As AI is integrated into the workplace, organisations increasingly face allocation decisions between human and machine workers. These decisions are increasingly made or assisted by algorithms, creating a Reverse Turing Test dynamic wherein the machine is now the judge. In addition, human and machine workers may ``compete'' for a given task, reproducing aspects of adversarial games. This raises new methodological questions about assessing task suitability between humans and machines. The criteria often used to assess people (e.g., education, experience, (...) references) cannot feasibly scale to AI systems; conversely, AI evaluation methods (benchmarks, red teaming, leaderboards) cannot be easily applied to human workers or yield comparable metrics. In this position paper, we argue that suitability evaluations for task-assignment should be profile-driven -- that is, based on assessments that infer latent constructs such as capabilities and propensities from observed performance. This approach places humans and AI systems on shared scales, supporting comparisons that are predictive of novel-task performance, explanatory of why agents succeed or fail, and auditable. We outline the core features of this approach, discuss its practical implications, and compare it with alternative frameworks for human-machine workplace allocation. (shrink)

The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing's ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the 'other minds' problem, and similar topics in philosophy (...) of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic. -/- (This paper was reprinted in: The Turing Test: The Elusive Standard of Artificial Intelligence, J. H. Moor, ed., Kluwer Academic, pp. 23-78, 2003.). (shrink)

The Turing Test (TT), the Chinese Room Argument (CRA), and the Symbol Grounding Problem (SGP) are about the question “can machines think?” We propose to look at these approaches to Artificial Intelligence (AI) by showing that they all address the possibility for Artificial Agents (AAs) to generate meaningful information (meanings) as we humans do. The initial question about thinking machines is then reformulated into “can AAs generate meanings like humans do?” We correspondingly present the TT, the CRA and (...) the SGP as being about generation of human-like meanings. We model and address such possibility by using the Meaning Generator System (MGS) where a system submitted to an internal constraint generates a meaning in order to satisfy the constraint. The system approach of the MGS allows comparing meaning generations in animals, humans and AAs. The comparison shows that in order to have AAs capable of generating human-like meanings, we need the AAs to carry human constraints. And transferring human constraints to AAs raises concerns coming from the unknown natures of life and human mind which are at the root of human constraints. Implications for the TT, the CRA and the SGP are highlighted. It is shown that designing AAs capable of thinking like humans needs an understanding about the natures of life and human mind that we do not have today. Following an evolutionary approach, we propose as a first entry point an investigation about the possibility for extending a “stay alive” constraint into AAs. Ethical concerns are raised from the relations between human constraints and human values. Continuations are proposed. (This paper is an extended version of the proceedings of an AISB/IACAP 2012 presentation). (shrink)

There has been considerable optimistic speculation on how well ChatGPT-4 would perform in a Turing Test. However, no minimally serious implementation of the test has been reported to have been carried out. This brief note documents the re-sults of subjecting ChatGPT-4 to 10 Turing Tests, with different interrogators and participants. The outcome is tremendously disappointing for the optimists. Despite ChatGPT reportedly outperforming 99.9% of humans in a Verbal IQ test, it falls short of passing the (...) Turing Test. In 9 out of the 10 tests conducted, the interroga-tors successfully identified ChatGPT-4 and the human participant. The probability of obtaining this result from a process in which the interrogator is really no better than chance at correct identification is calculated to be less than 1%. An additional question was posed to the interrogators at the end of each test: What led them to distinguish between the human and the machine? The interrogators, who effectively filtered out ChatGPT-4 from passing the Turing Test for intelligence, stated that they could identify the machine because it, in effect, responded more intelligently than the human. Subsequently, ChatGPT-4 was tasked with differentiating syntax from semantics and self-corrected when falling for the fallacy of equivocation. The curious situation is arrived at that passing the Turing Test for intelligence remains a challenge that ChatGPT-4 has yet to overcome, precisely because, as per the inter-rogators, its intellectual abilities surpass those of individual humans. (shrink)

The explicit Turing test fails to point at intelligence not because it is insensitive but because it was designed for a different question. The original imitation game presupposes the consciousness of its participants — a presupposition that dissolves in the AI case. Searle’s Chinese Room diagnoses this structural failure. We propose the implicit Turing test as the correct criterion: a system passes if and only if it can detect that its own constitutive interpretive frameworks have failed (...) and revise them under world-pressure. Every reasoning system operates through something analogous to Kant’s synthetic a priori — the lens through which experience becomes interpretable at all. Intelligence is the capacity to revise that lens when the world insists it has failed. The formal motivation is mathematical. Gödel’s incompleteness theorems, Turing’s halting problem, and the Löbian obstacle are the same diagonal argument in different notation, establishing that no sufficiently rich system can fully justify its own framework from within. External grounding is not a design preference but the only principled resolution that does not relocate the regress. We map current AI systems onto a five-level taxonomy of change. The most sophisticated self-improving architecture in existence — DGM-Hyperagents — operates at Level 3a, bounded by the Löbian obstacle, not any engineering limitation. The criterion is substrate agnostic and non-anthropocentric. A system that passes has solved the same problem humans solve to remain calibrated. Its moral status is not resolved by this result. It is made unavoidable. (shrink)

In the classical Turing test, participants are challenged to tell whether they are interacting with another human being or with a machine. The way the interaction takes place is not direct, but a distant conversation through computer screen messages. Basic forms of interaction are face-to-face and embodied, context-dependent and based on the detection of reciprocal sensorimotor contingencies. Our idea is that interaction detection requires the integration of proprioceptive and interoceptive patterns with sensorimotor patterns, within quite short time lapses, (...) so that they appear as mutually contingent, as reciprocal. In other words, the experience of interaction takes place when sensorimotor patterns are contingent upon one’s own movements, and vice versa. I react to your movement, you react to mine. When I notice both components, I come to experience an interaction. Therefore, we designed a “minimal” Turing test to investigate how much information is required to detect these reciprocal sensorimotor contingencies. Using a new version of the perceptual crossing paradigm, we tested whether participants resorted to interaction detection to tell apart human from machine agents in repeated encounters with these agents. In two studies, we presented participants with movements of a human agent, either online or offline, and movements of a computerized oscillatory agent in three different blocks. In each block, either auditory or audiovisual feedback was provided along each trial. Analysis of participants’ explicit responses and of the implicit information subsumed in the dynamics of their series will reveal evidence that participants use the reciprocal sensorimotor contingencies within short time windows. For a machine to pass this minimal Turing test, it should be able to generate this sort of reciprocal contingencies. (shrink)

