Abstract: Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human–machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human–machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

Abstract: This research was supported by the ERC advanced grant SIMBIONT (670555) and the Ministerio de Economia y Competitividad (through Centro de Excelencia Severo Ochoa 2013-2017, SEV-2012-0208). X. D. acknowledges support by the ERC-FP7 Grant Swarmorgan (601062). J. S. ackowledges support from ICREA. P. M. and L. M. were supported by ERC Starting Grant QUANTPATTERN (637840).

Abstract: This paper seeks to address emerging debates and controversies on the impact of robots and artificial intelligence on the world of work. Longer term discussions of technological ‘singularity’ are considered alongside the socio-technical and economic constraints on the application of robotics and AI. Evidence of robot ‘take-up’ is gathered from reports of the International Federation of Robotics and from case vignettes reported elsewhere. In assessing the contemporary relationship between singularity, robotics and AI the article reflects briefly on the two ‘tests’ of artificial ‘intelligence’ proposed by the pioneer computer scientist Alan Turing, and comments on the efficacy of his ‘tests’ in contemporary applications. The paper continues by examining aspects of public policy and concludes that technological singularity is far from imminent.

Abstract: In Contingent Computation, M. Beatrice Fazi offers a new theoretical perspective through which we can engage philosophically with computing. The book proves that aesthetics is a viable mode of investigating contemporary computational systems. It does so by advancing an original conception of computational aesthetics that does not just concern art made by or with computers, but rather the modes of being and becoming of computational processes. Contingent Computation mobilises the philosophies of Gilles Deleuze and Alfred North Whitehead in order to address aesthetics as an ontological study of the generative potential of reality. Through a novel philosophical reading of Godel’s incompleteness theorems and of Turing’s notion of incomputability, Fazi finds this potential at the formal heart of computational systems, and argues that computation is a process of determining indeterminacy. This indeterminacy, which is central to computational systems, does not contradict their functionality. Instead, it drives their very operation, albeit in a manner that might not always fit with the instrumental, representational and cognitivist purposes that we have assigned to computing.

Abstract: For delayed reaction-diffusion Schnakenberg systems with Neumann boundary conditions, critical conditions for Turing instability are derived, which are necessary and sufficient. And existence conditions for Turing, Hopf and Turing-Hopf bifurcations are established. Normal forms truncated to order 3 at Turing-Hopf singularity of codimension 2, are derived. By investigating Turing-Hopf bifurcation, the parameter regions for the stability of a periodic solution, a pair of spatially inhomogeneous steady states and a pair of spatially inhomogeneous periodic solutions, are derived in $(\tau,\varepsilon)$ parameter plane ($\tau$ for time delay, $\varepsilon$ for diffusion rate). It is revealed that joint effects of diffusion and delay can lead to the occurrence of mixed spatial and temporal patterns. Moreover, it is also demonstrated that various spatially inhomogeneous patterns with different spatial frequencies can be achieved via changing the diffusion rate. And, the phenomenon that time delay may induce a failure of Turing instability observed by Gaffney and Monk (2006) are theoretically explained.

Abstract: Turing instability and pattern formation in a super cross-diffusion predator-prey system with Michaelis-Menten type predator harvesting are investigated. Stability of equilibrium points is first explored with or without super cross-diffusion. It is found that cross-diffusion could induce instability of equilibria. To further derive the conditions of Turing instability, the linear stability analysis is carried out. From theoretical analysis, note that cross-diffusion is the key mechanism for the formation of spatial patterns. By taking cross-diffusion rate as bifurcation parameter, we derive amplitude equations near the Turing bifurcation point for the excited modes by means of weakly nonlinear theory. Dynamical analysis of the amplitude equations interprets the structural transitions and stability of various forms of Turing patterns. Furthermore, the theoretical results are illustrated via numerical simulations.

Abstract: This work was supported by The Alan Turing Institute under the UK EPSRC grant EP/N510129/1, and by the EPSRC grants EP/R013667/1, EP/L012138/1, and EP/M025268/1.

Abstract: The paper mainly majors in question answering system(QA). The original natural response theory originated from Alan Turing's Turing Machine Theory in 1950. Nowadays, the best way to implement a QA system is the Deep Learning. This project is based on the Seq2Seq model theory, I design and implement an automatic question answering system model based on LSTM-RNN algorithm. The paper completely describes the framework and design ideas of the entire system. it realizes the following aspects:1)A Seq2Seq model based on LSTM-RNN.2) We design the QA framework adapts to different chat scene. Results show that the average of perplexity is 2.92. And model's loss is 1.07. To some extent, it reduces the resistance of deep learning in developing. The design and implementation of the framework simplifies the development process and facilitates the user's own data set

