Why are we so many? Or, in other words, Why is our species so successful? The ultimate cause of our success as species is that we, Homo sapiens, are the first and the only Turing complete species. Turing completeness is the capacity of some hardware to compute by software whatever hardware can compute. To reach the answer, I propose to see evolution and computing from the problem solving point of view. Then, solving more problems is evolutionarily better, (...) computing is for solving problems, and software is much cheaper than hardware, resulting that Turing completeness is evolutionarily disruptive. This conclusion, together with the fact that we are the only Turing complete species, is the reason that explains why we are so many. Most of our unique cognitive characteristics as humans can be derived from being Turing complete, as for example our complete language and our problem solving creativity. (shrink)

Following Post program, we will propose a linguistic and empirical interpretation of Gödel’s incompleteness theorem and related ones on unsolvability by Church and Turing. All these theorems use the diagonal argument by Cantor in order to find limitations in finitary systems, as human language, which can make “infinite use of finite means”. The linguistic version of the incompleteness theorem says that every Turing complete language is Gödel incomplete. We conclude that the incompleteness and unsolvability theorems find limitations in (...) our finitary tool, which is our complete language. (shrink)

An abstract machine having a tape head that can be advanced in 0 to 0x7FFFFFFF increments an unlimited number of times specifies a model of computation that has access to unlimited memory. The technical name for memory addressing based on displacement from the current memory address is relative addressing.

Philosophical questions about minds and computation need to focus squarely on the mathematical theory of Turing machines (TM's). Surrogate TM's such as computers or formal systems lack abilities that make Turing machines promising candidates for possessors of minds. Computers are only universal Turing machines (UTM's)—a conspicuous but unrepresentative subclass of TM. Formal systems are only static TM's, which do not receive inputs from external sources. The theory of TM computation clearly exposes the failings of two prominent critiques, (...) Searle's Chinese room (1980) and arguments from Gödel's Incompleteness theorems (e.g., Lucas, 1961; Penrose, 1989), both of which fall short of addressing the complete TM model. Both UTM-computers and formal systems provide an unsound basis for debate. In particular, their special natures easily foster the misconception that computation entails intrinsically meaningless symbol manipulation. This common view is incorrect with respect to full-fledged TM's, which can process inputs non-formally, i.e., in a subjective and dynamically evolving fashion. To avoid a distorted understanding of the theory of computation, philosophical judgments and discussions should be grounded firmly upon the complete Turing machine model, the proper model for real computers. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. (...) A solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (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)

It is commonly thought that Impossibility, Incompleteness, Paraconsistency, Undecidability, Randomness, Computability, Paradox, Uncertainty and the Limits of Reason are disparate scientific physical or mathematical issues having little or nothing in common. I suggest that they are largely standard philosophical problems (i.e., language games) which were mostly resolved by Wittgenstein over 80years ago. -/- “What we are ‘tempted to say’ in such a case is, of course, not philosophy, but it is its raw material. Thus, for example, what a mathematician is (...) inclined to say about the objectivity and reality of mathematical facts, is not a philosophy of mathematics, but something for philosophical treatment.” Wittgenstein PI 234 -/- "Philosophers constantly see the method of science before their eyes and are irresistibly tempted to ask and answer questions in the way science does. This tendency is the real source of metaphysics and leads the philosopher into complete darkness." Wittgenstein -/- I provide a brief summary of some of the major findings of two of the most eminent students of behavior of modern times, Ludwig Wittgenstein and John Searle, on the logical structure of intentionality (mind, language, behavior), taking as my starting point Wittgenstein’s fundamental discovery –that all truly ‘philosophical’ problems are the same—confusions about how to use language in a particular context, and so all solutions are the same—looking at how language can be used in the context at issue so that its truth conditions (Conditions of Satisfaction or COS) are clear. The basic problem is that one can say anything, but one cannot mean (state clear COS for) any arbitrary utterance and meaning is only possible in a very specific context. -/- I dissect some writings of a few of the major commentators on these issues from a Wittgensteinian viewpoint in the framework of the modern perspective of the two systems of thought (popularized as ‘thinking fast, thinking slow’), employing a new table of intentionality and new dual systems nomenclature. I show that this is a powerful heuristic for describing the true nature of these putative scientific, physical or mathematical issues which are really best approached as standard philosophical problems of how language is to be used (language games in Wittgenstein’s terminology). -/- It is my contention that the table of intentionality (rationality, mind, thought, language, personality etc.) that features prominently here describes more or less accurately, or at least serves as an heuristic for, how we think and behave, and so it encompasses not merely philosophy and psychology, but everything else (history, literature, mathematics, politics etc.). Note especially that intentionality and rationality as I (along with Searle, Wittgenstein and others) view it, includes both conscious deliberative linguistic System 2 and unconscious automated prelinguistic System 1 actions or reflexes. (shrink)

