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Cybernetics and Information Theory in the United States, France and the Soviet Union

Mindell et al. (2003) «Cybernetics and Information Theory in the United States, France and the Soviet Union» http://t.co/WxxvghtyUz

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António F Fonseca's curator insight, March 1, 7:58 AM

Very interesting document!

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Origin of symbol-using systems: speech, but not sign, without the semantic urge

Natural language—spoken and signed—is a multichannel phenomenon, involving facial and body expression, and voice and visual intonation that is often used in the service of a social urge to communicate meaning. Given that iconicity seems easier and less abstract than making arbitrary connections between sound and meaning, iconicity and gesture have often been invoked in the origin of language alongside the urge to convey meaning. To get a fresh perspective, we critically distinguish the origin of a system capable of evolution from the subsequent evolution that system becomes capable of. Human language arose on a substrate of a system already capable of Darwinian evolution; the genetically supported uniquely human ability to learn a language reflects a key contact point between Darwinian evolution and language. Though implemented in brains generated by DNA symbols coding for protein meaning, the second higher-level symbol-using system of language now operates in a world mostly decoupled from Darwinian evolutionary constraints. Examination of Darwinian evolution of vocal learning in other animals suggests that the initial fixation of a key prerequisite to language into the human genome may actually have required initially side-stepping not only iconicity, but the urge to mean itself. If sign languages came later, they would not have faced this constraint.

 

Origin of symbol-using systems: speech, but not sign, without the semantic urge
Martin I. Sereno

http://dx.doi.org/10.1098/rstb.2013.0303
Phil. Trans. R. Soc. B 19 September 2014 vol. 369 no. 1651 20130303


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Ten Simple Rules of Live Tweeting at Scientific Conferences

Ten Simple Rules of Live Tweeting at Scientific Conferences | collectibles from scoop.it | Scoop.it

Rule 1: Short Conference Hashtag
Rule 2: Promote the Hashtag
Rule 3: Encourage Tweeting
Rule 4: Conference Twitter Etiquette
Rule 5: Conference Tweet Layout
Rule 6: Keep Conference Discussion Flowing
Rule 7: Differentiate Your Opinions from the Speaker's
Rule 8: Bring Questions up from Outside
Rule 9: Meet Other Live Tweeters Face to Face
Rule 10: Emphasize Impact of Live Tweeting

 

Ekins S, Perlstein EO (2014) Ten Simple Rules of Live Tweeting at Scientific Conferences. PLoS Comput Biol 10(8): e1003789. http://dx.doi.org/10.1371/journal.pcbi.1003789


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JIDT: An information-theoretic toolkit for studying the dynamics of complex systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave and Python. We present the principles behind the code design, and provide several examples to guide users

 

"JIDT: An information-theoretic toolkit for studying the dynamics of complex systems"
Joseph T. Lizier, arXiv:1408.3270, 2014
http://arxiv.org/abs/1408.3270


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Eli Levine's curator insight, August 19, 11:11 AM

This could be useful.

Rescooped by Vasileios Basios from Complexity - Complex Systems Theory
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Self-organized criticality: An explanation of the 1/f noise

We show that dynamical systems with spatial degrees of freedom naturally evolve into a self-organized critical point. Flicker noise, or 1/f noise, can be identified with the dynamics of the critical state. This picture also yields insight into the origin of fractal objects.


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Bernard Ryefield's curator insight, August 12, 10:45 AM

seminal paper on self-organized criticality

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The dynamics of correlated novelties

The dynamics of correlated novelties | collectibles from scoop.it | Scoop.it
Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called “expanding the adjacent possible”. The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.

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Sand Pile Model of the Mind Grows in Popularity

Sand Pile Model of the Mind Grows in Popularity | collectibles from scoop.it | Scoop.it
Support is growing for a decades-old physics idea suggesting that localized episodes of disordered brain activity help keep the overall system in healthy balance

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Soul Spelunker » Worlds of Being and Meaning

Soul Spelunker » Worlds of Being and Meaning | collectibles from scoop.it | Scoop.it

Our ego-centered culture has not yet grasped the fact that the archetypal structures of all reality are these worlds of being and meaning. We do not live in these worlds; these worlds live through us.  To the degree that we recognize the Powers who manifest through our lives, we can become that which we were meant to be.

