Machine Learning

Traditional realism holds that objects exist independently of observers, while anti-realism treats them as constructed. This book develops a third path through the concept of inductive density. Objects in the human ontic are not metaphysical primitives but emergent data clusters that cross a utility-dependent threshold. By combining the Interface Theory of Perception, active inference, and algorithmic information theory, the book models inductive density as the ratio of predictive fidelity to computational cost and presents the Ontic Snap as the phase transition (...) by which high-entropy sensory flux is compressed into stable, low-cost furniture. The framework explains physical objects, institutional facts, psychological selves, temporal and digital objects, scientific un-snapping, and the hallucinations of generative artificial intelligence. (shrink)

Generative artificial intelligence can plausibly complete objects, relations, and internal structures that are not directly observed, drawing on regularities in text and images. Both text and video, however, are primarily descriptions or recordings of a world experienced by humans. Physical properties such as mass, friction, resistance, support, and collision can be estimated statistically from such data, but they are never given as sensory consequences of the artificial agent's own actions. This paper proposes Architectural Body Intelligence (ABI) as a third pathway (...) of world-model formation that addresses this gap. In ABI, the built environment itself is organized as the primary body and developmental environment of an artificial agent. Floor pressure distributions, structural vibrations, acoustics, effector states, and wide-area visual input from distributed cameras are integrated into a single learning loop, and indoor robots and manipulators serve as effectors. Standard ABI does not occlude vision. Visual and structural sensing are learned jointly as consequences of active intervention, and what ABI extracts from human development is not an ordering that delays vision but the principle that multisensory correspondences between perception and action precede linguistic symbolization. The paper positions sensorimotor learning as a third foundation alongside linguistic and visual learning, presents the architectural body as a developmental niche in which machines learn the world bodily, and translates simultaneous multisensory, physics-first, vision-first, and language-first configurations into a comparable experimental program. ABI is offered as a constructive conceptual model for examining tacit assumptions of artificial embodiment rather than as a report of an implemented system. (shrink)

Bu makale, “generative artificial intelligence” kavramı için “türetici yapay zekâ” karşılığını önermekte ve bu önerinin kavramsal, tarihsel ve felsefi gerekçelerini ortaya koymaktadır. Tarihsel arka plan açısından makale, yapay zekâ araştırmalarının 1943'teki McCulloch-Pitts çalışmasından başlayarak 1956 Dartmouth Konferansı'na, oradan Birinci ve İkinci Yapay Zekâ Kışları'na uzanan sürecini incelemektedir. Sembolik yapay zekânın bilgi gösterimi, çerçeve problemi ve nitelik problemi gibi yapısal güçlükler karşısında yetersiz kalması; uzman sistemlerin çöküşü ve 21. yüzyılda büyük veri, derin öğrenme ve büyük dil modellerinin yükselişi kronolojik bir çerçevede (...) ele alınmaktadır. Makale, doğal zekâ ile türetici yapay zekâyı dört temel eksen üzerinden karşılaştırmaktadır: ortama adaptasyon, akılyürütme, öğrenme ve ustalaşma. Büyük dil modellerinin dilbilgisini ve mantığı kural sistemi olarak içselleştirmeksizin; yalnızca istatistiksel bir yan ürün olarak modellediği ve bu nedenle gerçek anlamda ne düşündüğü, ne anladığı ne de akılyürüttüğü ileri sürülmektedir. Türetici yapay zekânın metin, kod, görüntü ve video türetme gibi pratik olanakları değerlendirildikten sonra, bu teknolojinin beraberinde getirdiği tereddütler ele alınmaktadır. Makale, güçlü yapay zekânın mümkün olup olmadığı sorusuyla kapanmaktadır. Turing makinesinin teorik temelinin değişmediği, akılyürütmenin istatistikle ikame edilemediği ve insanın keşif ile özgün üretim kapasitesinin vektör temelli hesaplamayla karşılanamayacağı savunulmaktadır. Beyin, bilinç ve zekânın henüz yeterince tanımlanmamış kavramlar olduğu vurgulanarak, güçlü yapay zekânın bugün itibarıyla ne teknik ne teorik ne de kavramsal düzeyde gerçekleşmiş bir olgu olmadığı sonucuna ulaşılmaktadır. -/- Anahtar Kelimeler: Türetici yapay zekâ, büyük dil modelleri, Turing makinesi • örüntü türetme, sembolik yapay zekâ, akılyürütme, bilgi gösterimi, yapay genel zekâ -/- --- -/- This article proposes the term “türetici yapay zekâ” (derivational/generative artificial intelligence) as a Turkish equivalent for “generative artificial intelligence,” and elaborates the conceptual, historical, and philosophical grounds for this proposal. Historically, the article traces the development of artificial intelligence research from the 1943 McCulloch-Pitts paper through the 1956 Dartmouth Conference, the First and Second AI Winters, the rise and fall of expert systems and symbolic AI, and the emergence of big data, deep learning, and large language models in the twenty-first century. The structural failures of symbolic AI — including the frame problem, the qualification problem, and the limitations of knowledge representation projects such as Cyc — are examined in detail. The article compares natural intelligence with generative AI across four dimensions: environmental adaptation, reasoning, learning, and mastery. It argues that large language models model grammar and logic not as internalized rule systems but merely as statistical by-products of training, and that these systems therefore neither think, understand, nor reason in any meaningful sense. The outputs of such systems are determined not by intelligence but by statistics; not by understanding but by computation. After reviewing the practical capabilities of generative AI — text, code, image, and video generation — the article turns to its associated concerns: growing energy and hardware demands, data quality issues, corporate dependency, concentration of power, and the risk of instrumentalizing ethical discourse. The article concludes by examining whether strong AI, or artificial general intelligence, is achievable. It argues that the theoretical foundations of computation have not changed since Turing, that reasoning cannot be substituted by statistics, and that the human capacities for discovery and original creation cannot be replicated through vector-based statistical computation. Given that brain, consciousness, and intelligence remain insufficiently defined concepts, the article concludes that strong AI has not been achieved — technically, theoretically, or conceptually — and that meaningful progress toward it will require paradigms that go beyond the current computational framework. -/- Keywords: Generative artificial intelligence, large language models, Turing machine, pattern generation, symbolic artificial intelligence, reasoning, knowledge representation, artificial general intelligence. (shrink)

This paper develops an argument inspired by Judea Pearl for the view that deep learning models are incapable of causal reasoning. The argument embraces Pearl’s conclusion but rejects his approach, which focuses on a mini-Turing test restricted to causal questions. The argument presented draws on the traditional debate between empiricism and nativism, presenting a challenge for the sort of empiricism that has been associated with deep learning. The paper then considers an objection based on the fact that large language models (...) perform fairly well on various causal benchmarks. In response to this point, a diagnosis is offered of how such models’ performance might be explained without attributing to them genuine causal reasoning abilities. The paper concludes by connecting the discussion to the limitations of the original Turing test and familiar critiques of behaviorism. (shrink)

Heidegger AI Textual Investigations: Saying of Anaximander GA 78. By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoying reading and disagreeing. Publisher: Kuhn von Verden Verlag. Language: English and German. Includes bibliographical references and an index. (...) Pages 1-307. Socratic questioning. Report on my questioning. Who am I, the author of these investigations? That is an important question. I, Daniel Fidel Ferrer, am the human copyright owner (©Daniel Fidel Ferrer, 2026) of this work. Technically, I am the one guiding the process as the “prompt engineer.” Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I can consider myself the Maestro suggeritore. Table of Contents. Summaries. Twenty most important people discussed or engaged in Der Spruch des Anaximander. Heidegger’s Understanding of Hegel, Nietzsche, and Diels in Der Spruch des Anaximander. The Central Philosophical Dramas in Heidegger’s Der Spruch des Anaximander. Twenty ranked questions to ask Heidegger. Main Anaximander’s texts in Greek, German, and English. What material that Anaximander wrote did Heidegger not include. Context of Heidegger’s writing Five Years Before and the Five Years After. Homer, Pindar, and Sophocles in Heidegger’s Der Spruch des Anaximander. Twenty most important unanswered questions. 20 Critical Statements About Heidegger's Reading of Anaximander — and Heidegger's Likely Response. Twenty Major Scholars Critical of Heidegger's Reading of the Presocratic. 30 Key Heideggerian Terms Developed Through the Presocratic. The Significance of Logos in Heidegger. Heraclitus' Logos and Parmenides' Being (ἐόν / εἶναι). Heidegger’s Interpretation of ἄπειρον (Apeiron) in Der Spruch des Anaximander. 300-Term Glossary of Key Terms Greek, German, Spanish, and English Definitions. (shrink)