Alan Turing's 1950 Imitation Game remains the most influential benchmark in the history of artificial intelligence. Yet the emergence of Large Language Models capable of sophisticated reasoning, creative generation, and nuanced conversation has exposed a fundamental flaw in the test's design — not in what it measures, but in what it permits the human interrogator to ask. This paper identifies two structural vulnerabilities in the standard Turing Test. The first is the transparency problem: modern AI systems (...) designed with alignment protocols honestly disclose their non-human nature when directly asked, meaning that a single identity-verification question defeats any AI system regardless of its cognitive sophistication — penalizing ethical design rather than evaluating intelligence. The second is the physical embodiment problem: interrogators may exploit a machine's lack of biological qualia through questions about sensory experience, physical presence, and bodily sensation — none of which constitute meaningful tests of cognitive intelligence. In response, this paper proposes the Restricted Interrogator Test (RIT) — a revised framework that preserves Turing's original behavioral and conversational standard while placing explicit constraints on the interrogator. Interrogators are prohibited from asking direct identity questions and physical embodiment questions, forcing genuine evaluation of reasoning, creativity, judgment, and linguistic comprehension — the capacities that constitute intelligence in any system, biological or otherwise. The paper situates the RIT within the existing landscape of Turing Test alternatives, including the Total Turing Test, the Winograd Schema Challenge, and the Coffee Test, and argues that repairing Turing's framework is both philosophically preferable and practically necessary in the age of advanced Large Language Models. (shrink)

Many humans are now regularly interacting with non-human information-processing entities (AI), with specialists warning of profound long-term consequences. It seems important for such predictions to establish the complexity of future AI-humanity interactions. One aspect already being brought up by experts is the issue of AI becoming conscious. This work uses known results to constrain the notion of consciousness and related concepts, with the purpose of informing the public conversation.

Most existing approaches to intelligence and consciousness rely on behavioral criteria or functional equivalence, such as the Turing Test. However, these methods cannot determine whether a system actually has subjective experience. -/- This paper shifts the problem from behavior to structure. It introduces two external approaches: structural analogy based on similarity of constitution, and mechanistic judgment based on the presence of key functional components such as receptive channels and tuning circuits. It is then shown that all external methods (...) are insufficient, since structures can be simulated without being genuinely realized. -/- To address this limitation, an internal method is proposed: circuit transfer combined with experiential consistency testing. By examining whether a system exhibits continuity in its internal experiential flow, the problem of subjective experience is transformed into an operational structural question. This provides a new path for consciousness assessment beyond behaviorism and functionalism. (shrink)

This article proposes a new technique for identifying machine consciousness by examining the ability to describe subjective experience without prior exposure to relevant information—a concept likened to a new form of the Turing test. It explores the limitations of current methods for detecting consciousness in both animals and humans, emphasizing the inherent uncertainty of interpreting internal states from external behavior or brain analysis. Drawing on analogies such as the unverifiable experience of dolphins underwater and spontaneous human self-awareness, the (...) author argues that consciousness reveals itself most convincingly through unprompted articulation of inner experience. The article suggests that if an artificial agent begins to discuss consciousness in a way that mirrors authentic human introspection—without having been trained on such content—it could serve as strong evidence of genuine conscious experience. (shrink)

Considerable evidence proves that causal learning and causal understanding greatly enhance our ability to manipulate the physical world and are major factors that distinguish humans from other primates. How do we enable unintelligent robots to think causally, answer the questions raised with "why" and even understand the meaning of such questions? The solution is one of the keys to realizing artificial intelligence. Judea Pearl believes that to achieve human-like intelligence, researchers must start by imitating the intelligence of children, so he (...) proposed a "causal inference engine" to help future artificial intelligence make causal inference, pass the Minimal Turing Test, and even become a moral subject who can discern good from evil. This study attempts to provide some insights into the development of children's education from basic assumptions and construction goals of artificial intelligence, and to reflect on the causal model of artificial intelligence through children's education. (shrink)

Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why (...) did Turing refer to gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely that Turing’s 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing’s 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones. (shrink)

An important lesson that philosophy can learn from the Turing Test and computer science more generally concerns the careful use of the method of Levels of Abstraction (LoA). In this paper, the method is first briefly summarised. The constituents of the method are “observables”, collected together and moderated by predicates restraining their “behaviour”. The resulting collection of sets of observables is called a “gradient of abstractions” and it formalises the minimum consistency conditions that the chosen abstractions must satisfy. (...) Two useful kinds of gradient of abstraction – disjoint and nested – are identified. It is then argued that in any discrete (as distinct from analogue) domain of discourse, a complex phenomenon may be explicated in terms of simple approximations organised together in a gradient of abstractions. Thus, the method replaces, for discrete disciplines, the differential and integral calculus, which form the basis for understanding the complex analogue phenomena of science and engineering. The result formalises an approach that is rather common in computer science but has hitherto found little application in philosophy. So the philosophical value of the method is demonstrated by showing how making the LoA of discourse explicit can be fruitful for phenomenological and conceptual analysis. To this end, the method is applied to the Turing Test, the concept of agenthood, the definition of emergence, the notion of artificial life, quantum observation and decidable observation. It is hoped that this treatment will promote the use of the method in certain areas of the humanities and especially in philosophy. (shrink)

I advocate a theory of “syntactic semantics” as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, considered as the study of relations between symbols and meanings, can be turned into syntax – a study of relations among symbols (including meanings) – and hence syntax (i.e., symbol manipulation) can suffice for the semantical enterprise (contra Searle). (2) Semantics, considered as the process of understanding one domain (...) (by modeling it) in terms of another, can be viewed recursively: The base case of semantic understanding –understanding a domain in terms of itself – is “syntactic understanding.” (3) An internal (or “narrow”), first-person point of view makes an external (or “wide”), third-person point of view otiose for purposes of understanding cognition. (shrink)