 

These Powers are in conflict with each other. Throughout our lives, we undergo our very own Trojan War. Pathologization is the way of the soul. This conflict can be mediated by a “transcendent function,” which is the “transpersonal nature of the archetypal structures…it gives us an Archimedean point of leverage, a perspective on the world from the standpoint of the world whose name is that of a God or Goddess” (ibid.).


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Eva Rider's curator insight, August 10, 10:24 PM

More on the Transcendent Function: the apex of the sacred triangle: through the looking glass into the reality not yet imagined

 

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Jung and Pauli on I Ching

Scribd is the world's largest social reading and publishing site.

Via Maxwell Purrington, Paola Spadaro, Bonnie Bright
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for Fivos

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Jung and Teilhard de Chardin

Jung and Teilhard de Chardin | collectibles from scoop.it | Scoop.it

 "Teilhard de Chardin and Carl Jung never met. Dr. Jung did finish reading The Phenomenon of Man less than a month before his death. The book had both a great and visible impact upon Dr. Jung according to Michael Serrano. It must have been satisfying for Dr. Jung to see Teilhard de Chardin from the perspective of a paleontologist and Jesuit Priest reach the same empirical conclusions about the evolution of consciousness that Dr. Jung found in his empirical research in Depth Psychology.
It is a peculiar oddity that both Teilhard de Chardin and Dr. Jung stressed the empirical nature of their work yet often find their books categorized under "Philosophy" and "New Age." This in itself justifies the importance of this great book as the recognition of consciousness and the reality of the inner world of the soul is far from recognized even at the dawn of the 21st century" ~Lewis Lafontaine   Carl Jung was reading Teilhard de Chardin during the last few days of his life. According to Miguel Serrano, when he visited Jung on May 10, 1961, "On the small table beside the chair where Jung was sitting, was a book called The Human Phenomenon by Teilhard de Chardin. I asked Jung whether he had read it. 'It is a great book,' he said. His face was pale, but seemed strangely illuminated by an inner light." (Miguel Serrano, C.G. Jung and Hermann Hesse: A Record of Two Friendships [New York: Schocken Books, 1968] pp. 100-101) Jung died on June 6, 1961.


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SourceForge.net Introduces the PyCX Project for Scientific Visualization based on Python

SourceForge.net Introduces the PyCX Project for Scientific Visualization based on Python | collectibles from scoop.it | Scoop.it

PyCX is an online repository of simple, crude, easy-to-understand sample codes for various complex systems simulation, including iterative maps, cellular automata, dynamical networks and agent-based models. All the sample codes were written in plain Python, a general-purpose programming language widely used in industry as well as in academia, so that students can gain practical skills for both complex systems simulation and computer programming simultaneously. The core philosophy of PyCX is on the simplicity, readability, generalizability and pedagogical values of simulation codes. PyCX has been used in instructions of complex systems modeling at several places with successful outcomes.

 

Until nearly the end of the last century, dynamic simulations of complex systems—such as cellular automata and agent-based models—were only available to researchers who had sufficient technical skills to develop and operate their own simulation software. At that time, there were very few general-purpose simulation software packages available (e.g., (Hiebeler 1994; Wuensche 1994)), and those packages were rather hard to program, unless one had a computer science background. The lack of general-purpose simulation software easily accessible for non-computer scientists was a major limiting factor for the growth of complex systems science, given the highly interdisciplinary nature of the field.


Over the last decade, several easy-to-use complex systems modeling and simulation software packages have been developed and become widely used for scientific research, including NetLogo (2004), Repast (2003), Mason (2004) (for agent-based models) and Golly (2005) (for cellular automata). They have been playing a critical role in making complex systems modeling and simulation available to researchers outside computer science. A number of publications used these software packages as key research tools, and increasingly many online tutorials and sample simulation models are becoming publicly available.

 

However, such existing software has several problems when used for teaching complex systems modeling and simulation in higher education settings. These are all real issues we have faced in classrooms and other educational settings over the last several years. Firstly, and most importantly for college students, learning languages or libraries specific to particular simulation software used only in academia would not help the students advance their general technical skills. Because most students will eventually build their careers outside complex systems science, they usually want to learn something generalizable and marketable, even though they want to study complex systems science and they do appreciate its concepts and values.


Secondly, even for those who actively work on complex systems research, choices of preferred software vary greatly from discipline to discipline, and therefore it is quite difficult to come up with a single commonly agreeable choice of software useful for everyone. This is particularly problematic when one has to teach a diverse group of students, which is not uncommon in complex systems education.