There is a long tradition of logic, from Aristotle to Gödel, of understanding a proof from the concepts of necessity and causality. Gödel's attempts to define provability in terms of necessity led him to the distinction of formal and absolute (abstract) provability. Turing's definition of mechanical procedure by means of a Turing machine (TM) and Gödel's definition of a formal system as a mechanical procedure for producing formulas prompt us to understand formal provability as a mechanical causality. We (...) propose a formalism which makes explicit the mechanical causal nature of a TM's work. We claim that Gödel's axiomatised ontotheology and his ontological proof give a clue for the understanding of the concept of absolute provability and the pattern of the corresponding absolute completeness proof, respectively. (shrink)

For Putnam in "Representation and Reality", there cannot be any intentional science, thus dooming cognitive science. His argument is that intentional concepts are functional, and that functionalism cannot explain anything because "everything has every functional organization", providing a proof. Analyzing his proof, we find that Putnam is assuming an ideal interpreting subject who can compute effortlessly and who is not intentional. But the subject doing science is a human being, and we are not that way. Therefore, in order to save (...) cognitive science, we propose to replace the ideal subject with a real and intentional human subject, and we propose to model intentionality by using a problem theory which is an intuitionist set theory where the resolving subject is a computing device. We are intentional because we are living beings, where life is the intention of not to die, so we are embodied intentions designed by evolution. We are real and then we have to compute our resolutions to the survival problem, and fortuitously we are computationally Turing complete, so our language is complete and then full and self referable. In summary, evolutionary subjectivism modeled as problem solving by computing should save cognitive science. Or, in other words, we are proposing to update Kant with Darwin and Turing. (shrink)

The Turing machine halting problem can be explained by several factors, including arithmetic logic irreversibility and memory erasure, which contribute to computational uncertainty due to information loss during computation. Essentially, this means that an algorithm can only preserve information about an input, rather than generate new information. This uncertainty arises from characteristics such as arithmetic logical irreversibility, Landauer's principle, and memory erasure, which ultimately lead to a loss of information and an increase in entropy. To measure this uncertainty and (...) loss of information, the concept of arithmetic logical entropy can be used. The Turing machine and its equivalent, general recursive functions can be understood through the λ calculus and the Turing/Church thesis. However, there are certain recursive functions that cannot be fully understood or predicted by other algorithms due to the loss of information during logical-arithmetic operations. In other words, the behaviour of these functions cannot be completely determined at every stage of the computation due to a lack of information in their definition. While there are some cases where the behaviour of recursive functions is highly predictable, the lack of information in most cases makes it impossible for algorithms to determine if a program will halt or not. This inability to predict the outcome of the computation is the essence of the halting problem of the Turing machine. Even in cases where more information is available about the program, it is still difficult to determine with certainty if the program will halt or not. This also highlights the importance of the Turing oracle machine, which introduces information from outside the computation to compensate for the lack of information and ultimately decide the result of the computation. (shrink)

Informatics is generally understood as a “new technology” and is therewith discussed according to technological aspects such as speed, data retrieval, information control and so on. Its widespread use from home appliances to enterprises and universities is not the result of a clear-cut analysis of its inner possibilities but is rather dependent on all sorts of ideological promises of unlimited progress. We will discuss the theoretical definition of informatics proposed in 1936 by Alan Turing in order to show that (...) it should be taken as final and complete. This definition has no relation to the technology because Turing defines computers as doing the work of solving problems with numbers. This formal definition implies nonetheless a relation to the non-formalized elements around informatics, which we shall discuss through the Greek notion of téchne. (shrink)