Abstract: Synthesizing a genetic network which generates stable Turing patterns is one of the great challenges of synthetic biology, but a significant obstacle is the disconnect between the mathematical theory and the biological reality. Current mathematical understanding of patterning is typically restricted to systems of two or three chemical species, for which equations are tractable. However, when models seek to combine descriptions of intercellular signal diffusion and intracellular biochemistry, plausible genetic networks can consist of dozens of interacting species. In this paper, we suggest a method for reducing large biochemical systems that relies on removing the non-diffusible species, leaving only the diffusibles in the model. Such model reduction enables analysis to be conducted on a smaller number of differential equations. We provide conditions to guarantee that the full system forms patterns if the reduced system does, and vice versa. We confirm our technique with three examples: the Brusselator, an example proposed by Turing, and a biochemically plausible patterning system consisting of 17 species. These examples show that our method significantly simplifies the study of pattern formation in large systems where several species can be considered immobile.

Abstract: The Turing patterning mechanism is believed to underly the formation of repetitive structures in development, such as zebrafish stripes and mammalian digits, but it has proved difficult to isolate the specific biochemical species responsible for pattern formation. Meanwhile, synthetic biologists have designed Turing systems for implementation in cell colonies, but none have yet led to visible patterns in the laboratory. In both cases, the relationship between underlying chemistry and emergent biology remains mysterious. To help resolve the mystery, this article asks the question: what kinds of biochemical systems can generate Turing patterns? We find general conditions for Turing pattern inception -- the ability to generate unstable patterns from random noise -- which may lead to the ultimate formation of stable patterns, depending on biochemical non-linearities. We find that a wide variety of systems can generate stable Turing patterns, including several which are currently unknown, such as two-species systems composed of two self-activators, and systems composed of a short-range inhibitor and a long-range activator. We furthermore find that systems which are widely believed to generate stable patterns may in fact only generate unstable patterns, which ultimately converge to spatially-homogeneous concentrations. Our results suggest that a much wider variety of systems than is commonly believed could be responsible for observed patterns in development, or could be good candidates for synthetic patterning networks.

Abstract: Computation models and specification methods seem to be worlds apart. The project on abstract state machines (in short ASMs, also known as evolving algebras) started as an attempt to bridge the gap by improving on Turing's thesis. We sought more versatile machines which would be able to step-for-step simulate arbitrary algorithms on their natural abstraction levels. The ASM thesis asserts that ASMs are such versatile machines. The guide provides the definitions of sequential, parallel and distributed ASMs.

Abstract: In this paper, stability analysis is applied to a discrete Lotka–Volterra cooperative system with the periodic boundary conditions, then Turing pattern formation conditions can be derived, theory analysis and numerical simulation show that turing patterns can be realized. In addition, we also pay attention on what reason or what system environment to result into the current state patterns, which can be reduced to estimate or identify the system parameter. A regularization method is applied to parameter inversion, and numerical simulation can verify the effectiveness of the algorithm.

Abstract: Bacterial computing is a known candidate in natural computing, the aim being to construct "bacterial computers" for solving complex problems. In this paper, a new kind of bacterial computing system, named the bacteria and plasmid computing system (BP system), is proposed. We investigate the computational power of BP systems with finite numbers of bacteria and plasmids. Specifically, it is obtained in a constructive way that a BP system with 2 bacteria and 34 plasmids is Turing universal. The results provide a theoretical cornerstone to construct powerful bacterial computers and demonstrate a concept of paradigms using a "reasonable" number of bacteria and plasmids for such devices.

Abstract: Although Alan Turing has been cast as a thinker who separates mind and body, this article approaches his technical writing anew through the theoretical lenses of embodied rhetoric and queer rhetoric. Alan Turing’s technical and theoretical writings are shown to be lively with embodied, gendering, and queer rhetoric. This article also argues that queer, embodied experiences ground Turing’s contributions toward early digital computation. Turing’s rhetoric resists norms in technical communication that expect stable and complete knowledge. Instead, Turing is an outlier who reminds us that queer, embodied rhetorics can complicate and expand our understanding of technical and scientific communication.

Abstract: Herbert A. Simon and Alan Newell won the Turing Award jointly in Computer Science for foundational work on Artificial Intelligence. Simon also won the Nobel Prize in Economics for the concept of “bounded rationality.” In both cases, the same heuristic was deemed fundamental: “Search till a satisfactory solution is found.” We argue that behavioral strategy can learn a great deal from the Theory of Computational Complexity and Artificial Intelligence. These fields can provide a sounder theoretical grounding for bounded rationality and for the necessity and usefulness of heuristics. Finally, a concept of “organizational intractability” based roughly on the metaphor provided by the Theory of Computational Complexity may be useful in determining what analytical decision technologies are actually intractable in real organizations with constraints on time and managerial attention.

