Abstract: We explore in the framework of Quantum Computation the notion of computability, which holds a central position in Mathematics and Theoretical Computer Science. A quantum algorithm that exploits the quantum adiabatic processes is considered for the Hilbert’s tenth problem, which is equivalent to the Turing halting problem and known to be mathematically noncomputable. Generalised quantum algorithms are also considered for some other mathematical noncomputables in the same and of different noncomputability classes. The key element of all these algorithms is the measurability of both the values of physical observables and of the quantum-mechanical probability distributions for these values. It is argued that computability, and thus the limits of Mathematics, ought to be determined not solely by Mathematics itself but also by physical principles.

Abstract: This paper proposes the Turing stream cipher. Turing offers up to 256-bit key strength, and is designed for extremely efficient software implementation. It combines an LFSR generator based on that of SOBER[27] with a keyed mixing function reminiscent of a block cipher round. Aspects of the block mixer round have been derived from Rijndael[20], Twofish[21], tc24[23] and SAFER[22].

Abstract: From the Publisher: Can we build a robot with humanlike intelligence? What technology is needed to produce the perfect blueprint for the ideal "living" robot? What part will robots play in the future of humanity? Are we risking robot domination of the world? This highly readable volume tells the past, present, and future stories of robots and Artificial Intelligence, more commonly known as AI. Discussed here are the dreams of the pioneers Alan Turing and John von Neumann, who envisioned making computers intelligent when they were first invented. The author also describes in detail the current state of computer and robot technology, and points out the challenges inherent in teaching a machine to "think" and make decisions. Other topics include the life cycle of a present-day robot, raw materials and energy sources required for robot construction, and experimental progress in the field of self-designing robots. This absorbing volume is science fact—not science fiction. It includes more than 200 full-color photos and illustrations.

Abstract: Since the development of the first digital computer in the 1940s, the notion of computer intelligence has received considerable attention from computer scientists, philosophers, and psychologists. The question of whether it is possible to create a computer program that possesses human intelligence has spurred much debate. Turing (1950) argued that computers are not capable of thinking and provided several theological, psychological, and sociological arguments in support of his position. To determine a computer program’s intelligence, Turing proposed several benchmark methods. One such method requires humans to decide whether they are interacting with an actual computer program or another human via computer mediation. According to Turing, a computer could be described as intelligent if it could deceive a human into believing that it was human. The two studies presented here were designed to determine whether AutoTutor could pass a variation of the Turing test, the Bystander Turing Test. The subsequent sections of this paper address the following: (1) the AutoTutor system, (2) the Bystander Turing Test, (3) the two empirical studies, and (4) the conclusions of the studies.

Abstract: Many special problems crop up when evolutionary theory turns, quite naturally, to the question of the adaptive value and causal role of consciousness in human and nonhuman organisms. One problem is that -- unless we are to be dualists, treating it as an independent nonphysical force -- consciousness could not have had an independent adaptive function of its own, over and above whatever behavioral and physiological functions it "supervenes" on, because evolution is completely blind to the difference between a conscious organism and a functionally equivalent (Turing Indistinguishable) nonconscious "Zombie" organism: In other words, the Blind Watchmaker, a functionalist if ever there was one, is no more a mind reader than we are. Hence Turing-Indistinguishability = Darwin-Indistinguishability. It by no means follows from this, however, that human behavior is therefore to be explained only by the push-pull dynamics of Zombie determinism, as dictated by calculations of "inclusive fitness" and "evolutionarily stable strategies." We are conscious, and, more important, that consciousness is piggy-backing somehow on the vast complex of unobservable internal activity -- call it "cognition" -- that is really responsible for generating all of our behavioral capacities. Hence, except in the palpable presence of the irrational (e.g., our sexual urges) where distal Darwinian factors still have some proximal sway, it is as sensible to seek a Darwinian rather than a cognitive explanation for most of our current behavior as it is to seek a cosmological rather than an engineering explanation of an automobile's behavior. Let evolutionary theory explain what shaped our cognitive capacity (Steklis & Harnad 1976; Harnad 1996, but let cognitive theory explain our resulting behavior.

