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Hello World: Being Human in the Age of Algorithms (Hannah Fry)

via Hello World: Being Human in the Age of Algorithms (Hannah Fry)

A look inside the algorithms that are shaping our lives and the dilemmas they bring with them.

If you were accused of a crime, who would you rather decide your sentence―a mathematically consistent algorithm incapable of empathy or a compassionate human judge prone to bias and error? What if you want to buy a driverless car and must choose between one programmed to save as many lives as possible and another that prioritizes the lives of its own passengers? And would you agree to share your family’s full medical history if you were told that it would help researchers find a cure for cancer?

Annunci
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On the Extraordinary Importance of Complexity

via On the Extraordinary Importance of Complexity

Of all physical quantities, energy is probably the most important. Energy expresses the capacity of a body or a system to perform work. Nature works by using energy to transform matter. This is done via processes (physical, chemical, etc.). However, in order to realize these processes it is necessary to have information. Energy on its own is not sufficient. One must know what to do with it and how to do it. This is where information comes into the picture. Information is stored and delivered in a variety of ways. The DNA, for example, encodes biological information.

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Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

Abstract Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al., 2016). Psychlab enables implementations of classical laboratory psychological experiments so that they work with both human and artificial agents. Psychlab has a simple and flexible API that enables users to easily create their own tasks. As examples, we are releasing Psychlab implementations of several classical experimental paradigms including visual search, change detection, random dot motion discrimination, and multiple object tracking. We also contribute a study of the visual psychophysics of a specific state-of-the-art deep reinforcement learning agent: UNREAL (Jaderberg et al., 2016). This study leads to the surprising conclusion that UNREAL learns more quickly about larger target stimuli than it does about smaller stimuli. In turn, this insight motivates a specific improvement in the form of a simple model of foveal vision that turns out to significantly boost UNREAL’s performance, both on Psychlab tasks, and on standard DeepMind Lab tasks. By open-sourcing Psychlab we hope to facilitate a range of future such studies that simultaneously advance deep reinforcement learning and improve its links with cognitive science.

https://arxiv.org/pdf/1801.08116.pdf

DeepMind, London, UK February 6, 2018

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Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

via Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network
Link prediction is one of the fundamental research problems in network analysis. Intuitively, it involves identifying the edges that are most likely to be added to a given network, or the edges that appear to be missing from the network when in fact they are present. Various algorithms have been proposed to solve this problem over the past decades. For all their benefits, such algorithms raise serious privacy concerns, as they could be used to expose a connection between two individuals who wish to keep their relationship private. With this in mind, we investigate the ability of such individuals to evade link prediction algorithms.

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Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

Complexity Digest

Link prediction is one of the fundamental research problems in network analysis. Intuitively, it involves identifying the edges that are most likely to be added to a given network, or the edges that appear to be missing from the network when in fact they are present. Various algorithms have been proposed to solve this problem over the past decades. For all their benefits, such algorithms raise serious privacy concerns, as they could be used to expose a connection between two individuals who wish to keep their relationship private. With this in mind, we investigate the ability of such individuals to evade link prediction algorithms. More precisely, we study their ability to strategically alter their connections so as to increase the probability that some of their connections remain unidentified by link prediction algorithms. We formalize this question as an optimization problem, and prove that finding an optimal solution is NP-complete. Despite…

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Entropy | Special Issue : Information Theory in Complex Systems

Entropy | Special Issue : Information Theory in Complex Systems

Complex systems are ubiquitous in the natural and engineered worlds. Examples are self-assembling materials, the Earth’s climate, single- and multi-cellular organisms, the brain, and coupled socio-economic and socio-technical systems, to mention a few canonical examples. The use of Shannon information theory to study the behavior of such systems, and to explain and predict their dynamics, has gained significant attention, both from a theoretical and from an experimental viewpoint. There have been many advances in applying Shannon theory to complex systems, including correlation analyses for spatial and temporal data and construction and clustering techniques for complex networks. Progress has often been driven by the application areas, such as genetics, neurosciences, and the Earth sciences.