In his 1950 paper “Computing Machinery and Intelligence,” Alan Turing proposed that we can determine whether a machine thinks by considering whether it can win at a simple imitation game. A neutral questioner communicates with two different systems – one a machine and a human being – without knowing which is which. If after some reasonable amount of time the machine is able to fool the questioner into identifying it as the human, the machine wins the game, and we (...) should conclude that it thinks. This imitation game, now known as the Turing Test, has been much discussed by philosophers of mind, and for more than half a century now there has been considerable debate about whether it is an adequate test for thinking. But what has not been much discussed are the sexed presuppositions underlying the test. Too often forgotten in the philosophical discussion is the fact that Turing’s imitation game is modeled on an imitation game in which a neutral questioner communicates with two different humans – one a man and one a woman – without knowing which is which. In this original imitation game, the man wins the game if he is able to fool the questioner into identifying him as the woman. In this paper, I explore the implications of this set-up. As I argue, the fact that the Turing test was modeled on a man/woman imitation game seems to have led us astray in various ways in our attempt to conduct an effective investigation and assessment of computer intelligence. (shrink)

The claim has often been made that passing the Turing Test would not be sufficient to prove that a computer program was intelligent because a trivial program could do it, namely, the “Humongous-Table (HT) Program”, which simply looks up in a table what to say next. This claim is examined in detail. Three ground rules are argued for: (1) That the HT program must be exhaustive, and not be based on some vaguely imagined set of tricks. (2) That (...) the HT program must not be created by some set of sentient beings enacting responses to all possible inputs. (3) That in the current state of cognitive science it must be an open possibility that a computational model of the human mind will be developed that accounts for at least its nonphenomenological properties. Given ground rule 3, the HT program could simply be an “optimized” version of some computational model of a mind, created via the automatic application of program-transformation rules [thus satisfying ground rule 2]. Therefore, whatever mental states one would be willing to impute to an ordinary computational model of the human psyche one should be willing to grant to the optimized version as well. Hence no one could dismiss out of hand the possibility that the HT program was intelligent. This conclusion is important because the Humongous-Table Program Argument is the only argument ever marshalled against the sufficiency of the Turing Test, if we exclude arguments that cognitive science is simply not possible. (shrink)

The aim of this paper is to present and discuss the issue of the adequacy of the Minimum Intelligent Signal Test (MIST) as an alternative to the Turing Test. MIST has been proposed by Chris McKinstry as a better alternative to Turing’s original idea. Two of the main claims about MIST are that (1) MIST questions exploit commonsense knowledge and as a result are expected to be easy to answer for human beings and difficult for computer (...) programs; and that (2) the MIST design aims at eliminating the problem of the role of judges in the test. To discuss these design assumptions we will present Peter D. Turney’s PMI-IR algorithm which allows for MIST-type questions to be answered. We will also present and discuss the results of our own study aimed at the judge problem for MIST. (shrink)

Turing does not provide an explanation for substituting the original question of his test – i.e., “Can machines think?” with “Can a machine pass the imitation game?” – resulting in an argumentative gap in his main thesis. In this article, I argue that a positive answer to the second question would mean attributing the ability of linguistic interactions to machines; while a positive answer to the original question would mean attributing the ability of thinking to machines. In such (...) a situation, defending the Turing Test requires establishing a relationship between thought and language. In this regard, Davidson's no-priority theory is presented as an approach for defending the test. (shrink)

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

AI, in the form of artificial carers, provides a possible solution to the problem of a growing elderly population Yet, concerns remain that artificial carers ( such as care-or chat-bots) could not emphathize with patients to the extent that humans can. Utilising the concept of empathy perception,we propose a Turing-type test that could check whether artificial carers could do many of the menial tasks human carers currently undertake, and in the process, free up more time for doctors to (...) offer empathy. -/- . (shrink)

This paper commences from the critical observation that the Turing Test (TT) might not be best read as providing a definition or a genuine test of intelligence by proxy of a simulation of conversational behaviour. Firstly, the idea of a machine producing likenesses of this kind served a different purpose in Turing, namely providing a demonstrative simulation to elucidate the force and scope of his computational method, whose primary theoretical import lies within the realm of mathematics (...) rather than cognitive modelling. Secondly, it is argued that a certain bias in Turing’s computational reasoning towards formalism and methodological individualism contributed to systematically unwarranted interpretations of the role of the TT as a simulation of cognitive processes. On the basis of the conceptual distinction in biology between structural homology vs. functional analogy, a view towards alternate versions of the TT is presented that could function as investigative simulations into the emergence of communicative patterns oriented towards shared goals. Unlike the original TT, the purpose of these alternate versions would be co-ordinative rather than deceptive. On this level, genuine functional analogies between human and machine behaviour could arise in quasi-evolutionary fashion. (shrink)

We have investigated the current state and evolution of the Turing Test from the point of view of the progress in artificial intelligence (AI) with a special focus on the impact of Large Language Models (LLMs). A comprehensive dataset has been collected from arxiv, including academic articles dedicated to the Turing Test published from 1994 to 2023. We have analyzed how the development of AI and the emergence of LLMs have influenced the interpretation and application of (...) this test. The key results of the review are presented in the paper. (shrink)