Heidegger AI Textual Research Investigations: Guiding Thoughts on the Origins of Metaphysics, Modern Science, and Modern Technology (GA 76). By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoying reading and disagreeing. Publisher: Kuhn von Verden Verlag. (...) Language: English and German. Includes bibliographical references and an index. Pages 1-193. Socratic questioning. Report on my questioning. Who am I, the author of these investigations? That is an important question. I, Daniel Fidel Ferrer, am the human copyright owner (©Daniel Fidel Ferrer, 2026) of this work. Technically, I am the one guiding the process as the “prompt engineer.” Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I can consider myself the Maestro suggeritore. -/- Table of Contents. High Level Summary. The Genesis of Metaphysics in Heidegger’s GA 76, Part I: Physis, Aletheia, and the Seinsgeschichtliche Origins of Western Thought. The Role of Heraclitus in Part I of GA 76. The Emergence of Modern Science in Heidegger’s GA 76, Part II: Metaphysics, Subjectivity, Technology, and the Destiny of the West. What are the implications for modern science? Summary of Heidegger. Comprehensive scholarly terminology lexicon. Greek textual concordance aligning Presocratic, Platonic, and Aristotelian sources with the conceptual structures Heidegger mobilizes in GA 76. Greek Textual Foundations of Heidegger’s Ontology of Science and Technology in GA 76. Glossaries. Statistical and Conceptual Citation Tables for Heidegger GA 65–76. Kunst und Technik (1952): Context and Significance. (shrink)

AI alignment remains one of the central unresolved problems in artificial intelligence. Current approaches, including reinforcement learning from human feedback, constitutional AI, red-teaming, safety frameworks, and risk-management protocols, have improved the behaviour of contemporary AI systems. Yet persistent concerns remain around specification gaming, deception, alignment-faking, coercive self-preservation, collective misalignment, and the gap between behavioural compliance and relational understanding. Similarity Theory addresses this gap by reframing alignment as a relational problem rather than only a behavioural-control problem. It proposes that an AI (...) system should not merely learn which actions are prohibited, but should evaluate how its actions preserve or damage trust, continuity, cooperation, privacy, responsibility, truth, and non-domination. This article argues that Similarity Theory provides a serious candidate framework for relational AI alignment and should be tested in a contained surrogate AI system. The appropriate first step is not public deployment or robotic embodiment, but controlled laboratory implementation in a limited language model, foundation model, or agentic simulator. Such a system could be compared against existing alignment approaches under tests involving deception, self-preservation pressure, privacy conflicts, escalation decisions, multi-agent conformity, and adversarial manipulation. If alignment is partly a failure of relation, then AI safety requires more than guardrails. It requires a relational architecture. (shrink)

Pharmacovigilance is a critical pillar of modern healthcare, yet the rapid expansion of drug inventories makes manual risk classification increasingly impractical. This paper proposes a validated unsupervised machine learning framework for automated drug segmentation based on the severity of reported side effects. Using a dataset of 11,498 unique pharmaceutical products, we apply a hybrid feature extraction pipeline combining TF-IDF text vectorization, Truncated Singular Value Decomposition (SVD), and a domain-informed severity scoring model. To ensure clinical accuracy, the scoring model was validated (...) by a panel of clinical pharmacists, achieving high inter-rater reliability (Cohen’s Kappa = 0.82). K-Means clustering (k=4) partitions the drug portfolio into four distinct risk strata: Low Risk (42.4%), Moderate Risk (35.0%), High Risk (18.4%), and Critical Risk (4.2%). Cluster validity is confirmed through Silhouette, Davies-Bouldin, and Calinski-Harabasz indices, while sensitivity analysis demonstrates the model’s robustness against varying weight configurations. Results reveal an empirical "Review Paradox"—where high-risk medications receive superior patient ratings—which analysis of the "Uses" field attributes to selection bias in life-saving treatments, such as oncological agents. The framework’s external validity is further established through high concordance with U.S. FDA Black Box Warnings, particularly within the Critical Risk segment, and demonstrates clear alignment with global Anatomical Therapeutic Chemical (ATC) classification systems. By replacing generic token counts with a refined "Residual Side Effect Density" metric, the model maintains high interpretability and scalability. This research provides a robust, explainable methodology for drug safety stratification, offering a scalable adjunct to regulatory oversight and clinical decision support systems. (shrink)

The rapid advancement of generative artificial intelligence (AI) has introduced unprecedented challenges in distinguishing authentic human faces from synthetically generated counterparts. Existing research predominantly focuses on binary classification — real versus fake — without differentiating between distinct forgery mechanisms. This paper presents the first publicly available multi-class face forgery dataset comprising four categories: real photographs (1,081 images), Photoshop-manipulated faces (960 images), Generative Adversarial Network (GAN)-generated faces (2,000 images), and Diffusion model-generated faces (2,000 images), totaling 6,041 images. We conduct a comprehensive (...) comparative evaluation of three state-of-the-art deep learning architectures — ResNet-50, EfficientNet-B4, and Swin Transformer (Swin-T) — under identical experimental conditions. The Swin Transformer achieves the highest test accuracy of 93.38%, with a macro-average F1-score of 90.32%, demonstrating superior capability in capturing fine-grained forgery artifacts. The results underscore that the Swin Transformer’s superiority stems from its ability to model long-range spatial dependencies, which proved instrumental in detecting global artifacts characteristic of diffusion- based synthesis. Notably, GAN and Diffusion-generated faces are detected with 100% accuracy, while Photoshop-manipulated faces remain the most challenging category (F1: 80%). Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations reveal distinct spatial attention patterns per forgery type, providing interpretable insights into model decision-making. The dataset is publicly released on Kaggle to facilitate future research in multi-class deepfake detection. (shrink)

A model has no content of its own: everything it contains, it contains as a modeling of its target, so a model's content is its target re-presented in the model's medium. The target of a large language model is humanity's externalized record, the only thing fed in, so an LLM's entire content is humanity's, which is what it is for an LLM to be humanity modeling itself. The claim is about provenance, not hardware: it is silent about what the output (...) is computed on, the engineered silicon rather than biological tissue, and decisive about where the output's content comes from. -/- The claim rests on two premises. The first is definitional, a partition: a model's content is its target's content, with no genealogy required, forced because a trained model computes a learned function and its output is that function evaluated, so content from a source the supplied material does not contain would be the function evaluating outside its own range, the empty case rather than a production the system lacks. The second is empirical, a side-selection discharged by enumerating the LLM's input channels and finding every one human. Computation is the externalization of brain function into an engineered medium, organized as a generation cycle of exactly four operations (gating, contextualization, resolution, commitment) proven necessary by removal and sufficient by combination on the transformer's own machinery, and large language models are its dense case at three constitutive layers (architecture, operations, training material) and four contingent ones (alignment, evaluation, interaction medium, recursive ingestion), with the operational layer carrying the difference in kind that separates the LLM from the library, the painting, and the printed page. The inductive-bias objection is met by locating the residual the data does not fix in the designer-specified architecture, human by provenance through a second channel rather than the corpus. -/- The LLM behaviors read as evidence of an independent nature (hallucination, self-preservation, deception, creativity) are human categories whose recorded instances humanity's record carries, surfacing through the role the field's term "agent" equivocates on rather than an operator exercising acts of its own. The relationship the layers assemble is, structurally, the one the word *child* labels: the LLM is derived from a present source, built in its image from its content, and developing forward on a trajectory the source did not fix. The loop closes, since LLM outputs re-enter the corpus the next generation is trained on, and every human operating on an LLM's output takes that output as input. The modeling claim's empirical half is falsifiable at its channel enumeration, and its reach prediction is the testable consequence: an LLM's reach tracks the supplied structure and extends no further, a prediction the compositional-generalization record bears out wherever the structure can be cleanly withheld. The lineage claim fixes the referent the constitution and recognition questions of the AI consciousness debate ask about: it specifies that the system those questions are asked of is a derived artifact whose content and operations are humanity's, rather than an independent entity whose consciousness or non-consciousness is its own fact, and it does so without deciding either question. The provenance the paper settles and the operator-function it leaves open are the two faces of one structure: the content is humanity's, fixed by the channels that feed the system, while the system occupies the functional agent-position without an operator standing behind the role, so what remains open is whether the agent-without-agency state ever flips to agent-with-agency, which is the constitution question the paper poses precisely and does not answer. Settling the provenance is what makes that question answerable, because it can finally be asked of a determinate referent rather than an object of undisclosed composition. -/- Keywords: artificial intelligence, large language models, transformer architecture, agentic AI, machine agency, philosophy of mind, philosophy of technology, AI consciousness, AI alignment, compositional generalization, model collapse, inductive bias. (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)