Thirdly, details of model assumptions and implementations in pre-built simulation software are often hidden from the user, such as algorithms of collision detection, time allocation and state updating schemes. As we all know, such microscopic details can and do influence macroscopic behavior of the model, especially in complex systems simulations.

 

Finally, using existing simulation software necessarily puts unrecognized limitations to the user’s creativity in complex systems research, because the model assumptions and analytical methods are influenced significantly by what is available in the provided software. This is a fundamental issue that could hamper the advance of complex systems science, since any breakthroughs will be achieved only by creating entirely novel ways of modeling and/or analyzing complex systems that were not done before.

 

These issues in using existing simulation software for complex systems education leads to the following very challenging riddle: Which computational tool is best for teaching complex systems modeling and simulation, offering students generalizable, marketable skills, being accessible and useful for everyone across disciplines, maintaining transparency in details, and imposing no implicit limit to the modeling and analysis capabilities?


Obviously, there would be no single best answer to this kind of question. In what follows, we present a case of our humble attempt to give our own answer to it, hoping that some readers may find it helpful for solving their unique challenges in complex systems education.


Through several years of experience in complex systems education, people have come to realize that using a simple general-purpose computer programming language itself as a complex systems modeling platform is our current best solution to address most, if not all, of the educational challenges discussed above. By definition, general-purpose computer programming languages are universal and can offer unlimited opportunity of modeling with all the details clearly spelled out in front of the user’s eyes. Identifying a programming language that would be easily accessible and useful in a wide variety of disciplines had been difficult even a decade ago. Fortunately, several easy-to-use programming languages have recently emerged and become very popular in various scientific and industrial communities, including Python and R.


Using the Python language itself as a modeling and simulation platform, “PyCX” has been developed, an online repository of simple, crude, easy-to-understand sample codes for various complex systems simulation. The target audiences of PyCX are researchers, scientists, and students who are interested in developing their own complex systems simulation software using a general-purpose programming language but do not have much experience in computer programming. Based on carefully designed sample codes, the audience can understand, modify, create and visualize dynamic complex systems simulations relatively easily.

 

The core philosophy of PyCX is therefore placed on the simplicity, readability, generalizability and pedagogical values of simulation codes. This is often achieved even at the cost of computational speed, efficiency or maintainability. For example: (1) every PyCX sample code is written as a single.py file, which is a plain text file, without being split into multiple separate files; (2) all the dynamic simulations follow the same scheme consisting of three parts (initialization, visualization and updating); (3) no object-oriented programming paradigm is used because it is sometimes difficult for non-computer scientists to grasp; and (4) no global variables are used to make the code more intuitive and readable. These choices were intentionally made based on our experience in teaching complex systems modeling and simulation to non-computer scientists and their feedback.

 


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Henry Corbin the Paradox of Monotheism

Henry Corbin sought to synthesize in one study these three works:

The first was "The Paradox of Monotheism," which was written for 1976 session of Eranos, where the theme was "The One and the Multiple."

The second was on the "Need for Angelogy," which was given at the Conference organized by the University of Tours (May 1977) on the topic of "The Angel and the Man."

The third was "Apophatic Theology as Antidote for Nihilism" given in Teheran (October 1977).

Alas, he passed to the other side without completing his work, which might have had given us a new and much needed integrated re-construction of Monotheism as a holistic doctrine.

Link;

http://www.amiscorbin.com/textes/anglais/paradoxofmonotheism.htm
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Properties of artificial networks evolved to contend with natural spectra

Properties of artificial networks evolved to contend with natural spectra | collectibles from scoop.it | Scoop.it

Understanding why spectra that are physically the same appear different in different contexts (color contrast), whereas spectra that are physically different appear similar (color constancy) presents a major challenge in vision research. Here, we show that the responses of biologically inspired neural networks evolved on the basis of accumulated experience with spectral stimuli automatically generate contrast and constancy. The results imply that these phenomena are signatures of a strategy that biological vision uses to circumvent the inverse optics problem as it pertains to light spectra, and that double-opponent neurons in early-level vision evolve to serve this purpose. This strategy provides a way of understanding the peculiar relationship between the objective world and subjective color experience, as well as rationalizing the relevant visual circuitry without invoking feature detection or image representation.

 


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Universality in network dynamics

Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system’s response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system’s dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system’s dynamics.