This is a non-technical version of "The Decision Problem for Effective Procedures." The “somewhat vague, intuitive” notion from computability theory of an effective procedure (method) or algorithm can be fairly precisely defined, even if it does not have a purely mathematical definition—and even if (as many have asserted) for that reason, the Church–Turing thesis (that the effectively calculable functions on natural numbers are exactly the general recursive functions), cannot be proved. However, it is logically provable from the notion of (...) an effective procedure--without reliance on any (partially) mathematical thesis or conjecture concerning effective procedures, such as the Church–Turing thesis--that the class of effective procedures is undecidable, i.e., that no effective procedure is possible for ascertaining whether a given procedure is effective. The proof does not appeal to a precise definition of ‘effective procedure’. Instead, it relies solely and entirely on a basic grasp of the intuitive notion of such a procedure. Though this undecidability result itself is not surprising, it is also not without significance. It has the consequence, for example, that the solution to a decision problem, if it is to be complete, must be accompanied by a separate argument that the proposed ascertainment procedure is, in fact, a decision procedure, i.e., effective—for example, that it invariably terminates with the correct verdict. (shrink)

A practical viewpoint links reality, representation, and language to calculation by the concept of Turing (1936) machine being the mathematical model of our computers. After the Gödel incompleteness theorems (1931) or the insolvability of the so-called halting problem (Turing 1936; Church 1936) as to a classical machine of Turing, one of the simplest hypotheses is completeness to be suggested for two ones. That is consistent with the provability of completeness by means of two independent Peano (...) arithmetics discussed in Section I. Many modifications of Turing machines cum quantum ones are researched in Section II for the Halting problem and completeness, and the model of two independent Turing machines seems to generalize them. Then, that pair can be postulated as the formal definition of reality therefore being complete unlike any of them standalone, remaining incomplete without its complementary counterpart. Representation is formal defined as a one-to-one mapping between the two Turing machines, and the set of all those mappings can be considered as “language” therefore including metaphors as mappings different than representation. Section III investigates that formal relation of “reality”, “representation”, and “language” modeled by (at least two) Turing machines. The independence of (two) Turing machines is interpreted by means of game theory and especially of the Nash equilibrium in Section IV. Choice and information as the quantity of choices are involved. That approach seems to be equivalent to that based on set theory and the concept of actual infinity in mathematics and allowing of practical implementations. (shrink)

Throughout this paper, we are trying to show how and why our Mathematical frame-work seems inappropriate to solve problems in Theory of Computation. More exactly, the concept of turning back in time in paradoxes causes inconsistency in modeling of the concept of Time in some semantic situations. As we see in the first chapter, by introducing a version of “Unexpected Hanging Paradox”,first we attempt to open a new explanation for some paradoxes. In the second step, by applying this paradox, it (...) is demonstrated that any formalized system for the Theory of Computation based on Classical Logic and Turing Model of Computation leads us to a contradiction. We conclude that our mathematical frame work is inappropriate for Theory of Computation. Furthermore, the result provides us a reason that many problems in Complexity Theory resist to be solved.(This work is completed in 2017 -5- 2, it is in vixra in 2017-5-14, presented in Unilog 2018, Vichy). (shrink)

An introductory textbook on metalogic. It covers naive set theory, first-order logic, sequent calculus and natural deduction, the completeness, compactness, and Löwenheim-Skolem theorems, Turing machines, and the undecidability of the halting problem and of first-order logic. The audience is undergraduate students with some background in formal logic.

The “somewhat vague, intuitive” notion from computability theory of an effective procedure (method) or algorithm can be fairly precisely defined even if it is not sufficiently formal and precise to belong to mathematics proper (in a narrow sense)—and even if (as many have asserted) for that reason the Church–Turing thesis is unprovable. It is proved logically that the class of effective procedures is not decidable, i.e., that no effective procedure is possible for ascertaining whether a given procedure is effective. (...) This undecidability result is proved directly from the notion itself of an effective procedure, without reliance on any (partly) mathematical lemma, conjecture, or thesis invoking recursiveness or Turing-computability. In fact, there is no reliance on anything very mathematical. The proof does not even appeal to a precise definition of ‘effective procedure’. Instead, it relies solely and entirely on a basic grasp of the intuitive notion of an effective procedure. Though the result that effectiveness is undecidable is not surprising, it is also not without significance. It has the consequence, for example, that the solution to a decision problem, if it is to be complete, must be accompanied by a separate argument that the proposed ascertainment procedure invariably terminates with the correct verdict. (shrink)