Abstract: Alan Turing draws a firm line between the mental and the physical, between the cognitive and physical sciences. For Turing, following a tradition that went back to D'Arcy Thompson, if not Geoffroy and Lucretius, throws out talk of function, intentionality, and final causes from biology as a physical science. He likens “mother nature” to the earnest A. I. scientist, who may send to school disparate versions of the “child machine,” eventually hoping for a test-passer but knowing that the vagaries of his experimental course are history and accident.

Abstract: The properties of Turing's famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. This paper shows that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing's powerful insights regarding computation. In addressing a possible reply to this criticism, it is further argued that functionalism is not a useful approach for understanding what it is to have a mind. In particular, it is shown that the difficulties involved in distinguishing implementation from function make multiple realizability claims untestable and uninformative. As a result, it is concluded that the role of Turing machines in philosophy of mind needs to be reconsidered.

Abstract: The history of formalizing the notion of algorithm (and of computation) dates back to G.W. Leibniz (1646–1716) when it focused on trying to model the computations performed by humans, for example bank clerks. These efforts culminated in the first part of the 20th century with the formalization in the form of machines, in the work of Turing [238]. These formalizations were very successful, because they led to the construction of the first electronic computers in the 1940’s. These efforts already were using some ideas from biology, for example, the functioning of neurons in neural networks (Kleene [106], McCulloch and Pitts [165]).

Abstract: According to the conventional wisdom, Turing (1950) said that computing machines can be intelligent. I don't believe it. I think that what Turing really said was that computing machines –- computers limited to computing –- can only fake intelligence. If we want computers to become genuinely intelligent, we will have to give them enough ``initiative'' (Turing, 1948, p. 21) to do more than compute. In this paper, I want to try to develop this idea. I want to explain how giving computers more ``initiative'' can allow them to do more than compute. And I want to say why I believe (and believe that Turing believed) that they will have to go beyond computation before they can become genuinely intelligent.

Abstract: The behaviour of ants when dealing with their dead has parallels with biological pattern formation more generally, for instance as seen during development.

Abstract: The Turing Test is of limited use for programs falling far short of human performance levels. We suggest an extension of Turing's idea to a more differentiated measure - the "Turing Ratio" - which provides a framework for comparing both human and algorithmic task performance. Games provide an example of the concepts. It is argued that evolutionary computation is a key method for amplifying human intelligence, an assertion which future scientists can empirically decide through measuring Turing Ratios and considering task breadth, prior knowledge, and time series of the measures.

Abstract: X ′ is the canonical example of a set which is definable from X but not recursive in X. The Turing degree of X ′ depends only on the Turing degree of X, so the jump induces an increasing function on the Turing degrees D. In this paper, we will discuss two aspects of the jump and its iterations. First, we will show that they are implicitly characterized by general properties of relative definability. Second, we will present the Shore and Slaman [1999] theorem that the function x 7→ x′ is first order definable in the Turing degrees. Finally, we will pose analogous questions about the relation y is recursively enumerable in x and discuss what is known about them. Our discussion will rest on two technical facts, which are generalizations of the following two theorems.

Abstract: We characterize the isomorphism types of principal ideals of the Turing degrees below 0′ that are lattices as the lattices with a S03 presentation, by showing that each S03 -presentable bounded upper semilattice is isomorphic to such a principal ideal. We get a similar result for the Turing degrees below any degree above 0″.

Abstract: Turing's (1936) analysis of effective symbolic procedures is a model of conceptual clarity that plays an essential role in the philosophy of mathematics. Yet appeal is often made to the effectiveness of human procedures in other areas of philosophy. This paper addresses the question of whether Turing's analysis can be applied to a broader class of effective human procedures. We use Sieg's (1994) presentation of Turing's Thesis to argue against Cleland's (1995) objections to Turing machines and we evaluate her proposal to understand the effectiveness of procedures in terms of their reliability and precision. A number of conditions for effectiveness are identified and these are used to provide a general argument against the possibility of a Leibnizian decision procedure.

