Recent work on hypercomputation has raised new objections against the Church–Turing Thesis. In this paper, I focus on the challenge posed by a particular kind of hypercomputer, namely, SAD computers. I first consider deterministic and probabilistic barriers to the physical possibility of SAD computation. These suggest several ways to defend a Physical version of the Church–Turing Thesis. I then argue against Hogarth's analogy between non-Turing computability and non-Euclidean geometry, showing that it is a non-sequitur. I conclude (...) that the Effective version of the Church–Turing Thesis is unaffected by SAD computation. (shrink)

Recent advances in neuroscience lead to a wider realm for philosophy to include the science of the Darwinian-evolved computational brain, our inner world producing organ, a non-recursive super- Turing machine combining 100B synapsing-neuron DNA-computers based on the genetic code. The whole system is a logos machine offering a world map for global context, essential for our intentional grasp of opportunities. We start from the observable contrast between the chaotic universe vs. our orderly inner world, the noumenal cosmos. So far, (...) philosophy has been rehearsing our thoughts, our human-internal world, a grand painting of the outer world, how we comprehend subjectively our experience, worked up by the logos machine, but now we seek a wider horizon, how humans understand the world thanks to Darwinian evolution to adapt in response to the metaphysical gap, the chasm between the human animal and its environment, shaping the organism so it can deal with its variable world. This new horizon embraces global context coded in neural structures that support the noumenal cosmos, our inner mental world, for us as denizens of the outer environment. Kant’s inner and outer senses are fundamental ingredients of scientific philosophy. Several sections devoted to Heidegger, his lizard debunked, but his version of the metaphysical gap & his doctrine of the logos praised. Rorty and others of the behaviorist school discussed also. (shrink)

The Turing machine is one of the simple abstract computational devices that can be used to investigate the limits of computability. In this paper, they are considered from several points of view that emphasize the importance and the relativity of mathematical languages used to describe the Turing machines. A deep investigation is performed on the interrelations between mechanical computations and their mathematical descriptions emerging when a human (the researcher) starts to describe a Turing machine (the object of (...) the study) by different mathematical languages (the instruments of investigation). Together with traditional mathematical languages using such concepts as ‘enumerable sets’ and ‘continuum’ a new computational methodology allowing one to measure the number of elements of different infinite sets is used in this paper. It is shown how mathematical languages used to describe the machines limit our possibilities to observe them. In particular, notions of observable deterministic and non-deterministic Turing machines are introduced and conditions ensuring that the latter can be simulated by the former are established. (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)

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)

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.org) on the limits to inference (computation) that are so general they are independent of the device doing the (...) class='Hi'>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. (shrink)

Ho letto molte recenti discussioni sui limiti del calcolo e dell'universo come computer, sperando di trovare alcuni commenti sull'incredibile lavoro del fisico polimatematico e del teorista delle decisioni David Wolpert, ma non ho trovato una sola citazione e quindi presento questo brevissimo riassunto. Wolpert si dimostrò una straordinaria impossibilità o incompletezza teoremi (1992-2008-see arxiv dot org) sui limiti dell'inferenza (calcolo) che sono così generali che sono indipendenti dal dispositivo che fa il calcolo, e anche indipendenti dalle leggi della fisica, in (...) modo che si applichino tra computer, fisica e comportamento umano. Fanno uso della diagonalizzazione di Cantor, del paradosso bugiardo e delle linee del mondo per fornire quello che potrebbe essere il teorema definitivo nella Teoria delle Macchine di Turing, e apparentemente forniscono intuizioni sull'impossibilità, l'incompletezza, i limiti del calcolo, e l'universo come computer, in tutti gli universi possibili e in tutti gli esseri o meccanismi, generando, tra le altre cose, un principio di incertezza meccanica non quantistica e una prova di monoteismo. Ci sono evidenti connessioni con l'opera classica di Chaitin, Solomonoff, Komolgarov e Wittgenstein e alla nozione che nessun programma (e quindi nessun dispositivo) può generare una sequenza (o dispositivo) con maggiore complessità di quella che possiede. Si potrebbe dire che questo corpo di lavoro implica l'ateismo poiché non può esserci alcuna entità più complessa dell'universo fisico e dal punto di vista della Wittgensteinian, "più complesso" non ha senso (non ha condizioni di soddisfazione, cioè, creatore di verità o test). Anche un 'Dio' (cioè un 'dispositivo'con tempo/spazio illimitato ed energia) non può, determinare se un dato "numero" è 'casuale', né trovare un modo per dimostrare che una determinata "formula", "teorema" o "sentenza" o "dispositivo" (tutti questi sono giochi linguistici complessi) fa parte di un particolare "sistema". Coloro che desiderano un quadro aggiornato completo per il comportamento umano dalla moderna vista a due systems possono consultare il mio libro 'La struttura logica dellafilosofia, psicologia, Mind e il linguaggio in Ludwig Wittgenstein e John Searle' 2nd ed (2019). Coloro che sono interessati a più dei miei scritti possono vedere 'TalkingMonkeys--Filosofia, Psicologia, Scienza, Religione e Politica su un Pianeta Condannato--Articoli e Recensioni 2006-2019nd ed (2019) e Suicidal Utopian Delusions nel 21st Century 4th ed (2019) . (shrink)