Classical interpretations of Goedels formal reasoning, and of his conclusions, implicitly imply that mathematical languages are essentially incomplete, in the sense that the truth of some arithmetical propositions of any formal mathematical language, under any interpretation, is, both, non-algorithmic, and essentially unverifiable. However, a language of general, scientific, discourse, which intends to mathematically express, and unambiguously communicate, intuitive concepts that correspond to scientific investigations, cannot allow its mathematical propositions to be interpreted ambiguously. Such a language must, therefore, define mathematical truth (...) verifiably. We consider a constructive interpretation of classical, Tarskian, truth, and of Goedel's reasoning, under which any formal system of Peano Arithmetic---classically accepted as the foundation of all our mathematical Languages---is verifiably complete in the above sense. We show how some paradoxical concepts of Quantum mechanics can, then, be expressed, and interpreted, naturally under a constructive definition of mathematical truth. (shrink)

In this paper I begin by extending two results of Solovay; the first characterizes the possible Turing degrees of models of True Arithmetic (TA), the complete first-order theory of the standard model of PA, while the second characterizes the possible Turing degrees of arbitrary completions of P. I extend these two results to characterize the possible Turing degrees of m-diagrams of models of TA and of arbitrary complete extensions of PA. I next give a construction showing that (...) the conditions Solovay identified for his characterization of degrees of models of arbitrary completions of PA cannot be dropped (I showed that these conditions cannot be simplified in the paper. (shrink)

These remarks take up the reflexive problematics of Being and Nothingness and related texts from a metalogical perspective. A mutually illuminating translation is posited between, on the one hand, Sartre’s theory of pure reflection, the linchpin of the works of Sartre’s early period and the site of their greatest difficulties, and, on the other hand, the quasi-formalism of diagonalization, the engine of the classical theorems of Cantor, Gödel, Tarski, Turing, etc. Surprisingly, the dialectic of mathematical logic from its inception (...) through the discovery of the diagonal theorems can be recognized as a particularly clear instance of the drama of reflection according to Sartre, especially in the positing and overcoming of its proper value-ideal, viz. the synthesis of consistency and completeness. Conversely, this translation solves a number of systematic problems about pure reflection’s relations to accessory reflection, phenomenological reflection, pre-reflective self-consciousness, conversion, and value. Negative foundations, the metaphysical position emerging from this translation between existential philosophy and metalogic, concurs by different paths with Badiou’s Being and Event in rejecting both ontotheological foundationalisms and constructivist antifoundationalisms. (shrink)

In this paper, I shall review the reasons that let Putnam to propose functionalism and the reasons that subsequently led him to abandon it. I would like to discuss Putnam's views belonging to early Putnam and later Putnam. First, let us focus on early Putnam. Early Putnam tries to show the possibility of robot consciousness. As a functionalist, Putnam shows that the human being is an autonomous: that is, human mind is a computing machine. Later, he changes his position to (...) demonstrate not just his intellectual confidence, but also the virtue of seeing the bigger picture. Later Putnam realized this because this sort of utopianism is an illustration of what is called ‘scientism’. It is based on speculations regarding scientific possibilities. The problem is that it is completely unclear just what possibility has been envisaged when one speaks of robotic consciousness. While arguing against Turing machine, the later Putnam said that pessimism about the success of artificial intelligence (AI) in simulating human intelligence amounts to pessimism about possibility of describing the functioning of the brain. He raised the question: what connection is there between simulating intelligence and describing the brain? Even if the computer model of the brain is correct, it does not follow that AI will succeed. Simulation is just simulation, it does not mean exact replications of human mind. (shrink)

Scroggs's theorem on the extensions of S5 is an early landmark in the modern mathematical studies of modal logics. From it, we know that the lattice of normal extensions of S5 is isomorphic to the inverse order of the natural numbers with infinity and that all extensions of S5 are in fact normal. In this paper, we consider extending Scroggs's theorem to modal logics with propositional quantifiers governed by the axioms and rules analogous to the usual ones for ordinary quantifiers. (...) We call them Π-logics. Taking S5Π, the smallest normal Π-logic extending S5, as the natural counterpart to S5 in Scroggs's theorem, we show that all normal Π-logics extending S5Π are complete with respect to their complete simple S5 algebras, that they form a lattice that is isomorphic to the lattice of the open sets of the disjoint union of two copies of the one-point compactification of N, that they have arbitrarily high Turing-degrees, and that there are non-normal Π-logics extending S5Π. (shrink)