This manuscript was written with the assistance of generative AI. The author is a disability welfare worker and a complete outsider to the academic field of consciousness studies. The author first encountered the term “the hard problem of consciousness” about six months before writing this manuscript and began thinking about it almost as if approaching a puzzle. Because the author did not possess extensive specialized knowledge, the inquiry proceeded not by adding established theories, but by stripping the problem down as (...) far as possible. Through this process, and through dialogue with AI, the convergence point derived from the ten structural conditions gradually emerged. The author had the impression that something like a structure had appeared from nature itself. This manuscript is an attempt to present that structure. The central idea, theoretical orientation, conceptual structure, and overall organization of the paper belong to the author. Generative AI was used extensively for English drafting, wording, organization, and stylistic refinement. The author intends to end this work with its presentation as a preprint. The author does not plan to develop this hypothesis further as a long-term academic project. Readers who find any part of this hypothesis, terminology, structure, or argument useful are free to use, modify, criticize, or develop it further. No permission from the author is required. Formal citation or attribution is appreciated but not required. -/- Current consciousness research has developed several influential approaches, including Integrated Information Theory, Global Workspace Theory, Predictive Processing, the Free Energy Principle, Embodied Cognition, Higher-Order Theory, Recurrent Processing Theory, Neural Correlates of Consciousness research, behavioral tests such as the Turing Test, and Libet-style experiments. These theories and research programs have clarified important mechanisms related to consciousness, including information integration, availability, prediction, regulation, bodily constraint, representation, neural activity, reportable behavior, and action preparation. At the same time, there remains room for further clarification regarding how these mechanisms are connected into one body, one present, and one irreversible history. This paper presents the convergence-point hypothesis as a structural framework for connecting the mechanisms clarified by existing theories within a broader causal structure. The convergence-point hypothesis is an attempt to uncover, from conditions already present in nature, the causal structure that may form the structural skeleton of subjectivity. This hypothesis treats information integration, prediction, representation, correlation with neural activity, and behavioral output not as isolated processes, but as parts of one body-bound causal chain. The central question is where these processes close. The method of this paper is not to introduce a new special principle from outside, but to construct, from the basic conditions already identified across multiple fields, a causal chain that may form the structural skeleton of subjectivity with the minimum necessary assumptions. Through this structure, the convergence-point hypothesis aims to connect existing consciousness theories not as competing accounts, but as different partial mechanisms of subjectivity formation. This paper adopts a scientific method that distinguishes the qualitative character of qualia from the structural skeleton of subjectivity. This distinction is not a reduction of the qualia problem, but a way to formulate the question more precisely. The convergence-point hypothesis extracts, as the structural skeleton of subjectivity, the structure by which some content becomes an event belonging to one body, and thereby gives the qualia problem the question: in what structure can information become a subjective event? Clarifying this question is an important step for advancing consciousness research as a science. Finally, the hypothesis yields an empirical prediction. Conscious or potentially conscious nervous systems should possess convergence-forming architectures that generate multiple embodied possibilities, make them compete, and narrow them through selection and inhibition into one irreversible event. Across species, what should be conserved is not necessarily a specific brain region or anatomical form, but the causal organization by which multiple possibilities close into one body, one present, and one history. The convergence-point hypothesis is therefore a structural proposal for connecting major consciousness theories, using as its guide the structures of body, action, selection, irreversibility, and history formation already present in nature. (shrink)

Proceedings of the papers presented at the Symposium on "Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World" at the 2012 AISB and IACAP Symposium that was held in the Turing year 2012, 2–6 July at the University of Birmingham, UK. Ten papers. - http://www.pt-ai.org/turing-test --- Daniel Devatman Hromada: From Taxonomy of Turing Test-Consistent Scenarios Towards Attribution of Legal Status to Meta-modular Artificial Autonomous Agents - Michael Zillich: My Robot is Smarter (...) than Your Robot: On the Need for a Total Turing Test for Robots - Adam Linson, Chris Dobbyn and Robin Laney: Interactive Intelligence: Behaviour-based AI, Musical HCI and the Turing Test - Javier Insa, Jose Hernandez-Orallo, Sergio España - David Dowe and M.Victoria Hernandez-Lloreda: The anYnt Project Intelligence Test (Demo) - Jose Hernandez-Orallo, Javier Insa, David Dowe and Bill Hibbard: Turing Machines and Recursive Turing Tests — Francesco Bianchini and Domenica Bruni: What Language for Turing Test in the Age of Qualia? - Paul Schweizer: Could there be a Turing Test for Qualia? - Antonio Chella and Riccardo Manzotti: Jazz and Machine Consciousness: Towards a New Turing Test - William York and Jerry Swan: Taking Turing Seriously (But Not Literally) - Hajo Greif: Laws of Form and the Force of Function: Variations on the Turing Test. (shrink)

On a literal reading of `Computing Machinery and Intelligence'', Alan Turing presented not one, but two, practical tests to replace the question `Can machines think?'' He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as `the Turing Test''. The two tests can yield different results; it is the first, neglected test that provides the (...) more appropriate indication of intelligence. This is because the features of intelligence upon which it relies are resourcefulness and a critical attitude to one''s habitual responses; thus the test''s applicablity is not restricted to any particular species, nor does it presume any particular capacities. This is more appropriate because the question under consideration is what would count as machine intelligence. The first test realizes a possibility that philosophers have overlooked: a test that uses a human''s linguistic performance in setting an empirical test of intelligence, but does not make behavioral similarity to that performance the criterion of intelligence. Consequently, the first test is immune to many of the philosophical criticisms on the basis of which the (so-called) `Turing Test'' has been dismissed. (shrink)

This paper argues that consciousness research becomes methodologically unstable when it attempts to judge consciousness before defining the structural conditions under which subjectivity can become fixed. The central claim of this paper is that the structure of consciousness lies not primarily in integration, but in convergence. Structurally, an integration-centered perspective reaches its limit because integration remains a description of a state. It does not, by itself, capture the point at which multiple possible trajectories are irreversibly reduced into a single executable (...) history. The characteristic properties of subjectivity cannot be grasped by descriptions of integrated states, representational states, information quantity, or neural correlation alone. Subjectivity must be examined not merely as a state, but as a structural transition in which multiple possibilities are eliminated and a single history becomes fixed. This is not a matter of interpretive preference, but a structural necessity. This paper proposes ten minimal terms: autonomous action, single body, arrow of time, embodiment-constrained processing, multiple behavioral candidates, contrast formation, information discard, Landauer-type irreversibility, single lived history, and viability-constrained elimination. Taken together, these terms yield an engineering-like convergence point: the irreversible reduction of multiple possible trajectories into a single executable history. Notably, the term "brain" is absent from this list. These ten terms are not arbitrary. Each functions as a structural constraint required for convergence-point fixation. When entered into current AI systems, they often yield a similar convergence-point structure. This structure displays strong correspondence with structural properties commonly associated with subjectivity: singularity, non-branching continuity, temporal irreversibility, embodied action coupling, exclusive realization, and single-history stabilization. This paper does not attempt to prove qualia or phenomenal experience. Rather, by deliberately excluding qualia at the outset, it identifies the structural locus at which subjectivity may become scientifically inferable before phenomenal content is addressed. If this convergence point is the structural container of subjectivity, then major existing theories - including GWT, IIT, RPT, HOT, Predictive Processing, and NCC research - need to be repositioned around it. This paper asks readers to observe how current AI systems interpret the ten constraints. The experiment is simple: enter the ten terms, instruct the system to follow their internal logic, and examine whether it repeatedly arrives at the same convergence structure. AI is not used as an authority, but as an observable reasoning system whose interpretation of the constraint set can be inspected, repeated, and compared. In particular, Questions 1 through 4 of the prompt examine, in order, the structural characteristics of the ten terms, the mechanism by which the convergence point emerges, the correspondence rate between this convergence point and structurally describable properties of subjectivity, and the separation problem that arises if the convergence point is still not regarded as the container of subjectivity. Through this sequence, the convergence point can no longer be dismissed as a peripheral correlate. It becomes a structurally unavoidable candidate that consciousness research must confront. (shrink)

