Cognitive Science - Artificial Intelligence
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The Informative Herd: why humans and other animals imitate more when conditions are adverse

Decisions in a group often result in imitation and aggregation, which are enhanced in panic, dangerous, stressful or negative situations. Current explanations of this enhancement are restricted to particular contexts, such as anti-predatory behavior, deflection of responsibility in humans, or cases in which the negative situation is associated with an increase in uncertainty. But this effect is observed across taxa and in very diverse conditions, suggesting that it may arise from a more general cause, such as a fundamental characteristic of social decision-making. Current decision-making theories do not explain it, but we noted that they concentrate on estimating which of the available options is the best one, implicitly neglecting the cases in which several options can be good at the same time. We explore a more general model of decision-making that instead estimates the probability that each option is good, allowing several options to be good simultaneously. This model predicts with great generality the enhanced imitation in negative situations. Fish and human behavioral data showing an increased imitation behavior in negative circumstances are well described by this type of decisions to choose a good option.

 

The Informative Herd: why humans and other animals imitate more when conditions are adverse
Alfonso Pérez-Escudero, Gonzalo G. de Polavieja

http://arxiv.org/abs/1403.7478


Via Complexity Digest, António F Fonseca
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António F Fonseca's curator insight, April 4, 2014 5:02 AM

I believe logic emerges from imitation.

Cognitive Science - Artificial Intelligence
Cognitive science is the interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does and how it works. It includes research on intelligence and behavior, especially focusing on how information is represented, processed, and transformed (in faculties such as perception, language, memory, reasoning, and emotion) within nervous systems (human or other animal) and machines (e.g. computers). Cognitive science consists of multiple research disciplines, including psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology. The fundamental concept of cognitive science is "that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures." Wikipedia (en)
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Rescooped by Bernard Ryefield from University-Lectures-Online
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MLS Academy: A curated list of Free Lectures on Artificial Intelligence, Bioinformatics, Neuronal Networks and Machine Learning

MLS Academy: A curated list of Free Lectures on Artificial Intelligence, Bioinformatics, Neuronal Networks and Machine Learning | Cognitive Science - Artificial Intelligence | Scoop.it

A curated list of Free Lecture Resources. Enjoy!


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Practical Machine Learning with R and Python (6 Parts)

Practical Machine Learning with R and Python (6 Parts) | Cognitive Science - Artificial Intelligence | Scoop.it

This is the final and concluding part of my series on ‘Practical Machine Learning with R and Python’. Included are Machine Learning algorithms in R and Python. The algorithms implemented are:

Practical Machine Learning with R and Python – Part 1 The student will learn regression of a continuous target variable. Specifically Univariate, Multivariate, Polynomial regression and KNN regression in both R and Python.Practical Machine Learning with R and Python – Part 2  The Focus is on Logistic Regression, KNN classification and Cross Validation error for both LOOCV and K-Fold in both R and Python.Practical Machine Learning with R and Python – Part 3 This 3rd part includes feature selection in Machine Learning. Specifically, best fit, forward fit, backward fit, ridge(L2 regularization) & lasso (L1 regularization). It contains equivalent code in R and Python.Practical Machine Learning with R and Python – Part 4 In this part, SVMs, Decision Trees, Validation, Precision-Recall, AUC and ROC curves are being discussed.Practical Machine Learning with R and Python – Part 5  This part touches upon B-splines, natural splines, smoothing splines, Generalized Additive Models (GAMs), Decision Trees, Random Forests and Gradient Boosted Trees.Practical Machine Learning with R and Python - Part6 This last part covers Unsupervised Machine Learning, specifically the implementations of Principal Component Analysis (PCA), K-Means and Heirarchical Clustering. The R Markdown file can be downloaded from Github.
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Resisting Reduction:  Designing our Complex Future with Machines 