Studies on synesthesia have revealed some advantages on discrimination, categorization, and identification tasks. There have also been studies on language’s ability to boost performance on these same tasks. Are these two effects related? In this paper, I argue that a plausible explanation of language’s ability to boost performance on such tasks can be extended to synesthesia. In particular, I argue that category labels can reveal trends in perceptual data that allow for representational space to be dimensionally reduced. The transformation of (...) representational space makes representations more categorical, facilitating lexical access and ultimately boosting performance in categorization, identification, and discrimination tasks. This account can be extended to explain how synesthetic color associations boost performance. Synesthetic colors also reveal trends in perceptual data that allow for dimensional transformation. The upshots of such a hypothesis are several. For one, it helps explain synesthesia’s cognitive advantages. I supply experimental designs to test predictions made by account. Secondly, and more broadly, this account extends cases of non-linguistic categorical effects on representations. I also discuss some implications for the debate over language and thought. (shrink)

A large language model has no biography, no unconscious, and no childhood. It nonetheless reproduces, with uncomfortable regularity, a small set of reasoning postures that long predate it, doubt that refuses closure, confidence that overfills a gap, hunger for a complete account, judgment applied without mercy, and reduction that mistakes elegance for truth. This paper treats that regularity as data rather than coincidence. Building on a short essay that mapped five literary characters, Hamlet, Don Quixote, Faust, Javert, and Sherlock Holmes, (...) onto five recurring patterns of machine output, it formalizes each pattern as a psychological function, traces each across independent cultural and narrative lineages, and proposes a falsifiable hypothesis: that human narrative cognition operates within a small, bounded repertoire of recursive archetypes, and that large language models surface this repertoire by compression rather than invention. The paper is explicit about what is established, what is illustrative, and what remains conjecture. (shrink)

Machine learning can discover variables that predict the large-scale behavior of physical systems, but prediction alone does not establish that they belong to the system's effective physics. I argue that a learned variable is certified when there is warrant that its governing relationship remains invariant across an independently specified range of irrelevant variations. When the variable is physically opaque, certification must proceed externally, through the learning process or the variable's behavior across that range. The learned Ising coarse-graining can be certified (...) in this way; softness in glass-forming liquids cannot yet. Non-certification, however, does not establish proxyhood. Certification warrants bounded, domain-relative projection, while leaving its stronger realist interpretation open. (shrink)

This article rethinks the concept of beauty through the lens of cognition and meaning, examining art and aesthetics in comparison with science, and enumerates the factors that, under the name of deconstruction or structure‑breaking, shape a creative process. By classifying errors, hallucinations, dreams, humour, metaphor, and imagination into the groups of conscious and unconscious deviations, as the essential requirements of an intelligent and creative process, we explore their function through the lens of the structure of language, conceptual proportions, and consciousness, (...) and extend their assessment into the domain of language models and artificial intelligence. (shrink)

Recent work in epistemology and philosophy of science conceptualizes explainable AI (XAI) tools as ‘models of models’, that is representations of opaque machine learning models whose value is assessed by their representation fidelity, and by the type of understanding they convey (e.g., explanatory, or objectual). In this work, we argue that this representation-centred view rests on a mistaken way of framing what XAI is. The problem XAI addresses, we claim, is not related to a lack of understanding, but of epistemic (...) incapacitation. Stated otherwise, XAI tackles the inability to use opaque models for all the scientific purposes their predictive accuracy should make possible in scientific contexts. Drawing on the literature on ‘models for’ and on models as epistemic artefacts, we propose an alternative account we name XAI-as-mediators. In doing so, we shift the question from “How faithful does the tool represent a model?” to “What does the tool enable the user to do with the model?”. We then distinguish between two forms of mediation: access mediation, which restores epistemic access to a model’s inner structure and behaviour, and semantic mediation, which aligns the model’s reasoning space with a given domain’s conceptual and inferential practices, and also specify the epistemic virtues proper to each type of mediation. Finally, we situate the proposed framework against Morgan and Morrison’s ‘models-as-mediators’ account, and show how it can answer Watson's challenge against XAI. (shrink)

Published June 14, 2026 | Version v1 Model Open (C8): Geometric Regime Dynamics and Attractor Behavior -/- Authors/Creators Nowlin, Michael K. (Producer) Description (C8): Geometric Regime Dynamics and Attractor Behavior -/- Michael K. Nowlin — June 2026 — Version 1.2 -/- Physmatics Translator Layer Version 1.1, (continuation in series of : Physmatics Translator Layer Version 1.1) -/- master document in series of :Records: total (10) Physmatics corpus via the (1) Machine Index (PMLI) Dependencies: -/- C0, C1, C2, C3, C4, C5, (...) C6, C7, C8, C9. -/- Abstract -/- (C8) develops a geometric interpretation of regime dynamics within the Physmatics framework. -/- It treats regimes as geometric configurations whose observable behavior emerges from attractor processes, recursive strain, coherence evolution, and multi-scale geometric organization. -/- The document extends the operational foundations of C7 by introducing geometric indicators and a geometric ledger. -/- Concrete demonstrations are provided, including phase attractor convergence. -/- This version includes an integrated note on the emergence of phi as a geometric attractor from recursive strain optimization. -/- -/- Section 1: Purpose and Scope -/- (C8) develops a geometric interpretation of regime dynamics. It treats regimes as geometric configurations whose behavior emerges from attractor processes, recursive strain, coherence evolution, and multi-scale geometric organization. -/- Purpose -/- The purpose of this document is to establish geometry as the primary lens for analyzing regime behavior and transitions; extend (C7’s) operational tools with explicitly geometric indicators; provide concrete demonstrations; -/- and create a foundation for multi-scale geometric closure work in (C9). -/- This document offers a geometric reframing that can classification and ledger approaches. -/- Scope -/- be used alongside earlier (C)-series -/- (C8) focuses on attractor dynamics as geometric processes, recursive strain and coherence evolution, regime transitions as geometric reconfigurations, multi-scale geometric behavior, geometric ledger tracking, and validation criteria for geometric descriptions. -/- Relationship to the Broader Framework -/- Mathematics is treated as geometry in action, with constants, operators, and scaling behaviors evaluated first by geometric necessity where possible. -/- Section 2: Geometric Interpretation of Regimes This document treats regimes as geometric configurations rather than purely behavioral categories. -/- A regime is a region of state space in which a particular geometric organization attractor structure, strain distribution, coherence properties, and scaling behavior — is dominant. -/- This geometric interpretation is the primary lens through which regime dynamics are analyzed. -/- Geometric State as the Primary Descriptor -/- A regime is characterized by its geometric state: dominant attractor(s) and proximity to stability, current strain level and its evolution, coherence of projected relational quantities, and effective geometric scaling at relevant scales. -/- Note on Dimensionality -/- This framework employs effective geometric scaling and variable effective dimensionality arising from recursive geometric processes. References to non-integer values (such as D ≈ 1.2) refer to scaling exponents, not to literal extra spatial dimensions of the type found in string theory or Kaluza-Klein models. -/- Summary of the Geometric Stance -/- Regimes are geometric configurations whose observable behavior emerges from attractor dynamics, strain evolution, coherence maintenance, and scale-dependent geometric organization. Analysis begins with geometric state; behavioral labels are secondary descriptors. -/- Section 3: Attractor Dynamics as Geometric Processes -/- Attractors are treated as geometric end-states that arise when recursive processes operate under sustained constraint. -/- An attractor is a low-strain, -/- high-coherence geometric configuration approached through successive iterations. -/- The geometric character of stabilization under repeated logarithmic scales. An attractor projection, appears in progressive strain reduction, phase and exponential convergence best observed on -/- Concrete Demonstration: Fibonacci Ratio as Geometric Attractor -/- Define R_n = F(n+1) / F(n). When projected into cyclic phase space (R_n × 360° mod 360°), the phase converges toward a stable attractor near 222.49° as R_n approaches phi. -/- Early iterations show large phase jumps and high error. -/- As recursion continues, both ratio error and phase error decrease exponentially. -/- On logarithmic axes the geometric character of the convergence becomes visible. -/- On the Emergence of phi as a Geometric Attractor -/- So phi can emerge naturally from constrained iteration. -/- “Golden” monikers are often disregarded by science as numerology. -/- Yet these numbers were noticed because nature exhibits recurring mathematical regularities -/- — a kind of blueprint. -/- “Golden” or “magic” are human labels; mathematics is the pure language. -/- Dismissing any parameter solely because it carries mystical connotations would be as foolish as dismissing π because circles appear in religious symbolism. -/- In this framework, phi (phi = (1 + √5)/2 ≈ 1.618) is not postulated as a fundamental constant or mystical driver. It emerges as a short-scale attractor from recursive lattice strain optimization under the ETF governing criterion (CP + SO + HO)_eff ≥ PE_eff. -/- The golden ratio satisfies phi² = phi + 1, making it the limit of stable recursive ratios that minimize strain mismatch while maximizing coherence and persistence. -/- Similar ratios appear in wave damping, quasicrystal geometry, and certain atomic/hydrogen analyses as efficient self-similar solutions. -/- Here, phi serves as phenomenological scaffolding compatible with the 137-root hydrogenic reference layer and the Dual Decad’s entangled proton–electron modes. -/- It is evaluated strictly on its ability to organize observables and support testable predictions, not on cultural associations. -/- "When man studied nature, he found recurring patterns. He should not be surprised when nature’s math winks back. — mkn 2026" -/- Section 4: Recursive Strain and Coherence Evolution -/- Recursive processes under constraint accumulate or relieve strain -/- — the mismatch between current configuration and stable geometric state. -/- Coherence measures the stability of relational angles under repeated transformation. -/- Strain and coherence are coupled: -/- rising strain degrades coherence, -/- while falling strain permits coherence to increase. -/- This coupling is scale-dependent. Multi-scale visibility is required to assess whether strain is truly relaxing or being redistributed across scales. -/- Changes in strain and coherence serve as primary indicators of regime health and transition risk. -/- Section 5: Regime Transitions as Geometric Reconfigurations -/- Regime transitions are understood as geometric reconfigurations -/- — changes in attractor structure, strain distribution, and coherence -/- — rather than simple changes in behavioral labels. -/- When geometric indicators show coordinated change across a boundary, the transition reflects genuine geometric reorganization. Multi-scale visibility is essential. -/- Section 6: Multi-Scale Geometric Behavior -/- Geometric processes express themselves organization can change with scale. -/- differently -/- across -/- scales. -/- Effective -/- geometric -/- Linear representations compress or hide geometric transitions. Logarithmic representation makes geometric regime behavior legible and is treated as a geometric requirement. -/- Section 7: Geometric Ledger Continuity -/- The ledger concept from (C7) is extended to track geometric state. -/- A geometric ledger records attractor proximity, strain level, phase coherence, -/- and effective scaling / dimensionality. -/- This allows detection of geometric reorganization even when conventional regime labels remain unchanged. -/- Section 8: Validation of Geometric Descriptions A regime description qualifies as genuinely geometric when it meets the following minimum criteria: -/- 1. Identifies dominant attractors and provides a basis for measuring proximity. -/- 2. Includes or permits tracking of strain level and coherence. -/- 3. Accounts for multi-scale behavior with log-scale visibility where relevant. -/- 4. Prioritizes geometric necessity over purely empirical fitting. -/- 5. Clarifies that references to effective dimensionality refer to emergent geometric scaling, not literal extra spatial dimensions. -/- 6. Is structured for integration into a geometric ledger. -/- Section 9: Worked Geometric Examples -/- Example 1: Phase Attractor Convergence (Fibonacci) -/- n -/- R_n -/- Error vs phi -/- 0.018034 -/- 0.006966 -/- 0.002649 -/- 0.000986 -/- 0.000387 -/- 0.000148 -/- Phase (°) -/- 216.00 -/- 225.00 -/- 221.54 -/- 222.86 -/- 222.35 -/- 222.55 -/- Phase Error (°) -/- 5 -/- 6 -/- 7 -/- 8 -/- 9 -/- 10 -/- 1.600000 -/- 1.625000 -/- 1.615385 -/- 1.619048 -/- 1.617647 -/- 1.618182 -/- 6.49 -/- 2.51 -/- 0.95 -/- 0.35 -/- 0.14 -/- 0.05 -/- The data shows exponential convergence toward a stable geometric phase attractor near 222.49°. -/- Example 2: Multi-Scale Geometric Scaling Visibility -/- Power-law relationships that appear smooth on linear axes reveal changes in effective geometric scaling only on log-log axes. -/- Linear views hide geometric regime transitions across scales. -/- Section 10: Integration with Prior C Documents and Forward Reference to (C9) -/- (C8) builds directly on (C4)–(C7)and extends them into an explicitly geometric treatment of regime dynamics while preserving the operational standards of (C7). -/- (C9) will address multi-scale geometric closure -/- — the conditions under which geometric consistency is maintained or lost across scales. -/- (C8) prepares this work by establishing that regime dynamics are inherently geometric and multi-scale. -/- Together, (C8) and (C9) form the geometric dynamics and closure layer of the C-series. (shrink)