Any computer can create a model of reality. The hypothesis that quantum computer can generate such a model designated as quantum, which coincides with the modeled reality, is discussed. Its reasons are the theorems about the absence of “hidden variables” in quantum mechanics. The quantum modeling requires the axiom of choice. The following conclusions are deduced from the hypothesis. A quantum model unlike a classical model can coincide with reality. Reality can be interpreted as a quantum computer. The physical processes (...) represent computations of the quantum computer. Quantum information is the real fundament of the world. The conception of quantum computer unifies physics and mathematics and thus the material and the ideal world. Quantum computer is a non-Turing machine in principle. Any quantum computing can be interpreted as an infinite classical computational process of a Turing machine. Quantum computer introduces the notion of “actually infinite computational process”. The discussed hypothesis is consistent with all quantum mechanics. The conclusions address a form of neo-Pythagoreanism: Unifying the mathematical and physical, quantum computer is situated in an intermediate domain of their mutual transformation. (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.org) on the limits to inference (computation) that are so general they are independent of the device doing the (...) class='Hi'>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 can 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 article The Logical Structure of Philosophy, Psychology, Mind and Language as Revealed in Wittgenstein and Searle 59p(2016). For all my articles on Wittgenstein and Searle see my e-book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Wittgenstein and Searle 367p (2016). Those interested in all my writings in their most recent versions may consult my e-book Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2016’ 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)

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)

At first sight the Theory of Computation i) relies on a kind of mathematics based on the notion of potential infinity; ii) its theoretical organization is irreducible to an axiomatic one; rather it is organized in order to solve a problem: “What is a computation?”; iii) it makes essential use of doubly negated propositions of non-classical logic, in particular in the word expressions of the Church-Turing’s thesis; iv) its arguments include ad absurdum proofs. Under such aspects, it (...) is like many other scientific theories, in particular the first theories of both mechanical machines and heat machines. A more accurate examination of Theory of Computation shows a difference from the above mentioned theories in its essentially including an odd notion, “thesis”, to which no theorem corresponds. On the other hand, arguments of each of the other theories conclude a doubly negative predicate which then, by applying the inverse translation of the ‘negative one’, is translated into the corresponding affirmative predicate. By also taking into account three criticisms to the current Theory of Computation a rational re-formulation of it is sketched out; to Turing-Church thesis of the usual theory corresponds a similar proposition, yet connecting physical total computation functions with constructive mathematical total computation functions. -/- . (shrink)

