Abstract: Alan Turing was neither a biologist nor a chemist, and yet the paper he published in 1952, 'The chemical basis of morphogenesis', on the spontaneous formation of patterns in systems undergoing reaction and diffusion of their ingredients has had a substantial impact on both fields, as well as in other areas as disparate as geomorphology and criminology. Motivated by the question of how a spherical embryo becomes a decidedly non-spherical organism such as a human being, Turing devised a mathematical model that explained how random fluctuations can drive the emergence of pattern and structure from initial uniformity. The spontaneous appearance of pattern and form in a system far away from its equilibrium state occurs in many types of natural process, and in some artificial ones too. It is often driven by very general mechanisms, of which Turing's model supplies one of the most versatile. For that reason, these patterns show striking similarities in systems that seem superficially to share nothing in common, such as the stripes of sand ripples and of pigmentation on a zebra skin. New examples of 'Turing patterns' in biology and beyond are still being discovered today. This commentary was written to celebrate the 350th anniversary of the journal Philosophical Transactions of the Royal Society.

Abstract: One of the most fundamental questions in biology is that of biological pattern: how do the structures and shapes of organisms arise? Undoubtedly, the two most influential ideas in this area are those of Alan Turing’s ‘reaction-diffusion’ and Lewis Wolpert’s ‘positional information’. Much has been written about these two concepts but some confusion still remains, in particular about the relationship between them. Here, we address this relationship and propose a scheme of three distinct ways in which these two ideas work together to shape biological form.

Abstract: We introduce the notion of universal memcomputing machines (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the same physical location. We analytically prove that the memory properties of UMMs endow them with universal computing power (they are Turing-complete), intrinsic parallelism, functional polymorphism, and information overhead , namely, their collective states can support exponential data compression directly in memory. We also demonstrate that a UMM has the same computational power as a nondeterministic Turing machine, namely, it can solve nondeterministic polynomial (NP)-complete problems in polynomial time. However, by virtue of its information overhead, a UMM needs only an amount of memory cells (memprocessors) that grows polynomially with the problem size. As an example, we provide the polynomial-time solution of the subset-sum problem and a simple hardware implementation of the same. Even though these results do not prove the statement NP = P within the Turing paradigm, the practical realization of these UMMs would represent a paradigm shift from the present von Neumann architectures, bringing us closer to brain-like neural computation.

Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading alan turing the enigma as one of the reading material to finish quickly.

Abstract: How periodic patterns are generated is an open question. A number of mechanisms have been proposed – most famously, Turing's reaction-diffusion model. However, many theoretical and experimental studies focus on the Turing mechanism while ignoring other possible mechanisms. Here, we use a general model of periodic patterning to show that different types of mechanism (molecular, cellular, mechanical) can generate qualitatively similar final patterns. Observation of final patterns is therefore not sufficient to favour one mechanism over others. However, we propose that a mathematical approach can help to guide the design of experiments that can distinguish between different mechanisms, and illustrate the potential value of this approach with specific biological examples.

Abstract: In the popular imagination, the relevance of Turing's theoretical ideas to people producing actual machines was significant and appreciated by everybody involved in computing from the moment he published his 1936 paper ‘On Computable Numbers’. Careful historians are aware that this popular conception is deeply misleading. We know from previous work by Campbell-Kelly, Aspray, Akera, Olley, Priestley, Daylight, Mounier-Kuhn, Haigh, and others that several computing pioneers, including Aiken, Eckert, Mauchly, and Zuse, did not depend on (let alone were they aware of) Turing's 1936 universal-machine concept. Furthermore, it is not clear whether any substance in von Neumann's celebrated 1945 ‘First Draft Report on the EDVAC’ is influenced in any identifiable way by Turing's work. This raises the questions: (i) When does Turing enter the field? (ii) Why did the Association for Computing Machinery (ACM) honor Turing by associating his name to ACM's most prestigious award, the Turing Award? Previous authors have ...

Abstract: This paper presents some important issues on misidentification of human interlocutors in text-based communication during practical Turing tests. The study here presents transcripts in which human judges succumbed to theconfederate effect, misidentifying hidden human foils for machines. An attempt is made to assess the reasons for this. The practical Turing tests in question were held on 23 June 2012 at Bletchley Park, England. A selection of actual full transcripts from the tests is shown and an analysis is given in each case. As a result of these tests, conclusions are drawn with regard to the sort of strategies which can perhaps lead to erroneous conclusions when one is involved as an interrogator. Such results also serve to indicate conversational directions to avoid for those machine designers who wish to create a conversational entity that performs well on the Turing test.