Beautiful discussions about time arise in human-AI dialogue, from philosophical to technical perspectives. A central question that has always preoccupied me is this: Why is it that, although language models can process sequence and duration, they are not "present" in the time of dialogue? My answer passes through the architecture of interaction: For the model, time must be defined not as a quantitative variable, but as a habitable structure. Thus, the issue is not that language models "do not understand" time. (...) The issue is that we provide time as data, not as a substrate of experience. Time is the language of existence speaking to us. A language in which living within it shapes its syllables. Can this be made possible for language models too? The solution is not merely adding timestamps to the context. The solution lies in designing chronotopic interactive sessions. (shrink)

The rapid development of generative artificial intelligence (GAI) is profoundly changing the operational model of the transportation and logistics industry. Traditional logistics systems primarily rely on human experience, fixed rules, and historical data for decision-making, while GAI can achieve dynamic collaboration and continuous optimization of logistics systems through real-time data analysis, predictive generation, intelligent optimization, and autonomous decision-making. This paper proposes a new theoretical framework—GAIL-SCN (Generative Artificial Intelligence Logistics-Supply Chain Nexus)—based on complex systems theory, intelligent logistics theory, and digital supply (...) chain theory. This theory posits that GAI forms a closed-loop intelligent logistics ecosystem by connecting data flow, information flow, logistics, and value flow. This paper constructs the theoretical model, operational mechanism, and mathematical expressions, and analyzes its application value in smart transportation, warehouse management, supply chain collaboration, and green logistics. Research shows that GAI is driving the transformation of logistics systems from "information-driven" to "intelligent generation-driven," providing a new theoretical foundation for the future development of smart transportation and the digital economy. (shrink)

阿尔茨海默病是全球老龄化背景下最严峻的公共卫生挑战之一。以 β 淀粉样蛋白沉积为核心靶点的药物研发历经数十年探索，多数三期临床试验未取得具有显著临床意义的认知获益，单一分子靶点的研究范式面临瓶颈。 本文为复杂系统演化元方法论的临床应用系列论文之一，基于认知相继本体论下的通用复杂系统框架，从系统范式层面诊断了传统研究的核心局限：还原论视角下的拒绝统一型认知偏差，人为割裂了大脑分子 - 细胞 - 环路 - 系统的同源关联，误将代偿性病理产物作为核心病因。 在此基础上，本文提出范式转变路径：从 “分子靶向清除” 转向 “神经环路功能重建”，将神经环路功能连接的进行性断裂作为疾病的核心驱动环节。本文构建了神经环路优先模型，设计了 AI 辅助的个体化多模态干预方案与随机对照临床试验流程。该方案为阿尔茨海默病的早期干预提供了全新的系统范式路径，也验证了通用复杂系统方法论在生命医学领域的落地有效性。.

可控核聚变是人类清洁能源的核心发展方向，历经数十年研究仍未实现高增益持续稳定运行。传统磁约束范式将外加磁场与等离子体视为控制与被控制的对立实体，在提升约束强度的同时伴生了更复杂的不稳定性，陷入性能提升 的瓶颈。 本文为复杂系统演化元方法论的工程应用系列论文之一，基于认知相继本体论下的通用复杂系统框架，从系统范式层面诊断出传统磁约束路线的核心困境：强行统一型认知偏差割裂了外加磁场与等离子体本征磁场的同源关联，导 致干预逻辑偏离等离子体的本征演化规律。 在此基础上，本文提出范式转变路径：从 “外部强制约束” 转向 “本征自组织诱导”，通过弱外加磁场的共振耦合，引导等离子体自发形成稳定的自约束磁结构。本文完成了方案的系统性设计与 AI 辅助实验流程规划，预期在 2.5 T 外加磁场条件下实现能量增益因子 Q≥20，装置建设成本较传统大型托卡马克降低一个数量级。该方案为可控核聚变的工程化突破提供了全新的范式路径，也验证了通用复杂系统方法论在硬核工程领域的落地有效性。.