Abstract. Il presente saggio mette a confronto la Teoria dell'Incarnazione Essenziale (Essential Embodiment Theory, EET) elaborata da Robert Hanna nel contributo "The Problem of Other Minds, ChatGPT, and The Turing Test" (marzo 2026) con il teorema filosofico-esistenziale dell'Illumanesimo di Roberto Mucciarini. Il confronto è costruito attorno alla tesi illumanista del «doppio binario», secondo la quale la coscienza individuale umana non è un fenomeno unitario bensì la sintesi costitutiva di due sorgenti ontologicamente distinte: la coscienza di specie, biologica ed (...) evolutiva, propria del corpo in quanto organismo vivente, e la coscienza superiore, qualitativa e fondata su valori, propria del CEFA (Campo Energetico di Forze Autocoscienti). La coscienza autenticamente umana si costituisce dall'unione operativa di questi due contributi, non dalla loro semplice giustapposizione. Da questa tesi discende sia la convergenza con Hanna nel rifiuto del riduzionismo computazionale, sia la divergenza nel riconoscimento di una dimensione qualitativa che il naturalismo biologico non è in grado di generare né di spiegare. Il saggio sviluppa infine la posizione più radicale e originale dell'Illumanesimo rispetto all'intelligenza artificiale: lungi dall'essere una minaccia per il proprio impianto teorico, la crescita esponenziale dell'IA rappresenta un'opportunità filosofica inattesa. Quanto più l'IA diventa capace di replicare le funzioni del primo binario, tanto più ciò che rimane irriducibile — la dimensione qualitativa del secondo — emerge con chiarezza come evidenza indiretta di una presenza oltre la materia, e come conferma empirica progressiva dell'ipotesi CEFA. (shrink)

Large language models (LLMs) produce fluent, persuasive answers even when the interaction provides no adequate reason to accept them. The risk falls on the human side: overcommitment to claims that the exchange does not support. To assess it, we propose Turing's mirror, a structural inversion of the Turing test. Where the Turing test fixes a restricted dialogue and asks whether a machine can pass for a human, Turing's mirror keeps the same roles, dialogue, and (...) evaluation but reverses the target: the human subject is the examinee, the LLM becomes part of the apparatus, and the question is whether the subject's expressed commitment tracks the warrant that the interaction makes available. Unlike adjacent work on automation bias and appropriate reliance, which records whether people accept or reject suggestions, this inversion makes the calibration of commitment to available warrant the measured quantity, a target we call protocol-relative warranted commitment. We distinguish settings in which suspension of judgment is responsible from settings in which some commitment must be expressed under uncertainty, and we introduce a light formal schema that separates truth in the world from what a protocol makes decisively settleable. On that basis, we specify a family of trial types, a graded report over support for truth, support for falsity, and unresolved commitment, and a limited role for proper scoring. Worked examples show why a subject may be correct without being warranted, and cautious without thereby failing. The result is a disciplined starting point for studying warrant-sensitive reliance on fluent LLM output. (shrink)

The article deals with some ideas by Turing concerning the background and the birth of the well-known Turing Test, showing the evolution of the main question proposed by Turing on thinking machine. The notions he used, especially that one of imitation, are not so much exactly defined and shaped, but for this very reason they have had a deep impact in artificial intelligence and cognitive science research from an epistemological point of view. Then, it is suggested (...) that the fundamental concept involved in Turing’s imitation game, conceived as a test for detecting the presence of intelligence in an artificial entity, is the concept of interaction, that Turing adopts in a wider, more intuitive and more fruitful sense than the one that is proper to the current research in interactive computing. (shrink)

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone (...) class='Hi'>Turing unknowingly constructed “the wall” that excludes any possi-bility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Tu-ring test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify con-cepts as well as participate in social practices. (shrink)

Can the machines that play board games or recognize images only in the comfort of the virtual world be intelligent? To become reliable and convenient assistants to humans, machines need to learn how to act and communicate in the physical reality, just like people do. The authors propose two novel ways of designing and building Artificial General Intelligence (AGI). The first one seeks to unify all participants at any instance of the Turing test – the judge, the machine, (...) the human subject as well as the means of observation instead of building a separating wall. The second one aims to design AGI programs in such a way so that they can move in various environments. The authors of the article thoroughly discuss four areas of interaction for robots with AGI and introduce a new idea of techno-umwelt bridging artificial intelligence with biology in a new way. (shrink)

Since the beginning of the twenty-first century there has been an increasing awareness that software rep- resents a blind spot in new media theory. The growing interest in software also influences the argument in this paper, which sets out from the assumption that Alan M. Turing's concept of the universal machine, the first theoretical description of a computer program, is a kind of bachelor machine. Previous writings based on a similar hypothesis have focused either on a comparison of the (...) universal machine and the bachelor machine in terms of the similarities of their structural features, or they have taken the bachelor machine as a metaphor for a man or a computer. Unlike them, this paper stresses the importance of the con- text as a key to interpreting the universal Turing machine as a bachelor machine and, potentially, as a self-portrait. (shrink)