The recent debate on hypercomputation has raised new questions both on the computational abilities of quantum systems and the Church-Turing Thesis role in Physics.We propose here the idea of “effective physical process” as the essentially physical notion of computation. By using the Bohm and Hiley active information concept we analyze the differences between the standard form (quantum gates) and the non-standard one (adiabatic and morphogenetic) of Quantum Computing, and we point out how its Super-Turing potentialities derive from (...) an incomputable information source in accordance with Bell’s constraints. On condition that we give up the formal concept of “universality”, the possibility to realize quantum oracles is reachable. In this way computation is led back to the logic of physical world. (shrink)

This is the 3rd edition. Although a number of new technological applications require classical deductive computation with non-classical logics, many key technologies still do well—or exclusively, for that matter—with classical logic. In this first volume, we elaborate on classical deductive computing with classical logic. The objective of the main text is to provide the reader with a thorough elaboration on both classical computing – a.k.a. formal languages and automata theory – and classical deduction with the classical first-order predicate calculus (...) with a view to computational implementations, namely in automated theorem proving and logic programming. The present third edition improves on the previous ones by providing an altogether more algorithmic approach: There is now a wholly new section on algorithms and there are in total fourteen clearly isolated algorithms designed in pseudo-code. Other improvements are, for instance, an emphasis on functions in Chapter 1 and more exercises with Turing machines. (shrink)

What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such systems, guided and limited by associative memory, is similar to the stream of consciousness. A specific architecture of an artificial system, termed articon, is introduced that by its very design has to claim being conscious. (...) Non-verbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the flow of inner states of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills — when conscious information processing is replaced by subconscious — is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute articon’s claims that it is conscious. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human. (shrink)

This paper develops my (BJPS 2009) criticisms of the philosophical significance of a certain sort of infinitary computational process, a hyperloop. I start by considering whether hyperloops suggest that "effectively computable" is vague (in some sense). I then consider and criticise two arguments by Hogarth, who maintains that hyperloops undermine the very idea of effective computability. I conclude that hyperloops, on their own, cannot threaten the notion of an effective procedure.

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

In the brain the relations between free neurons and the conditioned ones establish the constraints for the informational neural processes. These constraints reflect the systemenvironment state, i.e. the dynamics of homeocognitive activities. The constraints allow us to define the cost function in the phase space of free neurons so as to trace the trajectories of the possible configurations at minimal cost while respecting the constraints imposed. Since the space of the free states is a manifold or a non orthogonal space, (...) the minimum distance is not a straight line but a geodesic. The minimum condition is expressed by a set of ordinary differential equation ( ODE ) that in general are not linear. In the brain there is not an algorithm or a physical field that regulates the computation, then we must consider an emergent process coming out of the neural collective behavior triggered by synaptic variability. We define the neural computation as the study of the classes of trajectories on a manifold geometry defined under suitable constraints. The cost function supervises pseudo equilibrium thermodynamics effects that manage the computational activities from beginning to end and realizes an optimal control through constraints and geodetics. The task of this work is to establish a connection between the geometry of neural computation and cost functions. To illustrate the essential mathematical aspects we will use as toy model a Network Resistor with Adaptive Memory (Memristors).The information geometry here defined is an analog computation, therefore it does not suffer the limits of the Turing computation and it seems to respond to the demand for a greater biological plausibility. The model of brain optimal control proposed here can be a good foundation for implementing the concept of "intentionality",according to the suggestion of W. Freeman. Indeed, the geodesic in the brain states can produce suitable behavior to realize wanted functions and invariants as neural expressionsof cognitive intentions. (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 definition of quantum computer is supposed: as a countable set of Turing machines on the ground of: quantum parallelism, reversibility, entanglement. Qubit is the set of all the i–th binary location cells transforming in parallel by unitary matrices. The Church thesis is suggested in the form relevat to quantum computer. The notion of the non–finite (but not infinite) potency of a set is introduced .