The application of Shannon theory to data of real-world complex systems are often hindered by the frequent lack of stationarity and sufficient statistics. Further progress on this front call for new statistical techniques based on Shannon information theory, for the sophistication of known techniques, as well as for an improved understanding of the meaning of entropy in complex systems. Contributions addressing any of these issues are very welcome.

This Special Issue aims to be a forum for the presentation of new and improved techniques of information theory for complex systems. In particular, the analysis and interpretation of real-world natural and engineered complex systems with the help of statistical tools based on Shannon information theory fall within the scope of this Special Issue.

Source: www.mdpi.com

Architecture and design for resilient networked systems

There is a need for new architectures and designs of resilient networked systems that are capable of supporting critical services and infrastructures. The arguments have previously been well rehearsed, but much remains to be done, not least to demonstrate the feasibility of building such systems.

Key among the remaining challenges is how to specify and realise appropriate components that interact with each other to produce a resulting resilient system. This paper reviews the state of the art, describes recent contributions, and looks ahead to future research and prospects.

 

Architecture and design for resilient networked systems
David Hutchison, James P.G.Sterbenz

Computer Communications

Source: www.sciencedirect.com

Geometrical effects on mobility

In this paper we analyze the effect of randomly deleting streets of a synthetic city on the statistics of displacements. Our city is constituted initially by a set of streets that form a regular tessellation of the euclidean plane. Therefore we will have three types of cities, formed by squares, triangles or hexagons. We studied the complementary cumulative distribution function for displacements (CCDF). For the whole set of streets the CCDF is a stretched exponential, and as streets are deleted this function becomes a linear function and then two clear different exponentials. This behavior is qualitatively the same for all the tessellations. Most of this functions has been reported in the literature when studying the displacements of individuals based on cell data trajectories and GPS information. However, in the light of this work, the appearance of different functions for displacements CCDF can be attributed to the connectivity of the underlying street network. It is remarkably that for some proportion of streets we got a linear function for such function, and as far as we know this behavior has not been reported nor considered. Therefore, it is advisable to analyze experimental in the light of connectivity of the street network to make correlations with the present work.

 

Geometrical effects on mobility
Jorge H. Lopez

Source: arxiv.org

On the networked architecture of genotype spaces and its critical effects on molecular evolution

Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, sharing deep qualitative and quantitative similarities with slowly driven physical systems: nonlinear responses analogous to critical transitions, sudden state changes or hysteresis, among others. Furthermore, the phenotypic plasticity inherent to genotypes transforms classical fitness landscapes into multiscapes where adaptation in response to an environmental change may be very fast. The quantitative nature of adaptive molecular processes is deeply dependent on a network-of-networks multilayered structure of the map from genotype to function that we begin to unveil.

 

On the networked architecture of genotype spaces and its critical effects on molecular evolution
Jacobo Aguirre, Pablo Catalán, José A. Cuesta, Susanna Manrubia

Open Biology

Published 4 July 2018.DOI: 10.1098/rsob.180069

Source: rsob.royalsocietypublishing.org

Older posts

via Entropy | Special Issue : Information Theory in Complex Systems

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Swarm Robotics – Pushing the state of the art

Complexity Digest

Swarm robotics is the domain of robotics that deals with large groups of robots that coordinately and cooperatively accomplish a task.
Inspired by natural self-organising systems like ant colonies, fish schools or bird flocks, the goal of swarm robotics research is to deploy complex robotics systems that present robustness to faults, scalability to different group sizes, flexibility of the displayed behaviour and adaptivity to environmental changes.
The problems faced by swarm robotics concerns mainly the analysis of complex systems formed by a multitude of interacting units, the design of individual level rules leading to desired collective behaviours, and the application of the lessons learned in lab research to real-world domains. The workshop will discuss cutting-edge research in all these directions thanks to a great assembly of invited speakers, and will represent a unique place where to trace the future of this research field beyond the state of the art. 

 

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