 

Universality in network dynamics
Baruch Barzel & Albert-László Barabási

Nature Physics 9, 673–681 (2013) http://dx.doi.org/10.1038/nphys2741


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Study of a model for the distribution of wealth

An equation for the evolution of the distribution of wealth in a population of economic agents making binary transactions with a constant total amount of "money" has recently been proposed by one of us (RLR). This equation takes the form of an iterated nonlinear map of the distribution of wealth. The equilibrium distribution is known and takes a rather simple form. If this distribution is such that, at some time, the higher momenta of the distribution exist, one can find exactly their law of evolution. A seemingly simple extension of the laws of exchange yields also explicit iteration formulae for the higher momenta, but with a major difference with the original iteration because high order momenta grow indefinitely. This provides a quantitative model where the spreading of wealth, namely the difference between the rich and the poor, tends to increase with time.

 

Study of a model for the distribution of wealth
Yves Pomeau, Ricardo Lopez-Ruiz

http://arxiv.org/abs/1408.2963


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Systems Thinking and the Future of Cities

Systems Thinking and the Future of Cities | collectibles from scoop.it | Scoop.it
The idea that nothing exists in isolation−but only as part of a system−has long been embedded in folklore, religious scriptures, and common sense.

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Erika Harrison's curator insight, August 15, 7:49 PM

In Brief

The idea that nothing exists in isolation−but only as part of a system−has long been embedded in folklore, religious scriptures, and common sense. Yet, systems dynamics as a science has yet to transform the way we conduct the public business. This article first briefly explores the question of why advances in systems theory have failed to transform public policy. The second part describes the ways in which our understanding of systems is growing−not so much from theorizing, but from practical applications in agriculture, building design, and medical science. The third part focuses on whether and how that knowledge and systems science can be deployed to improve urban governance in the face of rapid climate destabilization so that sustainability becomes the norm, not the occasional success story.


Key Concepts

Reducing wholes to parts lies at the core of the scientific worldview we inherited from Galileo, Bacon, Descartes, and their modern acolytes in the sciences of economics, efficiency, and management.The decades between 1950 and 1980 were the grand era for systems theory. However despite a great deal of talk about systems, we continue to administer, organize, analyze, manage, and govern complex ecological systems as if they were a collection of isolated parts and not an indissoluble union of energy, water, soils, land, forests, biota, and air.Much of what we have learned about managing real systems began in agriculture. One of the most important lessons being that land is an evolving organism of interrelated parts soils, hydrology, biota, wildlife, plants, animals, and people.The challenge is to transition organized urban complexity built on an industrial model and designed for automobiles, sprawl, and economic growth into coherent, civil, and durable places.A systems perspective to urban governance is a lens by which we might see more clearly through the fog of change, and potentially better manage the complex cause and effect relationships between social and ecological phenomena. The application of systems offers at least six possibilities to improve urban governance.

 

A system is an interconnected set of elements that is coherently organized in a way that achieves something . . . . [it] must consist of three kinds of things: elements, interconnections, and a function or purpose.
—Donella Meadows, Thinking in Systems1

 

A system [is] (a) a set of units or elements interconnected so that changes in some elements or their relations produce changes in other parts of the system, and (b) the entire system exhibits properties and behaviors that are different from those of the parts.
—Robert Jervis, Systems Effects 2

 

One of the most important ideas in modern science is the idea of a system; and it is almost impossible to define.
—Garrett Hardin, The Cybernetics of Competition3

Tobias Beckwith's curator insight, August 16, 1:45 PM

One of the things that gives real wizards their "powers," is the ability to see the world as systems within systems within systems... and then finding the leverage points, where a small action in one part of the system might cause a very large response elsewhere...

 

This post and article discuss that whole idea in a bit more depth. I found it to be a good read.

Gary Bamford's curator insight, August 20, 2:08 AM

Non-linear futures.

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JIDT: An information-theoretic toolkit for studying the dynamics of complex systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave and Python. We present the principles behind the code design, and provide several examples to guide users

 

"JIDT: An information-theoretic toolkit for studying the dynamics of complex systems"
Joseph T. Lizier, arXiv:1408.3270, 2014
http://arxiv.org/abs/1408.3270


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Eli Levine's curator insight, August 19, 11:11 AM

This could be useful.