Before commenting in detail on Making the Social World (MSW) I will first offer some comments on philosophy (descriptive psychology) and its relationship to contemporary psychological research as exemplified in the works of Searle (S) and Wittgenstein (W), since I feel that this is the best way to place Searle or any commentator on behavior, in proper perspective. It will help greatly to see my reviews of PNC, TLP, PI, OC,TARW and other books by these two geniuses of descriptive psychology. (...) -/- S makes no reference to W’s prescient statement of mind as mechanism in TLP, and his destruction of it in his later work. Since W, S has become the principal deconstructor of these mechanical views of behavior, and the most important descriptive psychologist (philosopher), but does not realize how completely W anticipated him nor, by and large, do others (but see the many papers and books of Proudfoot and Copeland on W, Turing and AI). S’s work is vastly easier to follow than W’s, and though there is some jargon, it is mostly spectacularly clear if you approach it from the right direction. See my reviews of W S and other books for more details. -/- Overall, MSW is a good summary of the many substantial advances over Wittgenstein resulting from S’s half century of work, but in my view, W still is unequaled for basic psychology once you grasp what he is saying (see my reviews). Ideally they should be read together: Searle for the clear coherent prose and generalizations on the operation of S2/S3, illustrated with W’s perspicacious examples of the operation of S1/S2, and his brilliant aphorisms. If I were much younger I would write a book doing exactly that. -/- Those wishing a comprehensive up to date account of Wittgenstein, Searle and their analysis of behavior from the modern two systems view may consult my article The Logical Structure of Philosophy, Psychology, Mind and Language as Revealed in Wittgenstein and Searle (2016). Those interested in all my writings in their most recent versions may download from this site my e-book ‘Philosophy, Human Nature and the Collapse of Civilization Michael Starks (2016)- Articles and Reviews 2006-2016’ by Michael Starks First Ed. 662p (2016). -/- All of my papers and books have now been published in revised versions both in ebooks and in printed books. -/- Talking Monkeys: Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet - Articles and Reviews 2006-2017 (2017) https://www.amazon.com/dp/B071HVC7YP. -/- The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle--Articles and Reviews 2006-2016 (2017) https://www.amazon.com/dp/B071P1RP1B. -/- Suicidal Utopian Delusions in the 21st century: Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2017 (2017) https://www.amazon.com/dp/B0711R5LGX . (shrink)

Should models of consciousness be detailed _mechanistic_ models of particular types of systems, or should they be _minimal_ models that abstract away from the underlying mechanistic details and provide generalisations? Detailed mechanistic models may afford a complete and precise account of consciousness in human beings and other, physiologically similar mammals. But they do not provide a good model of consciousness in other animals, such as non-vertebrates, let alone artificial systems. Minimal models can be applicable to a wide range of different (...) conscious systems. But do they provide genuine explanations that are autonomous from explanations by detailed mechanistic models? This paper provides a taxonomy of minimal models and measures of consciousness, clarifies their relation to detailed mechanistic models, and highlights benefits of different minimalist approaches to consciousness. (shrink)

Applications and devices of artificial intelligence are increasingly common in the healthcare field. Robots fulfilling some caregiving functions are not a distant future. In this scenario, we must ask ourselves if it is possible for machines to care to the extent of completely replacing human care and if such replacement, if possible, is desirable. In this paper, I argue that caregiving requires know-how permeated by affectivity that is far from being achieved by currently available machines. I also maintain that the (...) complete substitution of machine caregiving for human caregiving is not desirable because caregiving requires genuine human connection. (shrink)