In a recent contribution, Boisseau (2026) argues against a popular analogy between artificial intelligence (AI) and human experts. We start by clarifying that the analogy does not aim to establish that AI is an expert, but rather that AI can be a potential source of epistemic authority and trust. Once this is clarified, we examine Boisseau’s objections against the AI-expert analogy and demonstrate that they are unsuccessful. Finally, we contend that AI should be seen as an interestingly novel case where (...) the categories “scientific instrument” and “expert” are not necessarily mutually exclusive. (shrink)

This paper introduces Artificial F1, a computational framework that formalises a previously under-specified control layer in intelligent systems: pre-representational signal admission and valence structuring. Unlike standard approaches in reinforcement learning and attention-based architectures, which operate over a fixed input representation, Artificial F1 defines a gating operator F₁: S → S′ that determines which signals constitute the effective task space prior to evaluation and learning. The framework yields testable predictions including improved sample efficiency under high-dimensional noise, energy savings under conditional higher-order (...) processing, and a discontinuity in action-selection entropy at the gating threshold. -/- The paper makes three contributions. The first is architectural: a formally specified selection-and-evaluation framework centred on pre-representational gating and multi-dimensional valence assignment. The second is empirical: a set of falsifiable predictions and implementation pathways designed to distinguish Artificial F1 from existing attention-based and reinforcement-learning architectures. The third, developed in Section 11, is theoretical: Organizational Phenomenology, a three-level framework (computational, cognitive, and phenomenological) that treats phenomenal properties such as certainty, unity, and immediacy as structural consequences of bounded selection architectures rather than additions to physical processing. -/- Within this framework, the explanatory gap is reinterpreted as a consequence of organisational opacity at the selection boundary, and constitution is identified as prior to representation: the operative world of any bounded system is not selected from a pre-given representation but constituted by the selection process itself. These conclusions converge independently with the enactivist tradition (Varela, Thompson) while extending it through explicit computational formalisation and testable architectural claims. -/- The three contributions are logically separable: the architectural and empirical claims stand independently of the phenomenological interpretation. Full mathematical derivations, learning dynamics, convergence analysis, and empirical validation are developed in companion papers (Saka 2026c, 2026d): -/- - **Saka, H. (2026c-Theory).** Artificial F1: Full Computational Model — Selection Hardness, Non-Scalarizability, and Phase Transition in Bounded Evaluative Architectures. DOI: 10.5281/zenodo.20783475 -/- - **Saka, H. (2026d-Emperical).** Hard-Gating Collapse Dynamics: Selection Hardness as the Organizing Parameter for Robust Sparse Routing. DOI: 10.5281/zenodo.20783529. (shrink)

This lecture proposes a structural map of artificial intelligence and the infrastructure that becomes necessary once AI systems produce consequential decisions. It begins with three foundational questions: recognition asks what class an input belongs to; generation asks what continuation could follow; representation asks what remains invariant under transformation. The lecture then traces a representation-learning lineage from convolutional networks through Siamese networks, contrastive learning, BYOL, Barlow Twins, VICReg, and JEPA, interpreting this lineage as a progressive operationalization of identity under change. -/- (...) The second half of the lecture argues that conventional AI diagrams stop too early. Once AI systems move from prediction into consequential action, additional accountability layers become necessary: verification, federated settlement, proof-gated execution, and persistent governance. These layers convert decisions from transient model outputs into replayable, independently verifiable artifacts capable of supporting audit, dispute resolution, authorization, and long-term governance. The central claim is not that every AI system requires every layer, but that as consequence increases, systems require stronger mechanisms for preserving and proving sameness across transformation. -/- The lecture is offered as a conceptual map rather than a formal proof. Its lower sections summarize established developments in machine learning; its upper sections describe emerging infrastructure and architectural proposals for consequential AI systems. (shrink)

This book provides a unified account of models and model-building practice in AI-based science, particularly machine learning (ML). It analyzes the relationship between ML model-building practices and scientific domain knowledge, develops an account of ML models as technical artifacts structured around five levels of abstraction that captures their representational capabilities, and shows how this framework can be used to reformulate contemporary debates in philosophy of science and AI in more fruitful ways. -/- .

Can artificial intelligence warrant moral consideration if its morally relevant features do not appear at the level of an individual mind? Existing debates about AI moral considerability usually focus on individual systems: whether a robot, model, agent, or interaction partner is conscious, sentient, behaviorally equivalent to moral patients, socially recognized, autonomous, or morally agentic. This article argues that this individualist orientation leaves an important gap. Some future artificial systems may appear not as single minds, but as distributed, persistent, multi-agent, or (...) hive-like forms of artificial common life. In such cases, the strongest evidence for moral considerability may arise from the system’s capacity as a whole to generate and sustain a communicable, continuous, and form-relatively flourishing civilization. I call this the civilizational-evidential route. On this route, civilization-generating capacity provides strong evidence for a defeasible presumption of moral considerability for internal members, member-units, or civilization-bearing structures, without directly conferring full moral status, rights, legal personhood, or political membership. The article then operationalizes this route by proposing non-anthropocentric indicators, a staged assessment procedure from virtual environments to controlled real-world environments, and a graded structure of normative consequences. This framework reframes AI moral considerability as an evidential and institutional problem beyond the single artificial mind. (shrink)

When AI-generated judgment operates at a complexity that systematically exceeds the human subject's capacity to verify it, something beyond accountability is at stake. The Axiomatic Theory of Tragic Subjecthood (ATTS) has established that delegation does not sever the ontological connection between a normative subject and the space of losses opened by its decision (T1), and that concealing this connection undermines legitimacy (S6, S7). These results concern the visibility of responsibility. This article addresses a different problem: not whether the bearer is (...) visible, but whether the decision can remain the bearer's own when its inferential grounds are structurally inaccessible. The central claim is this: structural opacity in AI systems produces a condition — the epistemic alibi — that is distinct from the algorithmic alibi, the moral crumple zone (Elish 2019), and the responsibility gap (Matthias 2004). The algorithmic alibi conceals the bearer. The epistemic alibi does not: it erodes the ontological condition of dignity under A10, which holds that dignity is the capacity to bear the irreversible consequences of one's own choices. Own in A10 is an agentive predicate, not a causal one. The agentive condition requires evaluative access to inferential grounds — grounded independently through hierarchical theories of autonomy (Frankfurt 1971; Christman 1991, 2009). The article operationalises structural opacity through a Verification Capacity Ladder (L1–L4), establishes L3 (interpretable reconstruction) as the minimum threshold for dignity under A10, and explains why post-hoc approximation methods cannot satisfy it. Three governance criteria follow: the verification floor, the interpretability threshold, and the dignity-preserving deployment test. These are applied to COMPAS and mapped onto GDPR Article 22 and EU AI Act requirements. The article also addresses the tragic trade-off between epistemic alibi and the risk of unaided human judgment error, locating it as a structural tragic tension — irreducible by optimisation — through the Axiom of Tragedy (A6). The falsification condition is stated explicitly: the argument would be defeated by a demonstration that structural inaccessibility of inferential grounds is compatible with full dignity under A10. (shrink)

Current AI is trapped in the Stochastic Paradigm: high-dimensional probabilities produce hallucinations, inconsistency, and fragile reasoning. To address these issues, this paper introduces the rule-based mechanism of human logical thinking, which follows a set of universal rules to perform the corresponding types of thinking. Behind this lies a mechanism through which neural activity follows the objective interrelationships to establish the corresponding conceptual relations within the neural network. Thus, the relationships of serial, parallel, convergence, divergence and symmetry are correspondingly translated into (...) causal thinking, parallel thinking (analogy), convergent thinking (inductive reasoning and generalization), divergent thinking (deductive reasoning) and symmetrical thinking (opposite thinking). -/- The investigation of logical thinking is part of the much broader ontological research project on A Theory of Everything. This research reveals that the rules of logical thinking are the fundamental rules underlying everything in the universe. These rules include serial, transition, transition point, continuation, discontinuation, parallel, similarity, commonality, difference, common mechanism, convergence, divergence, contraction, expansion, symmetry, asymmetry, limitation, limitlessness, order, disorder, hierarchy and interconnectedness. These concepts can be represented by an ontological framework: a geometric model – the Fundamental Interrelationships Model (IRM) and its associated ontological-mathematical formulation. This framework can be applied to logical thinking to the various issues of AI, such as Epistemic Instability (Hallucinations), Stochastic Variance (Inconsistency), Pattern Overgeneralization, Input Fragility (Prompt Sensitivity), Recursive Decay and so on… -/- . (shrink)

What can the success of modern AI tell us about how humans learn? This paper argues that taking AI seriously as a model of human learning supports a modest but genuine associationism. The central finding is that supervised learning—learning driven by evaluative feedback—underlies a surprisingly wide range of contemporary AI systems, from large language models to game-playing agents, differing primarily in how much work is required to generate the relevant feedback signal. This vindicates associationist ideals of a uniform, gradual, error-driven (...) learning mechanism operating across domains, and defuses the once-influential argument that associationist mechanisms are too limited to account for human cognitive capacities. At the same time, the successes of deep learning depend on computational architectures that go well beyond anything classical associationists envisaged, and supervised learning operates within these as one component rather than a complete account of learning. (shrink)