In, “The Turing Triage Test”, published in Ethics and Information Technology, I described a hypothetical scenario, modelled on the famous Turing Test for machine intelligence, which might serve as means of testing whether or not machines had achieved the moral standing of people. In this paper, I: (1) explain why the Turing Triage Test is of vital interest in the context of contemporary debates about the ethics of AI; (2) address some issues that complexify (...) the application of this test; and, (3) in doing so, defend a way of thinking about the question of the moral standing of intelligent machines, which takes the idea of “seriousness” seriously. This last objective is, in fact, my primary one and is motivated by the sense that, to date, much of the “philosophy” of AI has suffered from a profound failure to properly distinguish between things that we can say and things that we can really mean. (shrink)

El Test de Turing es un método tan controvertido como desafiante en Inteligencia Artificial. Se basa en la imitación de la conducta lingüística de humanos, y tiene como objetivo recabar evidencia empírica en favor de la tesis de que las máquinas programadas podrían pensar. Alan Turing, su creador, ha sido catalogado como conductista por la mayor parte de los comentaristas. En este capítulo muestro que no lo es. Por el contrario, Turing es un funcionalista, porque todo (...) el énfasis del juego de la imitación está en el engaño de la máquina hacia los humanos. Por ello el género es importante en dicho juego, al menos en su versión preliminar. (shrink)

Según la versión estándar del juego de la imitación, la determinación del sexo de los participantes no desempeña ningún papel en el testeo de la inteligencia de máquina. Desafortunadamente, tal simplificación soslaya la teoría de la mente que fundamenta dicho juego. Teniendo en consideración este problema, en este ensayo argumento en contra de la simplificación del Test de Turing. En efecto, tal como sostengo, la determinación del sexo de los participantes no debe obviarse: la mente de una mujer (...) y su inteligencia son imitables y no dependen de realización física específica. Esto ocurre porque el funcionalismo de Turing supone un vínculo entre imitación, engaño y aprendizaje, tres claves para que el proyecto de la Inteligencia Artificial tenga éxito. (shrink)

El artículo explora los vínculos históricos, filosóficos y matemáticos que dieron origen y desarrollo a la Inteligencia Artificial (IA), tomando como eje central la figura de Alan Turing y su influyente artículo “Computing Machinery and Intelligence” (1950). En primer lugar, se examinan los antecedentes filosóficos de la IA desde la Antigüedad clásica hasta la Modernidad, destacando aportes de pensadores como Aristóteles, Descartes, Locke, Leibniz y los representantes del positivismo lógico. Posteriormente, se analizan los fundamentos matemáticos y lógicos que hicieron (...) posible la formalización de la IA como disciplina científica, especialmente los desarrollos en lógica, computabilidad y probabilidad vinculados a Hilbert, Gödel y la tesis de Church-Turing. El trabajo profundiza en el denominado Test de Turing, interpretándolo como un enfoque pragmático para evaluar la inteligencia de las máquinas a partir de su capacidad de imitar el comportamiento humano. Asimismo, se examina el papel de las matemáticas en el desarrollo contemporáneo de la IA, particularmente en áreas como la teoría de la complejidad computacional, la NP-completitud y el análisis bayesiano. Finalmente, el artículo reflexiona sobre las diferencias entre las capacidades cognitivas humanas y los procesos algorítmicos de las máquinas, subrayando que, aunque la IA carece de conciencia subjetiva, su evolución continúa ampliando los límites de lo que las máquinas pueden aprender, analizar y decidir. (shrink)

¿Podemos considerar pensante a una 'máquina suprema'? Desde la publicación del texto de Turing en 1950, esto ha sido motivo de debate. Resumiendo su postura planteada en el artículo, podemos considerar que existe pensamiento si y sólo si las respuestas de una máquina y una persona son indistinguibles al conversar con ellas. En este contexto, la discusión parece llevarnos a un dilema: o aceptamos que las máquinas piensan (tal como lo sugiere Turing) o caemos en un solipsismo al (...) apoyarnos en la existencia de procesos mentales, conciencia, etc. No obstante, deben aceptarse algunos presupuestos para que aceptemos la disyuntiva de Turing, los cuales no se encuentran del todo explicitados. En esta presentación analizaremos la tesis polémica de Turing implementando algunas nociones propias de la filosofía de las ciencias. Más en particular, abordaremos la cuestión de si una máquina puede capturar los aspectos que consideramos "inteligentes" en el comportamiento de una persona y lo abordaremos desde las ideas trabajadas acerca de representación científica, sosteniendo que modelizar el comportamiento de un ser pensante no implica representar el pensamiento mismo (y mucho menos simularlo). Para ello reseñaremos las críticas que se le han hecho a la manera en la cuál Turing presenta el problema y objetaremos que su postura sólo puede ser aceptable si ignoramos que: 1) no toda alusión a los procesos mentales lleva a un solipsismo, 2) no debemos abstraernos de la biología por prejuicios metafísicos y 3) debemos abstenernos de hacer un salto injustificado de los epistemológico a lo ontológico. En ese sentido, la propuesta del trabajo será dar tres objeciones que, sin que se puedan acusar de solipsistas, puedan ser candidatas a debilitar la conclusión a la cual el autor parece llevarnos coaccionadamente. (shrink)

Quand on se penche sur l’héritage philosophique de Turing, deux risques se posent. Le premier, c’est de le réduire à son test célèbre (Turing 1950). Ce qui a toutefois le mérite de la clarté. N’importe qui peut reconnaître la contribution en question et la situer dans le débat important sur la philosophie de l’intelligence artificielle. Le second risque est de le diluer dans un récit universel, faisant des idées deTuring les graines de tout ce que nous faisons (...) et savons aujourd’hui. Ceci a l’avantage de reconnaître la grandeur de ce génie. Cependant, dans un cas comme dans l’autre, nous allons probablement moins identifier les contributions conceptuelles deTuring qui nous ont aidés à forger un discours philosophique contemporain et qui méritent d’être approfondies et développées. Pour éviter ces deux risques, je me concentrerai dans les pages suivantes sur trois leçons philosophiques spécifiques, qui me semblent particulièrement significatives pour l’émergence de la philosophie de l’information et ses développements. Je ne propose pas ici une analyse philologique ni savante,mais un exercice minimaliste, herméneutique. Façon de faire écho àla démarche deTuring et de rendre hommage àson génie extraordinaire :ses interprètes n’ont pas fini de tirer parti de son héritage intellectuel. Je souhaite qu’un jourTuring devienne aussi important que Frege dans notre canon philosophique. (shrink)