Avant de commenter en détail sur Making the Social World (MSW) Je vais d’abord offrir quelques commentaires sur la philosophie (psychologie descriptive) et son rapport à la recherche psychologique contemporaine comme illustré dans les œuvres de Searle (S) et Wittgenstein (W), puisque je pense que c’est la meilleure façon de placer Searle ou tout commentateur sur le comportement, en perspective appropriée. Il aidera grandement à voir mes critiques de PNC, TLP, PI, OC, TARW et d’autres livres par ces deux génies (...) de la psychologie descriptive. S ne fait aucune référence à la déclaration prémonitoire de L’esprit de W comme mécanisme dans TLP, et sa destruction de celui-ci dans son travail ultérieur. Depuis W, S est devenu le principal déconstructeur de ces vues mécaniques du comportement, et le psychologue descriptif le plus important (philosophe), mais ne se rend pas compte à quel point W l’a anticipé complètement ni, dans l’ensemble, faire d’autres (mais voir les nombreux papiers et livres de Proudfoot et Copeland sur W, Turing et AI). Le travail de S est beaucoup plus facile à suivre que celui de W, et bien qu’il y ait un certain jargon, il est surtout spectaculairement clair si vous l’approchez de la bonne direction. Voir mes commentaires de W S et d’autres livres pour plus de détails. Dans l’ensemble, MSW est un bon résumé des nombreux progrès substantiels sur Wittgenstein résultant du demi-siècle de travail de S, mais à mon avis, W est toujours inégalé pour la psychologie de base une fois que vous saisissez ce qu’il dit (voir mes commentaires). Idéalement, ils devraient être lus ensemble: Searle pour la prose clairement cohérente et les généralisations sur le fonctionnement de S2/S3, illustré avec les exemples perspicaces de W de l’opération de S1/S2, et ses aphorismes brillants. Si j’étais beaucoup plus jeune, j’écrirais un livre faisant exactement cela. Ceux qui souhaitent un cadre complet à jour pour le comportement humain de la vue moderne de deuxemssyst 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 3e ed (2019) et Suicidal Utopian Delusions in the 21st Century 4th ed (2019) et autres. (shrink)

Objective: The objective of this study was to evaluate the clinical efficacy and appropriateness of colistin therapy in patients with hematological malignancies. -/- Methods: Age, gender, type of hematologic malignancy, and potential carbapenem-resistant microorganism risk factors were all noted in this retrospective study. In empirical and agent-specific treatment groups, differences in demographic features, risk factors, treatment responses, and side effects were compared. -/- Results: Sixty-three patients were included, 54% were male, and the median age was 49. In the last three (...) months, the hospitalization rate history was 68%, and four patients had a hospitalization history in the ICU. Carbapenem-resistant K. pneumoniae colonization was present in 22 patients (35%). Gram-negative microorganisms were isolated in 34 patients (54%). The carbapenem, quinolone, and colistin resistance rates were 82%, 76%, and 4% respectively. Clinical and microbiological response rates were 60% and 69%. 7 and 28-day mortality rates were 17% and 35%. There was no significant difference in demographic data and comorbidities in empirical (n=48) and agent-specific (n=15) treatment groups. The rate of carbapenem and glycopeptide treatments before colistin was higher in the empirical treatment group (p = 0.004; p = 0.001). The rate of starting combined antibiotics was higher in the empirical treatment group (p = 0.016). Two of the patients developed renal failure in the first week after treatment. -/- Conclusion: The use of empirical colistin may be unavoidable given the risk considerations. Shortly, colistin-resistant strains may also be a factor affecting treatment success negatively. (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)

