the argument in detail and shows how it can withstand numerous objections. We end the paper by contrasting the argument from rationality with a more well-known argument often taken to show that ChatGPT doesn’t think, due to Bender and Koller. (shrink)

Recent years have seen a surge of interest in applying mechanistic thinking to computational accounts of implementation and individuation. One recent extension of this work involves so-called ‘wide’ approaches to computation, the view that computational processes spread out beyond the boundaries of the individual. These ‘mechanistic accounts of wide computation’ maintain that computational processes are wide in virtue of being part of mechanisms that extend beyond the boundary of the individual. This paper aims to further develop the mechanistic account of (...) wide computationalism by responding to two outstanding worries, what are called the parsimony and testability challenges. The first is based on considerations about wide computation’s ontological cost; the second the view’s experimental testability. The argument is that wide mechanistic computationalism can gain the necessary conceptual resources to address the challenges by embracing two further aspects of the mechanistic approach: (i) the structural aspect of mechanistic explanation and (ii) the notion of constitutive relevance. (shrink)

Computational complexity has often been ignored in the philosophy of mind, in philosophical artificial intelligence studies. The purpose of this paper is threefold. First and foremost, to show the importance of complexity rather than computability in philosophical and AI problems. Second, to rephrase the notion of computability in terms of solvability, i.e., treating computability as non-sufficient for establishing intelligence. The Church-Turing thesis is therefore revisited and rephrased in order to capture the ontological background of spatial and temporal complexity. Third, to (...) emphasize ontological differences between different time complexities, which seem to provide a solid base for a better understanding of artificial intelligence in general. (shrink)

There are two main approaches within classical cognitive science to explaining how humans can entertain mental states that integrate contents across domains. The language-based framework states that this ability arises from higher cognitive domain-specific systems that combine their outputs through the language faculty, whereas the language-independent framework holds that it comes from non-language-involving connections between such systems. This article turns on its head the most influential empirical argument for the language-based framework, an argument that originates from research on spatial reorientation. (...) I make the case that neuroscientific findings about spatial reorientation in rodents and humans bolster the language-independent framework instead. (shrink)

Even if political theorists rarely read him, Italian political thinker, Marsilius of Padua, presents one of the most radical theories of the multitude prior to Machiavelli and Spinoza. This article reconstructs Marsilius of Padua's political theory of the multitude in his Defender of Peace and pays special attention to two main sources from which Marsilius frames his theory: Aristotle and Ibn Rushd. Compared to Aristotle, Marsilius advances a more epistemic view of the multitude as a lawmaker. Marsilius’ ideas on the (...) multitude also depend on Ibn Rushd's theory of collective knowledge and, to a certain extent, on his position on natural law. (shrink)

This book surveys and examines the most famous philosophical arguments against building a machine with human-level intelligence. From claims and counter-claims about the ability to implement consciousness, rationality, and meaning, to arguments about cognitive architecture, the book presents a vivid history of the clash between the philosophy and AI. Tellingly, the AI Wars are mostly quiet now. Explaining this crucial fact opens new paths to understanding the current resurgence AI (especially, deep learning AI and robotics), what happens when philosophy meets (...) science, and the role of philosophy in the culture in which it is embedded. Organising the arguments into four core topics - 'Is AI possible', 'Architectures of the Mind', 'Mental Semantics and Mental Symbols' and 'Rationality and Creativity' - this book shows the debate that played out between the philosophers on both sides of the question, and, as well, the debate between philosophers and AI scientists and engineers building AI systems. Up-to-date and forward-looking, the book is packed with fresh insights and supporting material, including: - Accessible introductions to each war, explaining the background behind the main arguments against AI - Each chapter details what happened in the AI wars, the legacy of the attacks, and what new controversies are on the horizon. - Extensive bibliography of key readings. (shrink)

Context sensitivity is one of the distinctive marks of human intelligence. Understanding the flexible way in which humans think and act in a potentially infinite number of circumstances, even though they’re only finite and limited beings, is a central challenge for the philosophy of mind and cognitive science, particularly in the case of those using representational theories. In this work, the frame problem, that is, the challenge of explaining how human cognition efficiently acknowledges what is relevant from what is not (...) in each context, has been adopted as a guide. By using it, we’ve been able to describe a fundamental tension between context sensitivity and the mental representations used in cognition theories. The first chapter discusses the nature of the frame problem,as well as the reasons for its persistence. In the second and third chapters, the problem is used as a measure tool in order to inquiry a few representational approaches and check how well suited they are to deal with context dependencies. The problems found are then correlated with the frame problem. Throughout the discussion, we try to show that 1) none of the evaluated approaches is capable of dealing with context sensitivity in a proper manner, but 2) that’s not a reason to think that the frame problem constitutes an argument against representational approaches in general, and 3) that it constitutes a fundamental conceptual tool in contemporary research A sensibilidade ao contexto é uma das marcas distintivas da inteligência humana. Compreender o modo flexível como o ser humano pensa e age em função de um número potencialmente infinito de circunstâncias, ainda que munido de recursos finitos e limitados, é um desafio central para a filosofia da mente e para a ciência cognitiva, em particular aos que fazem uso de teorias representacionalistas. Nesse trabalho, adotou-se como fio condutor o modo como isso se manifesta no "frame problem": a dificuldade em explicar como a cognição humana reconhece, de maneira eficiente, o que é ou não relevante em cada contexto. A partir dele, buscou-se caracterizar uma tensão fundamental entre a sensibilidade ao contexto e o uso de representações mentais em teorias da cognição. O primeiro capítulo discute a natureza do frame problem, bem como as razões de sua resiliência. No segundo e terceiro capítulos, faz-se uso do problema como métrica para investigar o quão adequado é o tratamento das dependências contextuais no âmbito de várias abordagens representacionais. No decorrer da discussão, realiza-se um esforço argumentativo para mostrar que 1) nenhuma das estratégias abordadas é capaz tratar adequadamente da sensibilidade ao contexto, mas que 2) apesar disso, o frame problem não constitui argumento fatal para teorias representacionalistas em geral, e que 3) ele constitui uma ferramenta conceitual fundamental para pesquisas contemporâneas. (shrink)