In this multi-disciplinary investigation we show how an evidence-based perspective of quantification---in terms of algorithmic verifiability and algorithmic computability---admits evidence-based definitions of well-definedness and effective computability, which yield two unarguably constructive interpretations of the first-order Peano Arithmetic PA---over the structure N of the natural numbers---that are complementary, not contradictory. The first yields the weak, standard, interpretation of PA over N, which is well-defined with respect to assignments of algorithmically verifiable Tarskian truth values to the formulas of PA under the interpretation. (...) The second yields a strong, finitary, interpretation of PA over N, which is well-defined with respect to assignments of algorithmically computable Tarskian truth values to the formulas of PA under the interpretation. We situate our investigation within a broad analysis of quantification vis a vis: * Hilbert's epsilon-calculus * Goedel's omega-consistency * The Law of the Excluded Middle * Hilbert's omega-Rule * An Algorithmic omega-Rule * Gentzen's Rule of Infinite Induction * Rosser's Rule C * Markov's Principle * The Church-Turing Thesis * Aristotle's particularisation * Wittgenstein's perspective of constructive mathematics * An evidence-based perspective of quantification. By showing how these are formally inter-related, we highlight the fragility of both the persisting, theistic, classical/Platonic interpretation of quantification grounded in Hilbert's epsilon-calculus; and the persisting, atheistic, constructive/Intuitionistic interpretation of quantification rooted in Brouwer's belief that the Law of the Excluded Middle is non-finitary. We then consider some consequences for mathematics, mathematics education, philosophy, and the natural sciences, of an agnostic, evidence-based, finitary interpretation of quantification that challenges classical paradigms in all these disciplines. (shrink)

This paper investigates the view that digital hypercomputing is a good reason for rejection or re-interpretation of the Church-Turing thesis. After suggestion that such re-interpretation is historically problematic and often involves attack on a straw man (the ‘maximality thesis’), it discusses proposals for digital hypercomputing with Zeno-machines , i.e. computing machines that compute an infinite number of computing steps in finite time, thus performing supertasks. It argues that effective computing with Zeno-machines falls into a dilemma: either they are specified (...) such that they do not have output states, or they are specified such that they do have output states, but involve contradiction. Repairs though non-effective methods or special rules for semi-decidable problems are sought, but not found. The paper concludes that hypercomputing supertasks are impossible in the actual world and thus no reason for rejection of the Church-Turing thesis in its traditional interpretation. (shrink)

In the article, the author analyzes the problems of human-computer communication in the context of artificial intelligence, augmented reality and a Turing methodology for comparing the capabilities of artificial and natural intelligence in a dialogue. It is argued that the tool with which the computer and humans communicate is of no less importance than the computer program with which the dialogue is conducted. As an example of the implementation of such visualization, the project “E.LENA” of a digital television anchor (...) created at the Sberbank Robotics Laboratory was considered. The author gives his own version of Turing's specific test to study the features of non- verbal communication interfaces between a person and a computer. The key idea formulated by the author for further discussion is to provide artificial intelligence with visual avatar in order to avoid the alienation of man and computer and the loss of semantic information during its transmission from computer to person. (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)

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)

There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The third, statistical (...) learning theory, has introduced measures of capacity that control (in part) the expected risk of classifiers [3]. These capacities quantify the expectations regarding future data that learning algorithms embed into classifiers. Solomonoff and Hutter have applied algorithmic information to prove remarkable results on universal induction. Shannon information provides the mathematical foundation for communication and coding theory. However, both approaches have shortcomings. Algorithmic information is not computable, severely limiting its practical usefulness. Shannon information refers to ensembles rather than actual events: it makes no sense to compute the Shannon information of a single string – or rather, there are many answers to this question depending on how a related ensemble is constructed. Although there are asymptotic results linking algorithmic and Shannon information, it is unsatisfying that there is such a large gap – a difference in kind – between the two measures. This note describes a new method of quantifying information, effective information, that links algorithmic information to Shannon information, and also links both to capacities arising in statistical learning theory [4, 5]. After introducing the measure, we show that it provides a non-universal analog of Kolmogorov complexity. We then apply it to derive basic capacities in statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. A nice byproduct of our approach is an interpretation of the explanatory power of a learning algorithm in terms of the number of hypotheses it falsifies [6], counted in two different ways for the two capacities. We also discuss how effective information relates to information gain, Shannon and mutual information. (shrink)