This volume celebrates the various facets of Alan Turing (1912–1954), the British mathematician and computing pioneer, widely considered as the father of computer science. It is aimed at the general reader, with additional notes and references for those who wish to explore the life and work of Turing more deeply. -/- The book is divided into eight parts, covering different aspects of Turing’s life and work. -/- Part I presents various biographical aspects of Turing, some from (...) a personal point of view. -/- Part II presents Turing’s universal machine (now known as a Turing machine), which provides a theoretical framework for reasoning about computation. His 1936 paper on this subject is widely seen as providing the starting point for the field of theoretical computer science. -/- Part III presents Turing’s working on codebreaking during World War II. While the War was a disastrous interlude for many, for Turing it provided a nationally important outlet for his creative genius. It is not an overstatement to say that without Turing, the War would probably have lasted longer, and may even have been lost by the Allies. The sensitive nature of Turning’s wartime work meant that much of this has been revealed only relatively recently. -/- Part IV presents Turing’s post-War work on computing, both at the National Physical Laboratory and at the University of Manchester. He made contributions to both hardware design, through the ACE computer at the NPL, and software, especially at Manchester. Part V covers Turing’s contribution to machine intelligence (now known as Artificial Intelligence or AI). Although Turing did not coin the term, he can be considered a founder of this field which is still active today, authoring a seminal paper in 1950. -/- Part VI covers morphogenesis, Turing’s last major scientific contribution, on the generation of seemingly random patterns in biology and on the mathematics behind such patterns. Interest in this area has increased rapidly in recent times in the field of bioinformatics, with Turing’s 1952 paper on this subject being frequently cited. -/- Part VII presents some of Turing’s mathematical influences and achievements. Turing was remarkably free of external influences, with few co-authors – Max Newman was an exception and acted as a mathematical mentor in both Cambridge and Manchester. -/- Part VIII considers Turing in a wider context, including his influence and legacy to science and in the public consciousness. -/- Reflecting Turing’s wide influence, the book includes contributions by authors from a wide variety of backgrounds. Contemporaries provide reminiscences, while there are perspectives by philosophers, mathematicians, computer scientists, historians of science, and museum curators. Some of the contributors gave presentations at Turing Centenary meetings in 2012 in Bletchley Park, King’s College Cambridge, and Oxford University, and several of the chapters in this volume are based on those presentations – some through transcription of the original talks, especially for Turing’s contemporaries, now aged in their 90s. Sadly, some contributors died before the publication of this book, hence its dedication to them. -/- For those interested in personal recollections, Chapters 2, 3, 11, 12, 16, 17, and 36 will be of interest. For philosophical aspects of Turing’s work, see Chapters 6, 7, 26–31, and 41. Mathematical perspectives can be found in Chapters 35 and 37–39. Historical perspectives can be found in Chapters 4, 8, 9, 10, 13–15, 18, 19, 21–25, 34, and 40. With respect to Turing’s body of work, the treatment in Parts II–VI is broadly chronological. We have attempted to be comprehensive with respect to all the important aspects of Turing’s achievements, and the book can be read cover to cover, or the chapters can be tackled individually if desired. There are cross-references between chapters where appropriate, and some chapters will inevitably overlap. -/- We hope that you enjoy this volume as part of your library and that you will dip into it whenever you wish to enter the multifaceted world of Alan Turing. (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)

In this article, I outline the three main philosophical lessons that we may learn from Turing’s work, and how they lead to a new philosophy of information. After a brief introduction, I discuss his work on the method of levels of abstraction (LoA), and his insistence that questions could be meaningfully asked only by specifying the correct LoA. I then look at his second lesson, about the sort of philosophical questions that seem to be most pressing today. Finally, I (...) focus on the third lesson, concerning the new philosophical anthropology that owes so much to Turing’s work. I then show how the lessons are learned by the philosophy of information. In the conclusion, I draw a general synthesis of the points made, in view of the development of the philosophy of information itself as a continuation of Turing’s work. (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)

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)

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 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)

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)

Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There are hopes (...) it can be adapted to other contexts besides that game show, in the role of a collaborator of, rather than a competitor to, humans. Another, different, research project—an artificial intelligence program put into operation in 2010—is the machine learning program NELL (Never Ending Language Learning), which continuously ‘learns’ by ‘reading’ massive amounts of material on millions of web pages. Both of these recent endeavors in artificial intelligence rely to some extent on the integration of human guidance and feedback at various points in the machine’s learning process. In this paper, I examine Turing’s remarks on the development of intelligence used in various kinds of search, in light of the experience gained to date on these projects. (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)

Turing used the expression “emotional” in three distinct ways: to state his philosophical theory of the concept of intelligence, to classify arguments for and against the possibility of machine intelligence, and to describe the education of a “child machine”. The remarks on emotion include several of the most important philosophical claims. This paper analyses these remarks and their significance for current research in Artificial Intelligence.

Amodal completion is the representation of occluded parts of perceived objects. We argue for the following three claims: First, at least some amodal completion-involved experiences can ground knowledge about the occluded portions of perceived objects. Second, at least some instances of amodal completion-grounded knowledge are not sensitive, that is, it is not the case that in the nearest worlds in which the relevant claim is false, that claim is not believed true. Third, at least some instances of amodal completion-grounded knowledge (...) are not safe, that is, it is not the case that in all or nearly all near worlds where the relevant claim is believed true, that claim is in fact true. Thus, certain instances of amodal completion-grounded knowledge refute both the view that knowledge is necessarily sensitive and the view that knowledge is necessarily safe. (shrink)

Due to his significant role in the development of computer technology and the discipline of artificial intelligence, Alan Turing has supposedly subscribed to the theory of mind that has been greatly inspired by the power of the said technology which has eventually become the dominant framework for current researches in artificial intelligence and cognitive science, namely, computationalism or the computational theory of mind. In this essay, I challenge this supposition. In particular, I will try to show that there is (...) no evidence in Turing’s two seminal works that supports such a supposition. His 1936 paper is all about the notion of computation or computability as it applies to mathematical functions and not to the nature or workings of intelligence. On the other hand, while his 1950 work is about intelligence, it is, however, particularly concerned with the problem of whether intelligence can be attributed to computing machines and not of whether computationality can be attributed to human intelligence or to intelligence in general. (shrink)