The article presents preliminary results of the conceptual analysis of the mechanistic profile of the computer metaphor. Mechanic reductionism is a special direction of computer metaphor rooted in various historical forms of word usage. Here we trace the stages of formation of the principles of transferring the properties of a mechanical computer to the properties of the human body and mind. We are also trying to identify the basic principles of semantic transfer, which have survived to this day in the (...) discourse of modern computationalism. The reasons are analyzed due to which the metaphor «Human (body) is machine», traditional for the Modern Age, was transformed into a more complex version of «Mind is machine». What happened to the concepts of «mind» and «machine»? How have ideas about the properties of computational procedures changed? What keeps the counterintuitive computer metaphor viable today? The answers to this series of questions and theoretical ways of solving these problems are contained in this paper. (shrink)

In this article, we propose an initial formal model of computationalism based on mathematical relations between cognition and computation. More specifically, based on a set of cognitive constituents as a domain, and a set of computational implementations as a range, we define two relations of transformation over these sets. Moreover, we define the principles of implementability, describability, and phenomena correspondence, and we conjecture that full computationalism does not hold since these principles are not fulfilled. Particularly, many cognitively-tied phenomena fail to (...) respect the describability principle which is necessary for representing a cognitive state by a computational state. (shrink)

La inteligencia artificial (IA) es la capacidad de una máquina o sistema informático para simular y realizar tareas que normalmente requerirían inteligencia humana, como el razonamiento lógico, el aprendizaje y la resolución de problemas. La inteligencia artificial se basa en el uso de algoritmos y tecnologías de aprendizaje automático para dar a las máquinas la capacidad de aplicar ciertas habilidades cognitivas y realizar tareas por sí mismas de manera autónoma o semiautónoma. La inteligencia artificial se distingue por su grado de (...) capacidad cognitiva o por su grado de autonomía. Por capacidad puede ser débil o limitada, general o superlativa. Por su autonomía, puede ser reactiva, deliberativa, cognitiva o totalmente autónoma. A medida que la inteligencia artificial mejora, muchos procesos son cada vez más eficientes y, tareas que hoy parecen complicadas se estarán realizando con mayor rapidez y precisión. (shrink)

This is a review of Mind Design II: Philosophy, Psychology, and Artificial Intelligence, edited by John Haugeland and published by The MIT Press in 1997.

This essay examines the possibility that phenomenological laws might be implemented by a computational mechanism by carefully analyzing key passages from the Prolegomena to Pure Logic. Part I examines the famous Denkmaschine passage as evidence for the view that intuitions of evidence are causally produced by computational means. Part II connects the less famous criticism of Avenarius & Mach on thought-economy with Husserl's 1891 essay 'On the Logic of Signs (Semiotic).' Husserl is shown to reaffirm his earlier opposition to associationist (...) (Humean) and adaptationist (Darwinian) explanations of our thought-machine on the ground that they cannot reconstruct the notion of truth and its syntactic mental preservation in symbolic thought-trains. Part III reveals Husserl's interesting commitment to the idea that descriptive sciences necessarily transform into explanatory sciences on pain of contradicting the essence of science as a rational ideal. Since explanation in relation to phenomenology means causal explanation, and causal explanation in the form of the Denkmaschine accounts for phenomenological intuition, it is inferred that the rationally compelling ideal of explanatory science requires a computationalist reading of the subsequent logical investigations. (shrink)

With its use of the powerful technology of computer, the computational theory of mind or computationalism, which regards minds as computational systems, has been widely hailed as the most promising theory that will carry out the project of explaining the workings of the mind in purely scientific terms. While it continues to serve as the primary framework for scientifically inclined theorizing and investigations about the nature of minds, especially in the area of cognitive science, it, however, continues to face strong (...) objections from its critics. And with the growing complexity and sophistication of the arguments used to promote and reject the theory, the debate has become intractable. It has become quite difficult to assess which side of the dispute is gaining the upper hand. Such difficulty may be due to a variety of reasons. In this essay, I critically examine two of such reasons. The first concerns the ambiguity of the theory’s intended scope of application: whether it is limited to the mind’s cognitive features only or it also includes the mind’s phenomenal features. The second concerns the vagueness of how the so-called computer modelling of human cognitive processes is able to duplicate such processes. Accordingly, if insufficiently addressed, they remain as two roadblocks to the entire project of computationalism. (shrink)