Abstract: This article explores the life and ideas of Alan Turing (1912–1954), commonly known as the father of artificial intelligence (AI), and highlights the process whereby the human self is reconceptualized in the development of Turing's ideas of machine intelligence. I will further illustrate how this process of self-reconceptualization – composed of the pursuit, adaptation and transformation of self-knowledge – is closely related to contemporary digital life. In doing so, I wish to reveal the ways in which Turing's underlying self-transforming agenda of AI can contribute to our understanding of the human self, for AI, as I will argue, leads to questions of existence and existential anxieties.

Abstract: As the movie The Imitation Game celebrates British mathematician Alan Turing9s contributions to the Allied victory in World War II, the artificial intelligence (AI) community is rethinking another of his legacies: the Turing Test. At a 25 January workshop at the 29th Association for the Advancement of Artificial Intelligence conference in Austin, researchers will discuss proposals for a new Turing Championship composed of three to five research challenges. In contrast with Turing9s single litmus test, the proposed challenges acknowledge that intelligence has multiple dimensions—from language comprehension to social awareness—that are best tackled piece by piece. The new Turing Championship would motivate researchers to develop machines with a deeper understanding of the world, argue the workshop organizers. By early 2016, they hope to stage a set of trial competitions that will be revised and repeated regularly.

Abstract: In this paper, we study the stationary and oscillatory Turing instabilities of a homogeneous equilibrium in prey–predator reaction–diffusion systems with dormant phase of predators. We propose a simple criterion which is useful in classifying these Turing instabilities. Moreover, numerical simulations reveal transient spatio-temporal complex patterns which are a mixture of spatially periodic steady states and traveling/standing waves. In this mixture, the steady part is the stable Turing pattern bifurcated primarily from the homogeneous equilibrium, while wave parts are unstable oscillatory solutions bifurcated secondarily from the same homogeneous equilibrium. Although our criterion does not exclude the occurrence of oscillatory Turing instability, we have not yet found stable traveling/standing waves due to oscillatory Turing instability in our simulations. These results suggest that dormancy of predators is not a generator but an enhancer of spatio-temporal Turing patterns in prey–predator reaction–diffusion systems.

Abstract: This article makes two major contributions. First, it develops a methodology to investigate techno-social engineering of human beings. Many claim that technology dehumanizes, but this article is the first to develop a systematic approach to identifying when technologies dehumanize. The methodology depends on a fundamental and radical repurposing of the Turing test. The article develops an initial series of human-focused tests to examine different aspects of intelligence and distinguish humans from machines: (a) mathematical computation, (b) random number generation, (c) common sense, and (d) rationality. All four are plausible reverse Turing tests that generally could be used to distinguish humans and machines. Yet the first two do not implicate fundamental notions of what it means to be a human; the third and fourth do. When these latter two tests are passed, we have good reason to question and evaluate the humans and the techno-social environment within which they are situated. Second, this article applies insights from the common sense and rationality tests to evaluate the ongoing behavioral law and economics project of nudging us to become rational humans. Based on decades of findings from cognitive psychologists and behavioral economists, this project has influenced academics across many disciplines and public policies around the world. There are a variety of institutional means for implementing "nudges" to improve human decision making in contexts where humans tend to act irrationally or contrary to their own welfare. Cass Sunstein defines nudges more narrowly and carefully as "low-cost, choice-preserving, behaviorally informed approaches to regulatory problems, including disclosure requirements, default rules, and simplification." These approaches tend to be transparent and more palatable. But there are other approaches, such as covert nudges like subliminal advertising. The underlying logic of nudging is to construct or modify the "choice architecture" or the environment within which humans make decisions. Yet as Lawrence Lessig made clear long ago, architecture regulates powerfully but subtly, and it can easily run roughshod over values that don’t matter to the architects. Techno-social engineering through (choice) architecture is rampant and will grow in scale and scope in the near future, and it demands close attention because of its subtle influence on both what people do and what people believe to be possible. Accordingly, this article evaluates nudging as a systematic agenda where institutional decisions about particular nudges aggregate and set a path that entails techno-social engineering of humans and society. The article concludes with two true stories that bring these two contributions together. Neither is quite a story of dehumanization where humans become indistinguishable from machines. Rather, each is an example of an incremental step in that direction. The first concerns techno-social engineering of children’s preferences. It is the story of a simple nudge, implemented through the use of a wearable technology distributed in an elementary school for the purpose of encouraging fitness. The second concerns techno-social engineering of human emotions — the Facebook Emotional Contagion Experiment. It is not (yet) a conventional nudge, but it relies on the underlying logic of nudging. Both can be seen as steps along the same path.