Abstract: This article gives a concise but intensive survey on one of the subfields of natural computing – Molecular Computing Starting with making a brief revisit to Adleman's pioneering work, this paper will give an overview of selected topics of the field from theory to experiments, while the stress is primarily put on research of theoretical achievements of molecular computing models

Abstract: What is Church’s thesis? In bald statement, that the effectively compatable functions are the same as the recursive functions. But thinking carefully about this and other issues has led me to suspect both the formulation and accuracy of this thesis. This paper sketches my doubts. The first three characters on the stage are RF, the recursive functions; OP, the physics of our universe; and E1, the functions computable by (say) the physical operations described by Turing (that is, writing symbols from a finite set of rules.) The first formulation of Church’s thesis is The functions E1 computable in principle by Turing’s operations in OP are the same as RF. By “in principle” we mean to neglect the (supposed) finiteness of matter in our universe. There are many reasons for thinking the identity of E1(OP) and RF to be a (fortuitous) accident of our universe. Gandy attempts to describe, in his Kleene symposium paper, ways in which slight variations in OP make E1 include non-recursive functions, simply by allowing the “same” physical operations to involve more information or information paths than usual. It is also easy to imagine variations in OP so that E1 is a proper subset (even empty!) of RF. For example, if physics allowed no matter, or only gases, the Turing’s operations would not be physically realizable, so E1 would be empty. The fourth character of our story, E2, is the set of functions computable by an extension of Turing’s operations. That is, E2 embodies a different notion of what are “elementary effective operations”. My idea is this. One of the most common abstract phenomena in our world is that of equilibriating systems: parts of the universe that settle into one of a spectrum of equilibrium states once certain boundary conditions are imposed. There are, in fact, many equilibriating systems with discrete spectra, for example the quantum states of molecules. Given the definiteness of these systems, we might take the operation of equilibriating as an effective one. Note carefully, I do not mean that equilibria are computable by Turing’s operations, but that equilibriating can be so easily, reproducibly, and mindlessly accomplished that we grant it equal status with marking and moving slips of paper. My suspicion is that physics is easily rich enough so that E2, the functions compatable in principle given Turing’s operations and equilibriating, include non-recursive functions. For example, I think that chemistry may be rich enough that given a diophantine equation, we can recursively compute a molecular structure (teflon, DNA, proteins, etc.) that has been a quantum level within some interval iff the diophantive equation has a solution. That is, we plug values into the molecule as boundary conditions, and solve the equation iff the molecule finds an equilibrium. Of course, we must still have “in principle” in our claim, since matter is still finite, and possibly because engineering limitations may prevent successful manipulation of arbitrarily sized

Abstract: An assertion in a computer program is a logical formula (Boolean expression) which the programmer expects to evaluate to true on every occasion that program control reaches the point at which it is written. Assertions can be used to specify the purpose of a program, and to define the interfaces between its major components. An early proponent of assertions was Alan Turing (1948), who suggested their use in establishing the correctness of large routines. In 1967, Bob Floyd revived the idea as the basis of a verifying compiler that would automatically prove the correctness of the programs that it compiled. After reading his paper, I became a member of a small research school devoted to exploring the idea as a theoretical foundation for a top-down design methodology of program development. I did not expect the research to influence industrial practice until after my retirement from academic life, thirty years ahead. And so it has been.

Abstract: Alan Turing and the Author worked in the Computing Laboratory at Manchester University from 1951 to 1954. In the autumn of 1953 the Author asked Turing for help in proving a general theorem on the optimum design of elastic pin-jointed frames. The manuscript he produced in response to this request appears to have been his only venture into the field of engineering structures: because of his tragic death a year later it was never published. The general proof resulting from this collaboration has long since been superseded. It is now natural to formulate the design problem in linear programming terms: this makes the proof of the theorem entirely straightforward. However, Turing’s present reputation may lend some interest to a brief historical account of the way in which the ideas developed. The first two sections of this paper describe the computing facilities at Manchester in the early 1950’s and the computing work carried out there by the Author —work which eventually led to the collaboration with Turing. The remainder of the paper looks at the theorem and its proof from a present-day point of view.