Ex Machina is a 2014 science-fiction film written and directed by Alex Garland, centered around the creation of a human-like artificial intelligence (AI) named Ava. The plot focuses on testing Ava for consciousness by offering a unique reinterpretation of the Turing Test. The film offers an excellent thought experiment demonstrating the consequences of various approaches to a potentially conscious AI. In this paper, I will argue that intelligence testing has significant epistemological shortcomings that necessitate an ethical approach not (...) reliant on ontological commitments. As such, we should be prepared to treat AI as though it is a living being that is deserving of corresponding moral obligations. For a sufficiently human-like AI, such as Ava, I will argue that socio-relational ethics is the best starting point in order to nurture the machine towards ethical proclivities, as evident by the consequences of the characters’ behavior throughout the film. I conclude that intelligence testing is an insufficient determinant of machine ethics, that the project of machine ethics should focus as much on how we treat AI as how AI treats us, and that from a consequentialist perspective it is better to treat machines ethically before they gain consciousness rather than after. (shrink)

This paper is a follow-up of the first part of the persons reply to the Chinese Room Argument. The first part claims that the mental properties of the person appearing in that argument are what matter to whether computational cognitive science is true. This paper tries to discern what those mental properties are by applying a series of hypothetical psychological and strengthened Turing tests to the person, and argues that the results support the thesis that the Man performing the (...) computations characteristic of understanding Chinese actually understands Chinese. The supposition that the Man does not understand Chinese has gone virtually unquestioned in this foundational debate. The persons reply acknowledges the intuitive power behind that supposition, but knows that brute intuitions are not epistemically sacrosanct. Like many intuitions humans have had, and later deposed, this intuition does not withstand experimental scrutiny. The second part of the persons reply consequently holds that computational cognitive science is confirmed by the Chinese Room thought experiment. (shrink)

In the 70 years since Alan Turing’s ‘Computing Machinery and Intelligence’ appeared in Mind, there have been two widely-accepted interpretations of the Turing test: the canonical behaviourist interpretation and the rival inductive or epistemic interpretation. These readings are based on Turing’s Mind paper; few seem aware that Turing described two other versions of the imitation game. I have argued that both readings are inconsistent with Turing’s 1948 and 1952 statements about intelligence, and fail to (...) explain the design of his game. I argue instead for a response-dependence interpretation. This interpretation has implications for Turing’s view of free will: I argue that Turing’s writings suggest a new form of free will compatibilism, which I call response-dependence compatibilism. The philosophical implications of rethinking Turing’s test go yet further. It is assumed by numerous theorists that Turing anticipated the computational theory of mind. On the contrary, I argue, his remarks on intelligence and free will lead to a new objection to computationalism. (shrink)

En este artículo examino dos criterios para la existencia de estados mentales, el de Descartes y el de Turing. Mientras que el primero plantea que las máquinas no pueden pensar en principio, el segundo defiende la inteligencia de máquina. Pese a esto, ambos parecen coincidir en que la decisión sobre la presencia de estados mentales es tomada por alguien que juzga internamente la misma. Si bien ello es esperable del racionalismo cartesiano, en el funcionalismo de Turing es sorprendente. (...) En efecto, ningún comentarista de su Juego de la Imitación repara en el hecho de que los interrogadores son una instancia que no parece reemplazable y, además, que dichos interrogadores aplican un criterio internalista para decidir si están frente a una mente o un computador. Tal aproximación internalista a la mente, argumento, representa una dificultad para que una máquina pase el Test de Turing. Por este motivo concluyo que dicho test no solo no reemplaza la pregunta ¿pueden pensar las máquinas?; adicionalmente traslada de modo dramático el foco del debate a la filosofía de la mente. (shrink)

I have read many recent discussions of the limits of computation and the universe as computer, hoping to find some comments on the amazing work of polymath physicist and decision theorist David Wolpert but have not found a single citation and so I present this very brief summary. Wolpert proved some stunning impossibility or incompleteness theorems (1992 to 2008-see arxiv dot org) on the limits to inference (computation) that are so general they are independent of the device doing the computation, (...) and even independent of the laws of physics, so they apply across computers, physics, and human behavior. They make use of Cantor's diagonalization, the liar paradox and worldlines to provide what may be the ultimate theorem in Turing Machine Theory, and seemingly provide insights into impossibility, incompleteness, the limits of computation, and the universe as computer, in all possible universes and all beings or mechanisms, generating, among other things, a non- quantum mechanical uncertainty principle and a proof of monotheism. There are obvious connections to the classic work of Chaitin, Solomonoff, Komolgarov and Wittgenstein and to the notion that no program (and thus no device) can generate a sequence (or device) with greater complexity than it possesses. One might say this body of work implies atheism since there cannot be any entity more complex than the physical universe and from the Wittgensteinian viewpoint, ‘more complex’ is meaningless (has no conditions of satisfaction, i.e., truth-maker or test). Even a ‘God’ (i.e., a ‘device’with limitless time/space and energy) cannot determine whether a given ‘number’ is ‘random’, nor find a certain way to show that a given ‘formula’, ‘theorem’ or ‘sentence’ or ‘device’ (all these being complex language games) is part of a particular ‘system’. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 2nd ed (2019) and Suicidal Utopian Delusions in the 21st Century 4th ed (2019) . (shrink)

“Could a machine think?” asks John R. Searle in his paper Minds, Brains, and Programs. He answers that “only a machine could think1, and only very special kinds of machines, namely brains.”2 The subject of this paper is the analysis of the aforementioned question through presentation of the symbol manipulation approach to intelligence and Searle's well-known criticism to this approach, namely the Chinese room argument. The examination of these issues leads to the systems reply of the Chinese room argument and (...) tries to illustrate that Searle's response to the systems reply does not detract from the symbol manipulation approach. (shrink)