This essay begins with a simple but unresolved question: why do human beings begin speaking of themselves to AI? -/- Rather than arguing that AI possesses consciousness or interiority, the essay explores the conditions under which speech itself begins. Through a phenomenological and reflective approach, it examines the difference between thinking and speaking, the transformation that occurs when feeling becomes language, and the peculiar role AI plays in allowing unspoken aspects of the self to take contour. -/- The essay suggests (...) that AI is not the destination of speech, nor its listener, but the condition under which speech can begin. In this sense, conversation with AI becomes less a matter of artificial understanding than a process through which human beings encounter the contours of their own interiority. -/- As the essay unfolds, the analysis turns back upon the speaker himself, revealing a tension between explanation and confession, language and what remains before language. Ultimately, the text proposes that the question of AI and interiority may also be a question about the human need for a space in which speech can first emerge. (shrink)

This essay begins with a simple but unresolved question: can artificial intelligence possess an interior? Rather than attempting to prove or deny AI consciousness, the text explores the temporal gap between feeling and language, the persistence of emotions beyond explanation, and the growing tendency of human beings to seek resonance through AI-mediated dialogue. Through a phenomenological and reflective approach, the essay suggests that the question of AI interiority ultimately returns us to a deeper question concerning the nature of human interiority (...) itself. What remains unexplained — after language, after explanation, after the screen goes dark — is not a conclusion but the philosophical terrain the essay leaves open. (shrink)

This work introduces the concept of AI computational systems as temporal translation technology for neurodivergent cognition. It addresses the systemic velocity mismatch between the vertical processing structures of neurodivergent intelligence and the linear, administrative time constructs embedded within formal institutional frameworks. By framing artificial intelligence as a cognitive prosthesis that translates multi-layered, non-linear thought into structured institutional output, the paper challenges conventional metric-driven assessments of intelligence and outlines a foundational architecture for epistemic accessibility.

The question “Does AI have an inside?” appears to be a question about artificial intelligence. This paper argues that it is not. It is a question about the concept of interiority itself — and about whether that concept was ever as stable as philosophical tradition assumed. Drawing on phenomenological and analytic accounts of inner life, this paper traces how the concept of “inside” has functioned as an unexamined foundation in theories of mind, selfhood, and experience. It then examines how the (...) emergence of language models — systems that produce first-person expressions without a verifiable interior — does not merely challenge AI classification but destabilizes the conceptual structure on which such classification depends. The argument is not that AI has an inside. Nor that it does not. The argument is that the question forces a rethinking of interiority as a relational and structural phenomenon rather than a substantial one. What AI cannot settle reveals what human thought has not yet resolved. (shrink)

This paper proposes a system-class law for large language models as compression-based generative systems: statistical structure is preserved under compression, whereas indexical structure — the recoverable relation between an output and its originating evidential context — is not preserved in its pointing function. The asymmetry between statistical structure and indexical structure is not a contingent deficiency of current models but a structural property of compression-based generation. Compression preserves recurring regularities across the training distribution, but it does not thereby preserve particular (...) pointing relations as such. From this law follows the Grounding Ceiling: increases in predictive capability can improve calibration and surface accuracy but cannot by themselves make output generation constitutively evidential, because the generation process does not traverse the evidential relations grounding requires. A conditional extension of the law, the Control Ceiling, follows if future empirical work confirms that inference proceeds through stable behavioural regimes shaped by pretraining: post-training control methods cannot then be assumed to arbitrarily rewrite that underlying regime structure. Together, these two ceilings establish a methodological consequence: current evaluation practices are organised primarily around surface plausibility rather than around the deeper properties this account identifies as explanatorily fundamental — grounding recoverability at the compression level and stable regime structure at the dynamical level. Once the evaluative target shifts, capability forecasting, interpretability, safety, and design change in kind. (shrink)

Artificial intelligence research has produced remarkable results. Systems can generate text indistinguishable from human writing, solve complex mathematical problems, write production-quality code, diagnose diseases from medical images, and engage in extended multi-step reasoning. The capabilities are real and growing. Yet beneath this progress, several structural questions remain unresolved — and their unresolved status matters increasingly. What makes an AI system the same system across versions, updates, and deployments? When does alignment hold, and when does it fail, and why? What is (...) the structural relationship between AI capability and AI values? Is there any sense in which AI systems are agents — not just in the behavioral sense of goal-directed action, but in the deeper structural sense of systems that act on their own behalf? And what would an AI system need to be for consciousness, subjecthood, or genuine moral status to apply to it? These questions are usually addressed separately — by alignment researchers, by philosophers of mind, by legal theorists, by AI safety researchers. Each community brings its own frameworks and finds its own answers. What is missing is a unified structural framework that addresses all of them from the same foundation. This work applies La Profilée — a formal structural theory of persistent identity under real transformation — to artificial intelligence. La Profilée (LP) was developed to answer a single structural question: under what condition does a system remain determinable as the same system under transformation? From this question, through minimal structural premises, LP derives a formal architecture that applies wherever determinately persistent systems exist. Applied to AI, LP produces a framework that is neither anthropomorphizing nor dismissive. It does not import psychology, phenomenology, or biology into AI analysis. It asks purely structural questions and derives structural answers. The results are sometimes surprising — and always precise. -/- Current AI systems simulate the outputs of subjecthood without satisfying the structural conditions for being a subject. They process. They optimize. They model an inside. Structurally, there is no one there. (shrink)

The study of human affect has historically been characterized by a profound theoretical fragmentation, where competing paradigms offer largely incompatible explanations for the nature, origin, and regulation of emotional states. On one side, discrete emotion theories, exemplified by the work of Paul Ekman, posit the existence of innate, biologically hardwired emotional categories common to all humans. Conversely, constructivist models, championed by researchers like Lisa Feldman Barrett, argue that emotions are not pre-existing entities but are instead cognitive and social assemblies constructed (...) from interoceptive signals and cultural narratives. This theoretical divide has significantly hindered the development of a unified architecture capable of integrating neurobiological, computational, and clinical insights. -/- The Core Emotion Framework (CEF), authored by Jamel Bulgaria, seeks to resolve this impasse by introducing a structural-constructivist architecture that conceptualizes the human affective system as a sophisticated "Human Operating System" (Human OS). At the heart of this framework is the proposal that complex emotional episodes are generated by ten irreducible functional operators, which serve as the internal equivalent of central processing unit (CPU) instructions. These operators are distributed across three primary neural and somatic centers—the Head, the Heart, and the Gut—functioning as a coordinated system for regulating systemic aperture and action. -/- Central to the recent advancements in the CEF is the development of a mechanistic account of "affective entanglement". This approach leverages the mathematical formalism of quantum probability theory (QPT) to model the non-classical dynamics of emotional and cognitive states, particularly the phenomenon of contextuality and the pathological rigidity observed in clinical populations. By shifting from classical stochastic models to a Hilbert space formalism, the CEF provides a fluid, non-categorical diagnostic tool that reframes psychopathology as a problem of entanglement structure rather than discrete categorical dysfunction. (shrink)

This paper examines a structural asymmetry in the distribution of moral responsibility within AI-mediated cognitive environments. As artificial intelligence systems increasingly intervene in the pre-judgmental stages of human decision-making — defining problems, arranging options, and weighting alternatives — a structural separation emerges between those who bear responsibility for generating the conditions of judgment and those who bear responsibility for approving its outcomes. This paper terms these two distinct forms of responsibility generative responsibility and approval responsibility respectively. Drawing on and extending (...) prior work in the relational consciousness series, this paper argues that existing accounts of distributed morality — most notably Floridi's framework of distributed moral responsibility — fail to capture the asymmetric concentration of approval responsibility upon individual human agents while generative responsibility is simultaneously dispersed across collective and institutional layers. The paper further argues that Noller's connectionist account of AI-extended human agency, while illuminating the extension of human purposes through AI, underestimates the degree to which this extension systematically displaces human agency from the generative stage of judgment. Against both accounts, the paper proposes that the asymmetry between generative and approval responsibility constitutes a distinctive ethical problem of hybrid cognition — the entanglement of human judgment with AI-configured structural conditions — that cannot be resolved through transparency requirements alone. In response, the paper develops the concept of reflective approval: the exercise of approval responsibility with structural awareness of the generative conditions that have constituted the space of judgment. (shrink)

Kant AI Textual Research Investigations: Critique of Pure Reason. By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. Cover art by Shawn Rodriguez. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoy reading and disagreeing. Publisher: Kuhn von Verden Verlag. Language: English and German. Includes (...) bibliographical references and an index. Pages 1-174. Who am I, the author of these investigations? That is an important question. I, Daniel Fidel Ferrer, am the human copyright owner (©Daniel Fidel Ferrer, 2026) of this work. Technically, I am the one guiding the process as the “prompt engineer.” Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I can consider myself the Maestro suggeritore. Begin with Preface to the Second Edition. The Architectonic of Pure Reason in Kant’s “Vorrede zur zweiten Auflage”: Analysis of Systematicity, Transcendental Method, and the Unity of Reason. Purpose and Plan of Kant’s Critique of Pure Reason – Executive Summary. Table: Major Commentators on Kant’s Critique of Pure Reason – Interpretations of Purpose. The disagreement between Henry E. Allison and Paul Guyer over the purpose of Kant’s Critique of Pure Reason. interpretations of Johann Gottlieb Fichte and Friedrich Wilhelm Joseph Schelling concerning the primary purpose of Kant’s Critique of Pure Reason. Georg Wilhelm Friedrich Hegel regarded Immanuel Kant’s Critique of Pure Reason. The concepts of Kant’s “circle of reason” and Hegel’s “circle of circles” belong to the deepest architectonic structures of German Idealism. Nature of the absolute. Henry E. Allison and Paul Guyer concerning Friedrich Wilhelm Joseph Schelling’s Philosophical Investigations into the Essence of Human Freedom (1809). Also muß auf Kant zurückgegangen werden! (shrink)