Abstract. Recent developments, both in the cognitive sciences and in world events, bring special emphasis to the study of morality. The cognitive sci- ences, spanning neurology, psychology, and computational intelligence, offer substantial advances in understanding the origins and purposes of morality. Meanwhile, world events urge the timely synthesis of these insights with tra- ditional accounts that can be easily assimilated and practically employed to augment moral judgment, both to solve current problems and to direct future action. The object of the (...) following paper is to present such a synthesis in the form of a model of moral cognition, the ACTWith model of conscience. The purpose of the model is twofold. One, the ACTWith model is intended to shed light on personal moral dispositions, and to provide a tool for actual human moral agents in the refinement of their moral lives. As such, it re- lies on the power of personal introspection, bolstered by the careful study of moral exemplars available to all persons in all cultures in the form of literary or religious figures, if not in the form of contemporary peers and especially leadership. Two, the ACTWith model is intended as a minimum architec- ture for fully functional artificial morality. As such, it is essentially amodal, implementation non-specific and is developed in the form of an information processing control system. There are given as few hard points in this sys- tem as necessary for moral function, and these are themselves taken from review of actual human cognitive processes, thereby intentionally capturing as closely as possible what is expected of moral action and reaction by hu- man beings. Only in satisfying these untutored intuitions should an artificial agent ever be properly regarded as moral, at least in the general population of existing moral agents. Thus, the ACTWith model is intended as a guide both for individual moral development and for the development of artificial moral agents as future technology permits. (shrink)

Io do una recensione dettagliata di 'The Outer Limits of Reason' di Noson Yanofsky da una prospettiva unificata di Wittgenstein e psicologia evolutiva. Inditesto che la difficoltà con questioni come il paradosso nel linguaggio e nella matematica, l'incompletezza, l'indecidibilità, la computabilità, il cervello e l'universo come computer ecc., derivano tutto dall'incapacità di guardare attentamente al nostro uso del linguaggio nel contesto appropriato e quindi alla mancata separazione delle questioni di fatto scientifico dalle questioni di come funziona il linguaggio. Discuto le (...) opinioni di Wittgenstein sull'incompletezza, la paracoerenza e l'indecidibilità e il lavoro di Wolpert sui limiti del calcolo. Per riassumere: L'Universo Secondo Brooklyn---Buona Scienza, Non Così Buona Filosofia. Coloro che desiderano un quadro aggiornato completo per il comportamento umano dalla moderna vista a due systems possono consultare il mio libro 'La struttura logica dellafilosofia, psicologia, Mind e il linguaggio in Ludwig Wittgenstein e John Searle' 2nd ed (2019). Coloro che sono interessati a più dei miei scritti possono vedere 'TalkingMonkeys--Filosofia, Psicologia, Scienza, Religione e Politica su un Pianeta Condannato--Articoli e Recensioni 2006-2019 3rd ed (2019) e Suicidal Utopian Delusions in the 21st Century 5th ed (2020) . (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)