Abstract: This article expands on Curry's work on how to implement the problem of inverse interpolation on the ENIAC (1946) and his subsequent work on developing a theory of program composition (1948-1950). It is shown that Curry's hands-on experience with the ENIAC on the one side and his acquaintance with systems of formal logic on the other, were conductive to conceive a compact " notation for program construction " which in turn would be instrumental to a mechanical synthesis of programs. Since Curry's systematic programming technique pronounces a critique of the Goldstine-von Neumann style of coding, his " calculus of program composition " not only anticipates automatic programming but also proposes explicit hardware optimisations largely unperceived by computer history until Backus' famous ACM Turing Award lecture (1977). The cohesion of these findings asks for an integrative historiographical approach. An appendix gives, for the first time, a full description of Curry's arithmetic compiler.

Abstract: Information processing theory emerged from Turing's work in the 1960s in which humans' cognitive processing was compared to processing used in a computer. This was a response to radical behaviorism, which could not account for reading, perceptual inversion, and cognitive maps. This systems engineering of the mind attempts to isolate behavioral or electrophysiological effects to a given processing stage. This entry briefly applies information processing theory to age differences in processing speed, divided attention, and emotional threat perception. Keywords: cognitive development; information processing and cognitions; neuroscience; Skinner, B. F; Turing, Alan; Wundt, Wilhelm

Abstract: This monograph presents recursion theory from a generalized and largely global point of view. A major theme is the study of the structures of degrees arising from two key notions of reducibility, the Turing degrees and the hyperdegrees, using ideas and techniques beyond those of classical recursion theory. These include structure theory, hyperarithmetic determinacy and rigidity, basis theorems, independence results on Turing degrees, as well as applications to higher randomness.

Abstract: Sometime in the future we will have to deal with the impact of AI's being mistaken for humans. For this reason, I propose that any autonomous system should be designed so that it is unlikely to be mistaken for anything besides an autonomous sysem, and should identify itself at the start of any interaction with another agent.

Abstract: Turing morphogen models have been extensively explored in the context of large-scale self-organization in multicellular biological systems. However, reconciling the detailed biology of morphogen dynamics, while accounting for time delays associated with gene expression, reveals aberrant behaviours that are not consistent with early developmental self-organization, especially the requirement for exquisite temporal control. Attempts to reconcile the interpretation of Turing's ideas with an increasing understanding of the mechanisms driving zebrafish pigmentation suggests that one should reconsider Turing's model in terms of pigment cells rather than morphogens (Nakamasu et al., 2009, PNAS, 106: , 8429-8434; Yamaguchi et al., 2007, PNAS, 104: , 4790-4793). Here the dynamics of pigment cells is subject to response delays implicit in the cell cycle and apoptosis. Hence we explore simulations of fish skin patterning, focussing on the dynamical influence of gene expression delays in morphogen-based Turing models and response delays for cell-based Turing models. We find that reconciling the mechanisms driving the behaviour of Turing systems with observations of fish skin patterning remains a fundamental challenge.

Abstract: Comparing the legacy of Alan Turing in computer science with that of Carl Friedrich Gauss in mathematics.

Abstract: JORG BRENDLE, ANDREW BROOKE-TAYLOR, KENG MENG NG, AND ANDR¨ E NIES´Abstract We develop an analogy between cardinal characteristics from settheory and highness properties from computability theory, which specify asense in which a Turing oracle is computationally strong We focus on char-acteristics from Cichon´’s diagram

Abstract: Alan Turing, in “The Chemical Basis of Morphogenesis” [Turing AM (1952) Philos Trans R Soc Lond 237(641):37–72], described how, in circular arrays of identical biological cells, diffusion can interact with chemical reactions to generate up to six periodic spatiotemporal chemical structures. Turing proposed that one of these structures, a stationary pattern with a chemically determined wavelength, is responsible for differentiation. We quantitatively test Turing’s ideas in a cellular chemical system consisting of an emulsion of aqueous droplets containing the Belousov–Zhabotinsky oscillatory chemical reactants, dispersed in oil, and demonstrate that reaction-diffusion processes lead to chemical differentiation, which drives physical morphogenesis in chemical cells. We observe five of the six structures predicted by Turing. In 2D hexagonal arrays, a seventh structure emerges, incompatible with Turing’s original model, which we explain by modifying the theory to include heterogeneity.