Resisting Reduction:  Designing our Complex Future with Machines  | Cognitive Science - Artificial Intelligence | Scoop.it
While I had long been planning to write a manifesto against the technological singularity and launch it into the conversational sphere for public reaction and comment, an invitation earlier this year from John Brockman to read and discuss The Human Use of Human Beings by Norbert Wiener with him and his illustrious group of thinkers as part of an ongoing collaborative book project contributed to the thoughts contained herein. The essay below is now phase 1 of an experimental, open publishing project in partnership with the MIT Press. In phase 2, a new version of the essay enriched and informed by input from open commentary will be published online, along with essay length contributions by others inspired by the seed essay, as a new issue of the Journal of Design and Science. In phase 3, a revised and edited selection of these contributions will be published as a print book by the MIT Press. Version 1.0
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Why Neuroscientists Need to Study the Crow - Issue 40: Learning - Nautilus

Why Neuroscientists Need to Study the Crow - Issue 40: Learning - Nautilus | Cognitive Science - Artificial Intelligence | Scoop.it

The neocortex is argued to be the seat of cognition, but crows don't have one.

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[1507.01122] Modeling the Mind: A brief review

[1507.01122] Modeling the Mind: A brief review | Cognitive Science - Artificial Intelligence | Scoop.it
Part 1 in the Modeling the Mind review series. 52 pages, 5 figures

 Modeling the Mind: a brief review is an annual review, available for free on arXiv.org, whose aim is to help students and researchers unfamiliar with the field of neuroscience and computational neuroscience gain insight into the fundamentals of this domain of study. Creating an accurate simulation of the mind is no easy task, and while it took brilliant minds decades to advance us to where we’re at right now, we are still ways off our final goal. It is therefore imperative to have more research carried out in this multidisciplinary field, taking in help from researchers in biology, neuroscience, computer science, but also mathematics, physics, chemistry and imaging, in order to speed up this process and tip the scales in our favor for the upcoming decades. This annual review hopes to provide the required information for anyone who is considering this domain as his future endeavor. The reviews will be tackling relatively global characteristics at first in order to familiarize the reader with the basic foundations, and will be getting progressively more specific and in tune with current research in the upcoming parts. This is Part I. It will contain basic information about the computational aspect of this field, and will attempt to explain why certain concepts are generally agreed upon, and the intuition behind them, going through the essential founding works. 

 The brain is a powerful tool used to achieve amazing feats. There have been several significant advances in neuroscience and artificial brain research in the past two decades. This article is a review of such advances, ranging from the concepts of connectionism, to neural network architectures and high-dimensional representations. There have also been advances in biologically inspired cognitive architectures of which we will cite a few. We will be positioning relatively specific models in a much broader perspective, while comparing and contrasting their advantages and weaknesses. The projects presented are targeted to model the brain at different levels, utilizing different methodologies.
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Rescooped by Bernard Ryefield from Exploration de données
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This fake Rembrandt was created by an algorithm

This fake Rembrandt was created by an algorithm | Cognitive Science - Artificial Intelligence | Scoop.it
"The work was created by teams from Dutch museums Mauritshuis and Rembranthuis, alongside Microsoft, ING and the Delft University of Technology. Creating a faithful replication of a Rembrandt painting required huge amounts of data, with the team describing it was a "marriage" between technology and art. (...)
With the help of several art experts, 346 Rembrandt paintings – digitised using 3D scans – were analysed by a deep learning algorithm. The algorithm isolated common Rembrandt subjects to create the "most consistent subject" – a white, middle aged man with facial hair, "wearing black clothes with a white collar and a hat". "


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AI & The Future Of Civilization , A Conversation With Stephen Wolfram | Edge.org

AI & The Future Of Civilization
, A Conversation With Stephen Wolfram | Edge.org | Cognitive Science - Artificial Intelligence | Scoop.it
What makes us different from all these things? What makes us different is the particulars of our history, which gives us our notions of purpose and goals.
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Would you bet against sex robots? AI 'could leave half of world unemployed'