Self-consciousness relates to important themes, such as sentience and personhood, and is often the cornerstone of moral theories (Warren, 1997). However, not much attention is given to future moral creatures of the earth: robots. This may be due to the unsettled status of their experience, which is why I have chosen to find the necessary features of self-consciousness in them. Philosophy of mind is also my interest which I have developed since I rejected the idea of souls and could not (...) account for my own phenomenal experiences. Among the current answers to the hard problem of consciousness (Chalmers, 1995) one stood out to me – Daniel Dennett’s multiple drafts model of consciousness presented in “Consciousness Explained” (Dennett, 1991) and his other works. Daniel Dennett is a self-described illusionist (Dennett, 2016) and a functionalist. His position is synonymous to holding self-conscious experiences, such as deliberating over one’s pain or reflecting over one’s actions insufficient to postulate existence of a metaphysical self. He believes instead that experiences are functional realizations which do not require any self, and the feeling of being conscious or self-conscious is actually a ”user-illusion” (Dennett, 1991), a term describing the unjustified belief of being in control of one’s actions. In his views, a selfhood as it is popularly conceived is contradictory. Therefore, he rejects realism about mental events, a position also called naïve realism (Siegel, 2011), on the basis that it is unverifiable and there are insufficient grounds to accept mental events (Dennett, 1988). He adopts a similar scepticism towards other theories of minds, hence nearing eliminative materialism in self-consciousness and related theme of pain (Dennett, 1978). His multiple drafts model of consciousness seeks to ground our beliefs about self-consciousness in the specific architecture of mind, which is particular to every species and kind of being. As such it is a particularly flexible theory that examines experiences of non-humans without falling into binary thinking – believing that a being has as rich inner life as humans do or none. This is why it is a great starting point for establishing necessary conditions for consciousness in robots. In this essay, I will often use words computer, programme, robot interchangeably for the purpose of clarity and take as the working definition “autonomous machines capable of sensing their environment, making decision and performing tasks similar to human actions”. I chose to explore self-consciousness instead of committing to the issue of consciousness, because consciousness can be defined in a weak sense, in which even a thermostat is “conscious” of the temperature, and consciousness in a strong sense, which invokes the internalism and externalism debate that is less connected to the nature of experiences than self-consciousness. My exploration pertains first and foremost to the philosophy of mind, of which I will examine the most relevant positions and decide to commit to functionalism (Block, 1980). Then I will advocate for explaining away of some of phenomena of self-consciousness and believing self-consciousness to be a sort of illusion. From there I will conclude that robots could fall for the same illusion of self-consciousness and therefore become self-conscious. I will argue, that self-consciousness can be established using a modified Turing Test, which would determine if a robot believed in a meaningful narrative about itself (Ricoeur, 1991) and hence be self-conscious. This will lead to answering the question of this essay: what are the features necessary for a self-conscious robot in the light of “Consciousness Explained” by Daniel Dennett? (shrink)

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it (...) is with computing, which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine. (shrink)

We present a simple example that disproves the universality principle. Unlike previous counter-examples to computational universality, it does not rely on extraneous phenomena, such as the availability of input variables that are time varying, computational complexity that changes with time or order of execution, physical variables that interact with each other, uncertain deadlines, or mathematical conditions among the variables that must be obeyed throughout the computation. In the most basic case of the new example, all that is used is (...) a single pre-existing global variable whose value is modified by the computation itself. In addition, our example offers a new dimension for separating the computable from the uncomputable, while illustrating the power of parallelism in computation. (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)

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)

K denotes both the knowledge predicate satisfied by every currently known theorem and the finite set of all currently known theorems. The set K is time-dependent and publicly available. Any theorem of any mathematician from past or present forever belongs to K. In the statement: It is unknown whether or not there are infinitely many twin primes, the predicate K appears exclusively outside a formal mathematical statement. We prove the following Statement 1: there is a limit-computable function β:N→N of unknown (...) computability which eventually dominates every function δ:N→N with a single-fold Diophantine representation. Statement 1 shows that there is a non-trivially true statement in computability (which may be falsified) in which the predicate K appears exclusively inside the statement. Statement 1 holds when, for every n∈N, β(n) is the smallest b∈N such that if a system of equations S⊆{1=x_k, x_i+x_j=x_k, x_i \cdot x_j=x_k: i,j,k∈{0,...,n}} has a unique solution in N^{n+1}, then this solution belongs to {0,...,b}^{n+1}. Statement 1 does not follow from any widely known mathematical theorem. Ignoring the epistemic condition in Statement 1, Statement 1 is implied by the following theorem of Royer and Case: There is a limit-computable function β_1:N→N which eventually dominates every computable function δ_1:N→N. The proof of Statement 1 shows that Statement 1 follows from some mathematical theorem and the following conjunction: ((The function β is computable)∉K)∧((The function β is uncomputable)∉K). We present an alternative proof of the theorem of Royer and Case. (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 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)

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.