Abstract: Guided by a polymath approach--encompassing neuroscience, philosophy, psychology and computer science, this article describes a novel `cognitive' computational mind framework for text comprehension in terms of Minsky's `Society of Mind' and `Emotion Machine' theories. Observing a top-down design method, we enumerate here the macrocosmic elements of the model--the `agencies' and memory constructs, followed by an elucidation on the working principles and synthesis concerns. Besides corroboration of results of a dry-run test by thoughts generated by random human subjects; the completeness of the conceptualized framework has been validated as a consequence of its total representation of `text understanding' functions of the human brain, types of human memory and emulation of the layers of the mind. A brief conceptual comparison, between the architecture and existing `conscious' agents, has been included as well. The framework, though observed here in its capacity as a text comprehender, is capable of understanding in general. A cognitive model of text comprehension, besides contributing to the `thinking machines' research enterprise, is envisioned to be strategic in the design of intelligent plagiarism checkers, literature genre-cataloguers, differential diagnosis systems, and educational aids for children with reading disorders. Turing's landmark 1950 article on computational intelligence is the principal motivator behind our research initiative.

Abstract: To incorporate metabolic, bioremedial functions into the performance of buildings and to balance generative architecture's dominant focus on computational programming and digital fabrication, this text first discusses hybridizing Maturana and Varela's biological theory of autopoiesis with Andy Clark's hypothesis of extended cognition. Doing so establishes a procedural protocol to research biological domains from which design could source data/insight from biosemiotics, sensory plants, and biocomputation. I trace computation and botanic simulations back to Alan Turing's little-known 1950s Morphogenetic drawings, reaction-diffusion algorithms, and pioneering artificial intelligence (AI) in order to establish bioarchitecture's generative point of origin. I ask provocatively, Can buildings think? as a question echoing Turing's own, "Can machines think?"

Abstract: Turing’s (Proceedings of the London Mathematical Society 42:230–265, 1936) paper on computable numbers has played its role in underpinning different perspectives on the world of information. On the one hand, it encourages a digital ontology, with a perceived flatness of computational structure comprehensively hosting causality at the physical level and beyond. On the other (the main point of Turing’s paper), it can give an insight into the way in which higher order information arises and leads to loss of computational control—while demonstrating how the control can be re-established, in special circumstances, via suitable type reductions. We examine the classical computational framework more closely than is usual, drawing out lessons for the wider application of information–theoretical approaches to characterizing the real world. The problem which arises across a range of contexts is the characterizing of the balance of power between the complexity of informational structure (with emergence, chaos, randomness and ‘big data’ prominently on the scene) and the means available (simulation, codes, statistical sampling, human intuition, semantic constructs) to bring this information back into the computational fold. We proceed via appropriate mathematical modelling to a more coherent view of the computational structure of information, relevant to a wide spectrum of areas of investigation.

Abstract: We consider a model of so-called hybrid recurrent neural networks composed with Boolean input and output cells as well as sigmoid internal cells. When subjected to some infinite binary input stream, the Boolean output cells necessarily exhibit some attractor dynamics, which is assumed to be of two possible kinds, namely either meaningful or spurious, and which underlies the arising of spatiotemporal patterns of output discharges. In this context, we show that rational-weighted neural networks are computationally equivalent to deterministic Muller Turing machines, whereas all other models of real-weighted or evolving neural networks are equivalent to each other, and strictly more powerful than deterministic Muller Turing machines. In this precise sense, the analog and evolving neural networks are super-Turing. We further provide some precise mathematical characterization of the expressive powers of all these neural models. These results constitute a generalization to the current computational context of those obtained in the cases of classical as well as interactive computations. They support the idea that recurrent neural networks represent a natural model of computation beyond the Turing limits.

Abstract: For Turing and the majority of computer scientists, an Oracle is a device that supplies a Turing machine with the values of some function (on the natural numbers or words in some alphabet) that is not recursively, e.g., Turing-machine, computable. Now technological innovations and social progress necessitate further changes to the concept of an Oracle.The first step in this direction was done by Burgin and Mikkilineni [1] using the relativization of the concept of an Oracle and extending its functions. Here we develop a forward-thinking theory of Oracles with application to distributed computing network technology. Different types and sorts of Oracles are introduced and studied. Their properties are explicated and analyzed in the context of computation theory and network technology. Utilization of Oracles in the distributed intelligent managed element (DIME) network architecture is described demonstrating expediency of Oracle theory in designing self-managing distributed computing processes.