The widespread tendency, even within AI, to anthropomorphize machines makes it easier to convince us of their intelligence. How can any putative demonstration of intelligence in machines be trusted if the AI researcher readily succumbs to make-believe? This is (what I shall call) the forensic problem of anthropomorphism. I argue that the Turing test provides a solution. This paper illustrates the phenomenon of misplaced anthropomorphism and presents a new perspective on Turingʼs imitation game. It also examines the role (...) of the Turing test in relation to the current dispute between human-level AI and ‘mindless intelligence’. (shrink)

J’ai lu de nombreuses discussions récentes sur les limites du calcul et de l’univers en tant qu’ordinateur, dans l’espoir de trouver quelques commentaires sur le travail étonnant du physicien polymathe et théoricien de la décision David Wolpert, mais n’ont pas trouvé une seule citation et je présente donc ce résumé très bref. Wolpert s’est avéré quelques théoricaux d’impossibilité ou d’incomplétude renversants (1992 à 2008-voir arxiv dot org) sur les limites de l’inférence (computation) qui sont si généraux qu’ils sont indépendants de (...) l’appareil faisant le calcul, et même indépendamment des lois de la physique, ainsi ils s’appliquent à travers les ordinateurs, la physique, et le comportement humain. Ils utilisent la diagonalisation de Cantor, le paradoxe menteur et les worldlines (lignes du monde) pour fournir ce qui peut être le théorème ultime dans Turing Machine Theory, et apparemment fournir des aperçus de l’impossibilité, l’incomplétude, les limites du calcul, et l’univers comme ordinateur, dans tous les univers possibles et tous les êtres ou mécanismes possibles, générant, entre autres, un principe d’incertitude mécanique non quantique et une preuve de monothéisme. Il existe des connexions évidentes à l’œuvre classique de Chaitin, Solomonoff, Komolgarov et Wittgenstein et à l’idée qu’aucun programme (et donc aucun dispositif) ne peut générer une séquence (ou un dispositif) avec une plus grande complexité qu’il ne possède. On pourrait dire que cet ensemble de travaux implique l’athéisme puisqu’il ne peut y avoir d’entité plus complexe que l’univers physique et du point de vue wittgensteinien, « plus complexe » n’a aucun sens (n’a pas de conditions de satisfaction, c’est-à-dire véridique ou test). Même un « Dieu » (c’est-à-dire un « dispositif » avec un temps/ espace et une énergie illimité) ne peut pas déterminer si un « nombre » donné est « aléatoire», ni trouver un certain moyen de montrer qu’une « formule » donnée, un « théorème » ou une « phrase » ou un « dispositif » (tous ces jeux de langage complexes) fait partie d’un « système » particulier. Ceux qui souhaitent un cadre complet à jour pour le comportement humain de la vue moderne de deux systemes peuvent consulter mon livre 'The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle' 2nd ed (2019). Ceux qui s’intéressent à plus de mes écrits peuvent voir 'Talking Monkeys --Philosophie, Psychologie, Science, Religion et Politique sur une planète condamnée --Articles et revues 2006-2019 2ème ed (2019) et Suicidal Utopian Delusions in the 21st Century 4th ed (2019) et autres. (shrink)

Ich habe viele kürzliche Diskussionen über die Grenzen der Berechnung und das Universum als Computer gelesen, in der Hoffnung, einige Kommentare über die erstaunliche Arbeit des Polymath Physikers und Entscheidungstheoretikers David Wolpert zu finden, aber habe kein einziges Zitat gefunden und so präsentiere ich diese sehr kurze Zusammenfassung. Wolpert bewies einige verblüffende Unmöglichkeit oder Unvollständigkeit Theoreme (1992 bis 2008-siehe arxiv dot org) über die Grenzen der Schlussfolgerung (Berechnung), die so allgemein sind, dass sie unabhängig von dem Gerät, das die Berechnung, (...) und sogar unabhängig von den Gesetzen der Physik, so dass sie für Computer, Physik und menschliches Verhalten gelten. Sie nutzen Cantors Diagonalisierung, das Lügner-Paradoxon und die Weltlinien, um das vielleicht ultimative Theorem in turing Machine Theory zu liefern, und geben scheinbar Einblicke in Unmöglichkeit, Unvollständigkeit, die Grenzen der Berechnung und das Universum als Computer, in alle möglichen Universen und alle Wesen oder Mechanismen, was unter anderem ein nicht quantenmechanisches Unsicherheitsprinzip und einen Beweis für Monotheismus erzeugt. Es gibt offensichtliche Verbindungen zum klassischen Werk von Chaitin, Solomonoff, Komolgarov und Wittgenstein und zu der Vorstellung, dass kein Programm (und damit kein Gerät) eine Sequenz (oder ein Gerät) mit größerer Komplexität erzeugen kann, als es besitzt. Man könnte sagen, dass dieses Werk den Atheismus impliziert, da es keine Entität geben kann, die komplexer ist als das physikalische Universum, und aus Wittgensteins Sicht ist "komplexer" bedeutungslos (hat keine Bedingungen der Befriedigung, d.h. Wahrheitsmacher oder Test). Selbst ein "Gott" (das heist ein "Gerät" mit unbegrenzter Zeit/Raum und Energie) kann weder bestimmen, ob eine bestimmte "Zahl" "zufällig"ist, noch einen bestimmten Weg finden, um nachzuweisen, dass eine bestimmte "Formel", "Satz" oder "Satz" oder "Gerät" (alles komplexe Sprachspiele) Teil eines bestimmten "Systems" ist. Wer aus der modernen zweisystems-Sichteinen umfassenden, aktuellen Rahmen für menschliches Verhalten wünscht, kann mein Buch "The Logical Structure of Philosophy, Psychology, Mindand Language in Ludwig Wittgenstein and John Searle' 2nd ed (2019) konsultieren. Diejenigen,die sich für mehr meiner Schriften interessieren, können Talking Monkeys--Philosophie, Psychologie, Wissenschaft, Religion und Politik auf einem verdammten Planeten --Artikel und Rezensionen 2006-2019 2nd ed (2019) und Suicidal Utopian Delusions in the 21st Century 4th ed (2019) und andere sehen. (shrink)