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)

This work addresses a broad range of questions which belong to four fields: computation theory, general philosophy of science, philosophy of cognitive science, and philosophy of mind. Dynamical system theory provides the framework for a unified treatment of these questions. ;The main goal of this dissertation is to propose a new view of the aims and methods of cognitive science--the dynamical approach . According to this view, the object of cognitive science is a particular set of dynamical systems, which (...) I call "cognitive systems". The goal of a cognitive study is to specify a dynamical model of a cognitive system, and then use this model to produce a detailed account of the specific cognitive abilities of that system. The dynamical approach does not limit a-priori the form of the dynamical models which cognitive science may consider. In particular, this approach is compatible with both computational and connectionist modeling, for both computational systems and connectionist networks are special types of dynamical systems. ;To substantiate these methodological claims about cognitive science, I deal first with two questions in two different fields: What is a computational system? What is a dynamical explanation of a deterministic process? ;Intuitively, a computational system is a deterministic system which evolves in discrete time steps, and which can be described in an effective way. In chapter 1, I give a formal definition of this concept which employs the notions of isomorphism between dynamical systems, and of Turing computable function. In chapter 2, I propose a more comprehensive analysis which is based on a natural generalization of the concept of Turing machine. ;The goal of chapter 3 is to develop a theory of the dynamical explanation of a deterministic process. By a "dynamical explanation" I mean the specification of a dynamical model of the system or process which we want to explain. I start from the analysis of a specific type of explanandum--dynamical phenomena--and I then use this analysis to shed light on the general form of a dynamical explanation. Finally, I analyze the structure of those theories which generate explanations of this form, namely dynamical theories. (shrink)

Is the brain really a computer? In particular, is our intelligence a computational achievement: is it because our brains are computers that we get on in the world as well as we do? In this paper I will evaluate an ambitious new argument to the contrary, developed in Landgrebe and Smith (2021a, 2022). Landgrebe and Smith begin with the fact that many dynamical systems in the world are difficult or impossible to model accurately (inter alia, because it is intractable to (...) find exact solutions to the differential equations that describe them—meaning we have to approximate—but at the same time they are such that small differences in starting conditions lead to big differences in final conditions, thwarting accurate approximation). Yet we manage to survive and thrive in a world full of such systems. Landgrebe and Smith argue from these premises that it is not because our brains are computers that we get on as well as we do: instead it is because of the various ways that we dynamically couple with such systems, these couplings themselves impossible to model well enough to emulate in silico. Landgrebe and Smith thus defend a dynamical systems model in the lineage of Gibson (1979), Van Gelder (1995), and Thompson (2007), though their focus is on decisively refuting the computationalist alternatives rather than developing the positive account. Here I will defend the claim that human intelligence is genuinely computational (and that whole brain emulation and others forms of AGI may be possible) against this argument. (shrink)

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)

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)

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)

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)

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 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)

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)

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)

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)

A number of examples have been given of physical systems (both classical and quantum mechanical) which when provided with a (continuously variable) computable input will give a non-computable output. It has been suggested that these systems might allow one to design analogue machines which would calculate the values of some number-theoretic non-computable function. Analysis of the examples show that the suggestion is wrong. In Section 4 I claim that given a reasonable definition of analogue machine it will always be wrong. (...) The claim is to be read not so much as a dogmatic assertion, but rather as a challenge. -/- In Sections 1 and 2 I discuss analogue machines, and lay down some conditions which I believe they must satisfy. In Section I discuss the particular forms which a paradigm undecidable problem (or non-computable function) may take. In Sections 5 and 6 I justify any claim for two particular examples lying within the range of classical physics, and in Section 7 I justify it for two (closely connected) examples from quantum mechanics, and discuss, very briefly, other possible quantum mechanical situations. Section 8 contains various remarks and comments. In Section 9 I consider the suggestion made by Penrose that a (future) theory of quantum gravity may predict non-locally-determined, and perhaps non-computable patterns of growth for microsopic structures. My conclusion is that such a theory will have to have non-computability built into it. (shrink)