Would you bet against sex robots? AI 'could leave half of world unemployed' | Cognitive Science - Artificial Intelligence | Scoop.it
Computer scientist Moshe Vardi tells colleagues that change could come within 30 years, raising the question: ‘What will humans do?’
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Memory capacity of brain is 10 times more than previously thought - Salk Institute for Biological Studies

Memory capacity of brain is 10 times more than previously thought - Salk Institute for Biological Studies | Cognitive Science - Artificial Intelligence | Scoop.it
LA JOLLA—Salk researchers and collaborators have achieved critical insight into the size of neural connections, putting the memory capacity of the brain far higher than common estimates. The new work also answers a longstanding question as to how the brain is so energy efficient and could help engineers build computers that are incredibly powerful but also conserve energy.
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Jim Gunderson's curator insight, January 24, 2016 10:43 AM

How does this impact the oft estimated singularity timeline?

Simone Fin's curator insight, February 8, 2016 7:10 PM

aggiungi la tua intuizione ...

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The Illusion Machine That Teaches Us How We See - Issue 32: Space - Nautilus

The Illusion Machine That Teaches Us How We See - Issue 32: Space - Nautilus | Cognitive Science - Artificial Intelligence | Scoop.it
The man sprang onstage dressed as a miner, complete with headlamp and pickaxe. After swinging the axe a few times, he proclaimed to…
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A Turing test for collective motion

A Turing test for collective motion | Cognitive Science - Artificial Intelligence | Scoop.it

A widespread problem in biological research is assessing whether a model adequately describes some real-world data. But even if a model captures the large-scale statistical properties of the data, should we be satisfied with it? We developed a method, inspired by Alan Turing, to assess the effectiveness of model fitting. We first built a self-propelled particle model whose properties (order and cohesion) statistically matched those of real fish schools. We then asked members of the public to play an online game (a modified Turing test) in which they attempted to distinguish between the movements of real fish schools or those generated by the model. Even though the statistical properties of the real data and the model were consistent with each other, the public could still distinguish between the two, highlighting the need for model refinement. Our results demonstrate that we can use ‘citizen science’ to cross-validate and improve model fitting not only in the field of collective behaviour, but also across a broad range of biological systems.

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It’s Harder to Empathize with People If You’ve Been in Their Shoes

It’s Harder to Empathize with People If You’ve Been in Their Shoes | Cognitive Science - Artificial Intelligence | Scoop.it
Research reveals a strange gap.
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Why We Kill for Symbols

Why We Kill for Symbols | Cognitive Science - Artificial Intelligence | Scoop.it

Metaphors Are Us: War, murder, music, art. We would have none without metaphor.

BY ROBERT SAPOLSKY
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The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities 

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities  | Cognitive Science - Artificial Intelligence | Scoop.it
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.
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Network Neuroscience Theory of Human Intelligence

Network Neuroscience Theory of Human Intelligence | Cognitive Science - Artificial Intelligence | Scoop.it

An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. This Opinion article surveys recent neuroscience evidence to elucidate how general intelligence, g, emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g, represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation. 


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 Accumulating evidence from network neuroscience indicates that g depends on the dynamic reorganization of brain networks, modifying their topology and community structure in the service of system-wide flexibility and adaptation. Whereas crystallized intelligence engages easy-to-reach network states that access prior knowledge and experience, fluid intelligence recruits difficult-to-reach network states that support cognitive flexibility and adaptive problem-solving. The capacity to flexibly transition between networks states therefore provides the basis for g – enabling rapid information exchange across networks and capturing individual differences in information processing at a global level. This framework sets the stage for new approaches to understanding the neural foundations of g, examining individual differences in brain network topology and dynamics.