Amodal completion is usually characterized as the representation of those parts of the perceived object that we get no sensory stimulation from. In the case of the visual sense modality, for example, amodal completion is the representation of occluded parts of objects we see. I argue that relationalism about perception, the view that perceptual experience is constituted by the relation to the perceived object, cannot give a coherent account of amodal completion. The relationalist has two options: construe the perceptual relation (...) as the relation to the entire perceived object or as the relation to the unoccluded parts of the perceived object. I argue that neither of these options are viable. (shrink)

Response to Gover (2015) on Trogdon and Livingston (2015) on artwork completion.

An Evaluation of the 2008 Loebner Contest.

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 (...) class='Hi'>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)

This is a Turing Machine which computes the exponential function f(x,y) = xˆy. Instructions format and operation of this machine are intended to best reflect the basic conditions outlined by Alan Turing in his On Computable Numbers, with an Application to the Entscheidungsproblem (1936), using the simplest single-tape and single-symbol version, in essence due to Kleene (1952) and Carnielli & Epstein (2008). This machine is composed by four basic task machines: one which checks if exponent y is zero, (...) a second which checks if base x is zero, a third that is able to copy the base, and a fourth able to multiply multiple factors (in this case, factors will be all equal). They were conveniently separated in order to ease the reader's task to understand each step of its operation. We adopt the convention that a number n is represented by a string of n+1 symbols "1". Thus, an entry (x, y) will be represented by two respective strings of x+1 and y+1 symbols "1", separated by a single "0" (or a blank), and as an output, this machine will generate a string of (xˆy)+1 symbols "1". Some of the instructions are followed by a brief description of what's going on. (shrink)

In their recent book Every Thing Must Go, Ladyman and Ross claim: (i) Physics is analytically complete since it is the only science that cannot be left incomplete. (ii) There might not be an ontologically fundamental level. (iii) We should not admit anything into our ontology unless it has explanatory and predictive utility. In this discussion note I aim to show that the ontological commitment in implies that the completeness of no science can be achieved where no fundamental level (...) exists. Therefore, if claim requires a science to actually be complete in order to be considered as physics,, and if Ladyman and Ross's “tentative metaphysical hypothesis ... that there is no fundamental level” is true,, then there simply is no physics. Ladyman and Ross can, however, avoid this unwanted result if they merely require physics to ever strive for completeness rather than to already be complete. (shrink)

Consider the following. The first is a one-premise argument; the second has two premises. The question sign marks the conclusions as such. -/- Matthew, Mark, Luke, and John wrote Greek. ? Every evangelist wrote Greek. -/- Matthew, Mark, Luke, and John wrote Greek. Every evangelist is Matthew, Mark, Luke, or John. ? Every evangelist wrote Greek. -/- The above pair of premise-conclusion arguments is of a sort familiar to logicians and philosophers of science. In each case the first premise is (...) logically equivalent to the set of four atomic propositions: “Matthew wrote Greek”, “Mark wrote Greek”, “Luke wrote Greek”, and “John wrote Greek”. The universe of discourse is the set of evangelists. We presuppose standard first-order logic. -/- As many logic texts teach, the first of these two premise-conclusion arguments—sometimes called a complete enumerative induction— is invalid in the sense that its conclusion does not follow from its premises. To get a counterargument, replace ‘Matthew’, ‘Mark’, ‘Luke’, and ‘John’ by ‘two’,’four’, ‘six’ and ‘eight’; replace ‘wrote Greek’ by ‘are even’; and replace ‘evangelist’ by ‘number’. This replacement converts the first argument into one having true premises and false conclusion. -/- But the same replacement performed on the second argument does no such thing: it converts the second premise into the falsehood “Every number is two, four, six, or eight”. As many logic texts teach, there is no replacement that converts the second argument into one with all true premises and false conclusion. The second is valid; its conclusion is deducible from its two premises using an instructive natural deduction. -/- This paper “does the math” behind the above examples. The theorem could be stated informally: the above examples are typical. (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)