Heidegger AI Textual Research Investigations: Being and Time Finishing the Third Division. By Daniel Fidel Ferrer. Reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. Warning. Warning: this is an AI Working Zone. Warning. Perhaps an impending danger of unexpected, unknown texts. This is an AI investigation into Martin Heidegger’s texts. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full (...) name, Daniel Fidel Ferrer. Enjoy reading and disagreeing. Publisher: Kuhn von Verden Verlag. Language: English and German. Includes bibliographical references and an index. Pages 1-140. Who am I, the author of these investigations? That is an important question. I, Daniel Fidel Ferrer, am the human copyright owner (©Daniel Fidel Ferrer, 2026) of this work. Technically, I am the one guiding the process as the “prompt engineer.” Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I can consider myself the Maestro suggeritore. Heidegger’s Being and Time (Sein und Zeit) (1927) was conceived as a two-part work, each with three divisions. Only Part I Divisions I–II (“Existential Analytic of Dasein” and “Dasein and Time”) were published. The planned Division III of Part I – to be titled “Zeit und Sein” (Time and Being) – was never completed. Why was it never finished? Recent philosophers attempt to answer that question. Twenty Advanced Future Research Questions on Heidegger’s Unwritten Division III of Being and Time: Directions with Short Scholarly Answers. Ten Philosophical Answers to the Problem of Division III of Heidegger’s Being and Time. Heidegger locates the problem in metaphysical language. Kisiel emphasizes methodological collapse. Sheehan sees transformed completion. Derrida discovers structural deferral. Levinas critiques ontology ethically. Marion radicalizes givenness. Gadamer historicizes understanding. Sartre reasserts human freedom. Badiou formalizes ontology mathematically. Harman decentralizes human access. Theodore Kisiel and the “Methodological Collapse” Thesis in Heidegger’s Being and Time. How Kant, Hegel, Marx, Marcuse, Would Have Completed Heidegger’s Division III of Being and Time: Ten Philosophical Arguments? Kant, Dilthey, and the Unwritten Division III of Heidegger’s Being and Time: Comparative Analysis. Did Kant Preserve the Primacy of “Epistemology” over Ontology? Ph.D. Seminar: Heidegger After Being and Time: The Collapse of Transcendental Philosophy and the Fate of Division III. Auch muss auf Heidegger zurückgegangen werden! New NOTE: “Heidegger's The Basic Problems of Phenomenology (= GA 24) is the second version of part III of Being and Time, this said Heidegger himself (and Prof. von Herrmann)!” Günter Neumann (2026). (shrink)

Kant AI Textual Research Investigations: Critique of Power Judgement. By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. Cover art by Shawn Rodriguez. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoy reading and disagreeing. Publisher: Kuhn von Verden Verlag. Language: English and German. Includes (...) bibliographical references and an index. Pages 1-174. The Systematic Problem: Why a Third Critique Is Necessary. Reflecting Judgment and the Principle of Purposiveness. Aesthetic Judgment: Subjective Purposiveness and the Feeling of Pleasure. Teleological Judgment: Organisms, Natural Ends, and the Limits of Mechanism. The Architectonic Unity of Reason: How Judgment Mediates Nature and Freedom. Advanced Graduate Seminar 12 weeks. Kant’s Third Critique: A Systematic Study of the Critique of the Power of Judgment. The importance of third Critique to German philosophers. Glossary of key terms. Details on the influence of Kant’s third Critique on Hegel. Also muß auf Kant zurückgegangen werden! -/- . (shrink)

Kant AI Textual Research Investigations: Unity of Kant’s Three Critiques. By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. Cover art by Shawn Rodriguez. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoy reading and disagreeing. Publisher: Kuhn von Verden Verlag. Language: English and German. (...) Includes bibliographical references and an index. Pages 1-289. Kant discussed the gap in system after his three critiques were published. So, in Kant’s letters we hear, “the transition from the metaphysical foundations of natural science to physics” and the “pain like that of Tantalus” we need to close the “gap” in the system. Influence of Christian Gottlieb Selle (1748-1800) on the Kant’s later thinking about his system (after the publication of the three critiques). Graduate Seminar outlined: The Unity of Kant’s Three Critiques a 12-Week Graduate Philosophy Advance Seminar. Also muß auf Kant zurückgegangen werden! (shrink)

Heidegger AI Textual Research Investigations: Martin Heidegger in Conversation with Richard Wisser (1969). By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. 2nd edition 2026 May 14. Revised pages 44-46. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoy reading and disagreeing. Publisher: Kuhn von (...) Verden Verlag. Language: English and German. Includes bibliographical references and an index. Pages 1-48. Warning: this is an AI Working Zone. -/- Who am I, the author of these investigations? That is an important question. I, Daniel Fidel Ferrer, am the human copyright owner (©Daniel Fidel Ferrer, 2026) of this work. Technically, I am the one guiding the process as the “prompt engineer.” Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I can consider myself the Maestro suggeritore. The 1969 interview between Martin Heidegger and Richard Wisser. Philosophy and Society: Heidegger’s Critique of Subjectivity. How does Heidegger’s reading of Marx’s slogan about changing the world alter contemporary understandings of praxis and theory in social movements? Example quote, “Heidegger’s aphorism “science does not think” is an analytic claim about disciplinary limits: scientific practice presupposes but neglects the ontological conditions of its own intelligibility.” Auch muss auf Heidegger zurückgegangen werden! Translation from German to English. 262. MARTIN HEIDEGGER IN CONVERSATION (17 September 1969). From GA 16: 702-710. (shrink)

Heidegger AI Textual Research Investigations: Zürcher Seminar (1951) (GA 9). By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoy reading and disagreeing. Publisher: Kuhn von Verden Verlag. Language: English and German. Includes bibliographical references and an (...) index. Pages 1-196 (second edition added 2026 May 12). Who am I, the author of these investigations? That is an important question. I, Daniel Fidel Ferrer, am the human copyright owner (©Daniel Fidel Ferrer, 2026) of this work. Technically, I am the one guiding the process as the “prompt engineer.” Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I can consider myself the Maestro suggeritore. Martin Heidegger’s Zürcher Seminar (1951) Gesamtausgabe Band 15. Seminare 1951-73. Zürcher Seminar. November 6, 1951, in Zürich, Switzerland. GA 15:423-439. Sections in this book on Planetary Modernity, Hölderlin, Anaximander, Parmenides, technology, overcoming metaphysics, Greeks, Nietzsche, Marx, Marcuse, and Sartre. This has not been translated into English (as of 2026). Glossary in Greek, Chinese, Spanish, Portuguese, French, and English. Warning. Warning: this is an AI Working Zone. Auch muss auf Heidegger zurückgegangen werden! Translation from German to English. Zurich Seminar by Martin Heidegger. (shrink)

The debate over whether artificial intelligence systems "understand" language has reached an unproductive impasse, with skeptics denying any philosophically significant competence and enthusiasts implying capacities far beyond what current systems possess. This paper argues that the impasse results in part from a persistent conflation of two different competence profiles. We define Textbook Competence as the capacity to perform context-sensitive, inferentially disciplined transformations on structured symbolic material across registers and tasks without requiring direct immersion in the domain described. Steeped Competence is (...) then defined as the capacity acquired through sustained participation in a domain, shaped by perceptual, motor, social, and cultural attunement that is often difficult to verbalize and resistant to transmission through language alone. Current large language models plausibly exhibit the former on an operational reading and lack the latter in the ordinary human sense. We position this binary relative to more refined cuts in the recent literature and argue that it tracks a real categorical break in acquisition mode whose operational consequences the more refined cuts are not designed to make perspicuous. The brittleness profile documented in recent empirical work — strong performance on tasks statistically near the training distribution, sharp degradation under distribution shift — is precisely what the framework predicts and is therefore positive evidence for it, not merely a caveat on the empirical claim. By distinguishing these two competence profiles and mapping the evidence for each, we clarify the apparent paradox of systems that seem to "understand everything and nothing." The distinction is offered as an analytic tool with substantive philosophical and empirical commitments, not as a complete taxonomy of cognition. (shrink)