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Making computers explain themselves

Making computers explain themselves | Cognitive Science - Artificial Intelligence | Scoop.it
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have devised a way to train neural networks so that they provide not only predictions and classifications but rationales for their decisions.Illustration: Christine Daniloff/MIT
New training technique would revea
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How Artificial Superintelligence Will Give Birth To Itself

How Artificial Superintelligence Will Give Birth To Itself | Cognitive Science - Artificial Intelligence | Scoop.it
There's a saying among futurists that a human-equivalent artificial intelligence will be our last invention. After that, AIs will be capable of designing virtually anything on their own — including themselves. Here's how a recursively self-improving AI could transform itself into a superintelligent machine.
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Creative machines: The next frontier in artificial intelligence

Creative machines: The next frontier in artificial intelligence | Cognitive Science - Artificial Intelligence | Scoop.it

Artificial intelligence is making huge advances but it still struggles to be creative or collaborate with us.

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Machines are becoming more creative than humans

Machines are becoming more creative than humans | Cognitive Science - Artificial Intelligence | Scoop.it
Can machines be creative? Recent successes in AI have shown that machines can now perform at human levels in many tasks that, just a few years ago, were considered to be decades away, like driving cars, understanding spoken language, and recognizing objects. But these are all tasks where we know what needs to be done, and the machine is just imitating us. What about tasks where the right answers are not known? Can machines be programmed to find solutions on their own, and perhaps even come up with creative solutions that humans would find difficult?
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The Future of Employment: How susceptible are jobs to computerisation?

The Future of Employment: How susceptible are jobs to computerisation? | Cognitive Science - Artificial Intelligence | Scoop.it

he authors examine how susceptible jobs are to computerisation, by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, they examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupation’s probability of computerisation, wages and educational attainment. According to their estimates, about 47 per cent of total US employment is at risk. They further provide evidence that wages and educational attainment exhibit a strong negative relationship with an occupation’s probability of computerisation.

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Searching for the Algorithms Underlying Life | Quanta Magazine

Searching for the Algorithms Underlying Life |  Quanta Magazine | Cognitive Science - Artificial Intelligence | Scoop.it

The biological world is computational at its core, argues computer scientist Leslie Valiant. His “ecorithm” approach uses computational concepts to explore fundamental mysteries of evolution and the mind.

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Rescooped by Bernard Ryefield from Self-organizing, Systems and Complexity
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Mental Modeler - Fuzzy Logic Cognitive Mapping

Mental Modeler - Fuzzy Logic Cognitive Mapping | Cognitive Science - Artificial Intelligence | Scoop.it

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june holley's curator insight, January 21, 2016 7:43 AM

Example of a simple web-based tool that can be used by communities to look at implications of decisions. 

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Bayes's Theorem: What's the Big Deal?

Bayes's Theorem: What's the Big Deal? | Cognitive Science - Artificial Intelligence | Scoop.it
Bayes’s theorem, touted as a powerful method for generating knowledge, can also be used to promote superstition and pseudoscience
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Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks | Cognitive Science - Artificial Intelligence | Scoop.it

Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies.

 (Illustrative videos can be downloaded from here:this http URL)

 

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks
Peter Csermely

http://arxiv.org/abs/1511.01238 ;


Via Complexity Digest, Bernard Ryefield
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Complexity Digest's curator insight, November 18, 2015 6:13 PM

See Also: http://networkdecisions.linkgroup.hu 

António F Fonseca's curator insight, November 23, 2015 3:30 AM

Interesting  paper about fast cores and slow periphery,  conflict in the elite vs democratic consensus.

Marcelo Errera's curator insight, November 24, 2015 11:32 AM

Yes, there must be few fasts and many slows.  It's been predicted by CL in many instances.

 

http://www.researchgate.net/publication/273527384_Constructal_Law_Optimization_as_Design_Evolution

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Neuroscience: Connectomes make the map : Nature : Nature Publishing Group

Neuroscience: Connectomes make the map : Nature : Nature Publishing Group | Cognitive Science - Artificial Intelligence | Scoop.it
Working at a variety of scales and with disparate organisms and technologies, researchers are mapping how parts of the brain connect.
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