Abstract: Darwin/Skinner/Turing explanation is all blind, behavioral explanation. It explains the underlying causal mechanisms of what we can do. But it does not and cannot explain how or why we feel.

Abstract: The VIIth Brazilian Symposium on Neural Networks (SBRN’02), held in 2002 at Porto de GalinhasPernambuco, was sponsored by the Brazilian Computer Society (SBC) and co-sponsored by SIG/INNS/Brazil Special Interest Group of the International Neural Networks Society in Brazil. The articles submitted to this year’s SBRN provide a more representative distribution of research around the country than previously, when submissions came primarily from the more established universities. There were submissions from authors of all regions of Brazil and countries like Argentina, Belgium, England, France, Italy, Japan, Peru, Portugal and Spain. All these papers were subjected to a rigorous review process. The SBRN’02 Proceedings were published in two volumes [1,2]. Volume I includes the accepted full papers in English, together with the abstracts of papers originally written in Portuguese or Spanish. Volume II contains only the full papers written in Portuguese and Spanish. After a new round of careful review, eleven papers in the SBRN’02 Proceedings were selected to be extended and included in this Special Issue of the Journal of Intelligent & Fuzzy Systems. In order to put the SBRN’02 into perspective, the scope of the papers selected for this Special Issue has been interdisciplinary, ranging from Mathematics, Computing, Engineering, and Cognitive Science: – A novel class of recurrent neural fuzzy networks is presented by Rosangela Ballini and Fernando Gomide. Computational experiments show that these neurofuzzy networks provide high performance in terms of approximation capability, memory requirements, and processing time when compared to its counterparts. – Marcelo A. Costa, Antonio P. Braga and Benjamim R. de Menezes introduce new constructive and pruning methods, which combine different paradigms, for controlling the neural network (Multi-layer Perceptron) generalization. In order to guarantee generalization, the proposed methods use a multi-objetive approach. – Artificial neural networks are investigated from the point of view of Turing’s analysis of computation by Wilson R. de Oliveira, Marcilio C.P. de Souto, and Teresa B. Ludermir. They propose a type of neural Turing simulation that, according to their arguments, are consistent with Turing’s analysis of computation and current cognitive theories based on agent-environment interaction, and it is physically realizable. From this study in artificial neural networks, a new sub-class of Turing machines is defined. – Gina M.B. Oliveira, Oscar K.N. Asakura, and Pedro P.B. de Oliveira extend previous results in incorporating a parameter-based heuristic into

Abstract: Abstract Two mysteries continue to frame debate in contemporary philosophy of mind. The first is the nature of consciousness. In particular, how can our conscious experience fit into the growing body of scientific knowledge about the mind and the brain? The second mystery is intentionality. How can our thoughts be about other things? For some time intentionality has appeared the easier of these two problems, and it seems that significant progress has been made. The invention of automatic computing devices in the middle of the century, following important theoretical work by Alan Turing, showed that a machine could manipulate and make use of internal representational states, thus providing an example of how the mind’s internal representations might operate. Throughout the 1980s and 1990s much work has been done on the questions of how material states of mind could bear content.

Abstract: It is a fundamental idea of computer science to search for, define, analyze, and operate with construction kits consisting of small sets of basic building blocks and a small number of operations to combine the building blocks to larger objects. While the construction kit is mostly simple, it often defines a vast, complex field that consists of all possible objects that can be built from the building blocks by using any (finite) sequence of combinations of operators. This idea affects and structures many areas of computer science. We present examples from several fields, including imperative and functional programming languages, computable functions, Turing and register machines, Boolean functions, data types, object-oriented programming, characterisations of formal languages along with examples from other disciplines. How can informatics lessons profit? If lessons are oriented towards a fundamental idea, the idea may explain, structure, and integrate many different informatics subjects and phenomena by a single recurring scheme. On the other hand, the construction kit principle belongs to the sphere of everyday thinking so students already have a basic intuition of the concept which may enhance their understanding when entering any field where the idea applies.