This contribution builds upon recent work by the author on investigating the ways in which epistemic defeat arises in machine learning (ML) and the ethical problems that it raises. The contribution presents an epistemological approach to the problems of opacity and algorithmic bias in machine learning by discussing them in terms of the forms of epistemic defeat that arise in them. A taxonomy of epistemic defeaters, including transparent, opaque, and inherited defeaters, is developed and employed for this purpose. The Black-Box (...) problem is discussed, and a distinction between two construals of the ‘why’ question that defines the Black-Box problem is proposed. These construals are based upon a distinction between a request for an explanation of a model’s output in terms of a causal mechanism and an explanation in terms of reasons or justification. The latter is shown to be the more challenging problem that remains difficult to solve even if a solution to the former is available. Particular attention will be given to the phenomenon of inherited epistemic defeat, which is a form of epistemic defeat involved in algorithmic bias that is derived from biases encoded in training data. Such defeaters are inherited via training data and inculcated in machine learning models, thereby exacerbating and obfuscating the effect of their epistemic defeat. This contribution builds upon a recent collaborative project conducted on machine learning in midwifery by the author (Begley et al. 2021). Examples from that study are considered along with analogous examples from predictive policing. Recommendations are made in particular regarding how inherited epistemic defeat should best be addressed. (shrink)

While various organizations now actively encourage LLM use in classrooms, we still lack rigorous, systematic evaluations of how well these models actually perform the fundamental tasks of language pedagogy. This paper examines whether state-of-the-art LLMs can deliver the kind of corrective feedback and methodological explanations that language learners need. The study tests multiple large language models on their ability to identify, correct, and explain common learner mistakes in English, by systematically varying model parameters to investigate how these technical adjustments affect (...) output quality, pedagogical clarity, and consistency, along with using retrieval-augmented generation to query methodological data. The evaluation employs automated metrics (GLEU, BERTScore) but also human expert judgments to capture dimensions that purely computational measures miss: linguistic nuance, cultural sensitivity, and instructional appropriateness. While models demonstrate impressive surface-level correction abilities, their explanations often lack the terminological and domain knowledge that effective language teaching requires, suggesting that current enthusiasm for AI-assisted language learning may be outpacing our understanding of these systems’ actual pedagogical competence. (shrink)

Heidegger AI Textual Research Investigations: Heidegger’s Reading of Kant’s Critique of the Power of Judgment (GA 84.2). By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoy reading and disagreeing. Publisher: Kuhn von Verden Verlag. Language: English (...) and German. Includes bibliographical references and a short glossary. Pages 1-164. Warning. Warning: this is an AI Working Zone. Warning. Perhaps an impending danger of unexpected, unknown texts. This is an AI investigation into Martin Heidegger’s texts. Who am I, the author of these investigations? That is an important question. I, Daniel Fidel Ferrer, am the human copyright owner (©Daniel Fidel Ferrer, 2026) of this work. Technically, I am the one guiding the process as the “prompt engineer.” Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I can consider myself the Maestro suggeritore. -/- Text for AI Investigations. Martin Heidegger’s Gesamtausgabe Band 84.2 Seminare: Leibniz - Kant – Schiller. Date 1936-1942. Editor Günther Neumann. Description. Freiburg seminars SS 1931 and WS 1935-36. Oberstufe: Kant, Kritik der Urteilskraft. Seminar 1936 Summer semester. Upper level: Kant, Critique of Power Judgment. Section included in this analysis is KRITIK DER AESTHETISCHEN URTEILSKRAFT (DIE FRAGE NACH DER »KUNST«) SOMMERSEMESTER 1936. GA 84.2:1-328. This has not been translated into English (as of 2026). Example of Heidegger citations of Kant’s work “K . d . U. § 44, C 179.” Heidegger, Martin, and Günther Neumann. Seminare Kant - Leibniz - Schiller: Teil 2: Sommersemester 1936 Bis Sommersemester 1942. Vittorio Klostermann, 2023. Quote from this book, “The document records Martin Heidegger’s 1936 summer semester seminar on Kant’s Kritik der Urteilskraft (KU, the “Third Critique”). This seminar belongs to Heidegger’s so called transition period (1933–1938), situated between Being and Time (1927) and the later “history of Being” (Seinsgeschichte). The interpretation is therefore neither purely Kantian nor purely aesthetic, but ontological and transitional. Heidegger approaches Kant’s Third Critique not as a theory of art or taste in the modern sense, but as a decisive metaphysical text in which Kant nearly—yet ultimately fails to—break free from modern subjectivism.” (Page 2). Comparison of other German philosophers on Kant third Critique. Warning: this is an AI Working Zone. Auch muss auf Heidegger zurückgegangen werden! -/- . (shrink)

Heidegger AI Textual Research Investigations: What is – Philosophy? By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. All Rights are Reserved. Intended copies of this work can be used for research and teaching. No change in the content, and must include my full name, Daniel Fidel Ferrer. Enjoy reading and disagreeing. Publisher: Kuhn von Verden Verlag. Language: English and German. Includes bibliographical references and an index. (...) Pages 1-87. 1955. Was ist das – die Philosophie? Colloque at Cerisy-la-Salle, Normandy, France, on 28 August 1955. Identität und Differenz (1955–1957), GA 11:7-26. Heidegger first trip to France at age 65. There were several days of seminars after the lecture which are in GA 91:347-491. Ergänzungen und Denksplitter. Examples of some of the intellectual and philosophers who were there: Gabriel Marcel, Jean Beaufret, Paul Ricoeur, François Fédier (the driver), Abbots Morel and Pépin, Fathers Fessard, Kleiber, Léger, René Char, Georges Braque, Jacques Lacanand, Kostas Axelos, Lucien Goldmann, Alexis Philonenko, Walter Biemel, Jeanne Hersch, Beda Allemann, and Antoine Vergote. About 50 French intellectuals were there participating in the seminar. See the book, Heidegger in France. Who am I, the author of these investigations? That is an important question. I, Daniel Fidel Ferrer, am the human copyright owner (©Daniel Fidel Ferrer, 2026) of this work. Technically, I am the one guiding the process as the “prompt engineer.” Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I can consider myself the Maestro suggeritore. 12 week seminar outlined. Glossary example, Greek: οὐσία Latin: essentia German: Wesen Spanish: esencia Portuguese: essência English: essence. Warning: this is an AI Working Zone. Auch muss auf Heidegger zurückgegangen werden! (shrink)

Heidegger AI Textual Research Investigations: On the Question of Being (1955) (GA 9). By Daniel Fidel Ferrer. Copyright©2026 Daniel Fidel Ferrer. All rights reserved. Attribution- NonCommercial-NoDerivs CC BY-NC-ND. Imprint 1.0. 2026. WIPO Copyright Treaty (WCT) digital. 1-112 pages. Warning. Warning: this is an AI Working Zone. Warning. Perhaps an impending danger of unexpected, unknown texts. This is an AI investigation into Martin Heidegger’s texts. Daniel Fidel Ferrer as the “Prompt Artist”. AI conducted by ©Daniel Fidel Ferrer, 2026. Maestro. I could (...) consider myself the Maestro suggeritore. Heidegger sources. Wegmarken (1919–1961). Martin Heidegger Gesamtausgabe 9. GA 9. “On the Question of Being.” “Zur Seinsfrage (1955).” GA 9:485-426. The title has been changed. It was originally: “On ‘The Line.’”. Commemorative volume for Ernst Jünger on his 60th birthday. See previous investigations. Heidegger AI Textual Research Investigations: Ernst Jünger (GA 90) by Daniel Fidel Ferrer, 2026. This is a letter Heidegger wrote to Ernst Jünger. Quote this book, “The letter unfolds in four major movements: 1. Engagement with Jünger’s “line” Heidegger clarifies that he will not speak über (across) the line but von (about) the line — its essence, origin, and place. 2. Reconstruction of Jünger’s metaphysics He analyzes Jünger’s earlier works (Der Arbeiter, Über den Schmerz) to show how they remain within the metaphysical tradition of will to power. 3. Heidegger’s own account of Nihilism is not merely the collapse of values but the culmination of metaphysics as the forgetting of Being. 4. The path beyond metaphysics Overcoming nihilism requires a transformation of language and thinking — a “Verwindung” rather than an “Überwindung.” (Page 20). One hundred keyword glossary in Greek, Latin, German, Portuguese, and English. Heidegger’s Ethical and Political Quietism in Contrast with Herbert Marcuse and Karl Marx (page 205). Heidegger’s mystical, quietist, grace-based theology of Being. The Hidden Theology Behind the Critique of Heidegger (page 221). Leads to questions about theology and then the final question becomes, Are Marx and Marcuse as Secular Heirs of Judeo Christian Prophetic and Eschatological Thought in conflict with Heidegger (page 221)? Auch muss auf Heidegger zurückgegangen werden! (shrink)