Abstract: This work has been partially supported by the EC-funded H2020 projects QT21 (grant no. 645452) and ModernMT (grant no. 645487). This work was also supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1 and by a donation of Azure credits by Microsoft.

Abstract: We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract. We design a novel Writing-editing Network that can attend to both the title and the previously generated abstract drafts and then iteratively revise and polish the abstract. With two series of Turing tests, where the human judges are asked to distinguish the system-generated abstracts from human-written ones, our system passes Turing tests by junior domain experts at a rate up to 30% and by non-expert at a rate up to 80%.

Abstract: David Hume, the Scottish philosopher, conceives reason as the slave of the passions, which implies that human reason has predetermined objectives it cannot question. An essential element of an algorithm running on a computational machine (or Logical Computing Machine, as Alan Turing calls it) is its having a predetermined purpose: an algorithm cannot question its purpose, because it would cease to be an algorithm. Therefore, if self-determination is essential to human intelligence, then human beings are neither Humean beings, nor computational machines. We examine also some objections to the Turing Test as a model to understand human intelligence.

Abstract: Alan Turing (1912–1954) is widely acknowledged as a genius. As well as codebreaking during World War II and taking a pioneering role in computer hardware design and software after the War, he also wrote three important foundational papers in the fields of theoretical computer science, artificial intelligence, and mathematical biology. He has been called the father of computer science, but he also admired by mathematicians, philosophers, and perhaps more surprisingly biologists, for his wide-ranging ideas. His influence stretches from scientific to cultural and even political impact. For all these reasons, he was a true polymath. This paper considers the genius of Turing from various angles, both scientific and artistic. The four authors provide position statements on how Turing has influenced and inspired their work, together with short biographies, as a starting point for a panel session and visual music performance.

Abstract: “Our life is half natural and half technological. Half-and-half is good. You cannot deny that high-tech is progress. We need it for jobs. Yet if you make only high-tech, you make war. So we must have a strong human element to keep modesty and natural life.” — Nam June Paik 1 My present work focuses on the new relationship generated by electronic information between the virtual archive (the Web in a broad sense, certain specialized archives in particular) and its referent (material reality in general, museums, inter-art practices, and artworks in particular). It proposes that the relationship between information, its representation and the referent (or in other words, the relation between reality and the conceptual construction of reality) has to be re-thought. Gabriela Galati University of Plymouth-Planetary Collegium gabriela.galati@plymouth.ac.uk The Electronic Representation of Information: New Relationships between the Virtual Archive and its (Possible) Referent

Abstract: This work was supported by The Alan Turing Institute under the UK EPSRC grant EP/N510129/1, and by the EPSRC grants EP/R013667/1, EP/L012138/1, and EP/M025268/1

Abstract: In its original form, the Church-Turing thesis concerned computation as Alan Turing and Alonzo Church used the term in 1936---human computation.

Abstract: This article introduces Turing’s idea of a “paper machine” to identify and understand one important mode of clinical research in the modern hospital, how that research worked, and how offic...

Abstract: Multi-species models play an important role in both ecology and mathematical ecology due to their practical relevance and universal existence. Some phenomena include but are not limited to osculating solutions behavior, multiple steady states and spatial patterns formation. In this article we study the numerical approximation of Turing patterns corresponding to the steady state solutions of systems of reaction-diffusion equations subject to zero-flux boundary conditions. We apply Chebyshev spectral methods which proved to be numerical methods that can significantly speed up the computation of systems of reaction-diffusion equations in the spatial part, while the temporal part is discretized using the Euler scheme in one dimension. For the evaluation of Turing instabilities and bifurcation of the steady state problem, we used the eigenvalues of the Jacobian matrix. The proposed scheme is then extended to the two-dimensional problem. We found that our numerical scheme is in very good agreement with other schemes available in the literature.

Abstract: Ethics and safety research in artificial intelligence is increasingly framed in terms of "alignment" with human values and interests. I argue that Turing's call for "fair play for machines" is an early and often overlooked contribution to the alignment literature. Turing's appeal to fair play suggests a need to correct human behavior to accommodate our machines, a surprising inversion of how value alignment is treated today. Reflections on "fair play" motivate a novel interpretation of Turing's notorious "imitation game" as a condition not of intelligence but instead of value alignment: a machine demonstrates a minimal degree of alignment (with the norms of conversation, for instance) when it can go undetected when interrogated by a human. I carefully distinguish this interpretation from the Moral Turing Test, which is not motivated by a principle of fair play, but instead depends on imitation of human moral behavior. Finally, I consider how the framework of fair play can be used to situate the debate over robot rights within the alignment literature. I argue that extending rights to service robots operating in public spaces is "fair" in precisely the sense that it encourages an alignment of interests between humans and machines.