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The Beautiful Phenomena Of Starling Flocks, Explained By Computers

The Beautiful Phenomena Of Starling Flocks, Explained By Computers | collectibles from scoop.it | Scoop.it

On its own, a single starling doesn't elicit much fuss. It's a tennis-ball-sized bird, glossy black in winter, purplish or green in summer, and in autumn, sometimes speckled with white spots. But when starlings congregate in flocks of hundreds of thousands over open fields (something scientists call a "murmuration") they pitch and arc and rush at one another in a bizarre choreography that's puzzled naturalists for hundreds of years. A whole catalog of YouTube videos has documented the black shapes in flight, a movement that looks like the birds are attached to a giant rhythmic gymnast's invisible ribbon.


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Physicists eye neural fly data, find formula for Zipf's law

Physicists eye neural fly data, find formula for Zipf's law | collectibles from scoop.it | Scoop.it

Physicists have identified a mechanism that may help explain Zipf's law – a unique pattern of behavior found in disparate systems, including complex biological ones. The journal Physical Review Letters is publishing their mathematical models, which demonstrate how Zipf's law naturally arises when a sufficient number of units react to a hidden variable in a system.


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Jean-Michel Livowsky's curator insight, August 8, 4:57 AM

Maintenant, on sait pourquoi les terroristes du hamaSS volent, et surtout comment.

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How bird flocks are like liquid helium

How bird flocks are like liquid helium | collectibles from scoop.it | Scoop.it

Mathematical model shows how hundreds of starlings coordinate their movements in flight.

A flock of starlings flies as one, a spectacular display in which each bird flits about as if in a well-choreographed dance. Everyone seems to know exactly when and where to turn. Now, for the first time, researchers have measured how that knowledge moves through the flock—a behavior that mirrors certain quantum phenomena of liquid helium.


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Soul Spelunker » Worlds of Being and Meaning

Soul Spelunker » Worlds of Being and Meaning | collectibles from scoop.it | Scoop.it

Our ego-centered culture has not yet grasped the fact that the archetypal structures of all reality are these worlds of being and meaning. We do not live in these worlds; these worlds live through us.  To the degree that we recognize the Powers who manifest through our lives, we can become that which we were meant to be.

 

These Powers are in conflict with each other. Throughout our lives, we undergo our very own Trojan War. Pathologization is the way of the soul. This conflict can be mediated by a “transcendent function,” which is the “transpersonal nature of the archetypal structures…it gives us an Archimedean point of leverage, a perspective on the world from the standpoint of the world whose name is that of a God or Goddess” (ibid.).


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Eva Rider's curator insight, August 10, 10:24 PM

More on the Transcendent Function: the apex of the sacred triangle: through the looking glass into the reality not yet imagined

 

Rescooped by Vasileios Basios from Depth Psych
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Shamanism, Alchemy and Yoga: Traditional Technologies of Tranformation

Shamanism, Alchemy and Yoga: Traditional Technologies of Tranformation | collectibles from scoop.it | Scoop.it

From the most ancient times, human beings have practiced disciplines of psychospiritual transformation with devoted energy and intention. Modern systems of psychotherapy are the inheritors of three great traditions of transformation, in which the human is seen as engaged in purposive processes of exploration and integration in many realms of consciousness. In this essay I describe some of the common methods used, as well as the major metaphors for transformation.1

One possible definition of shamanism is that it is the disciplined approach to what has been variously called "non-ordinary reality", "the sacred", "the mystery", "the supernatural", "the inner world(s)", or "the otherworld".

 

Psychologically speaking, one could say these expressions refer to realms of consciousness that lie outside the boundaries of our usual and ordinary perception. The depth psychologies derived from psychoanalysis refer to such normally inaccessible realms as "the unconscious", or "the collective unconscious". This would, however, be too limiting a definition for shamanism, if "unconscious" is taken to refer to something within the individual, i.e. intrapsychic. Shamanic practice involves the exploration not only of unknown aspects of our own psyche, but also the unknown aspects of the world around us, - the external as well as internal mysteries.

 

There are three traditional systems of consciousness... (Click title for more)


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Carol Sherriff's curator insight, August 8, 5:04 AM

You don't usually get pscyhologists (or coaches and facilitators) admitting they draw on shamanism and alchemy, so this is refreshing reading.

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Collective Learning and Optimal Consensus Decisions in Social Animal Groups

Collective Learning and Optimal Consensus Decisions in Social Animal Groups | collectibles from scoop.it | Scoop.it

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.

 
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The Science of Problem Solving

The Science of Problem Solving | collectibles from scoop.it | Scoop.it
A review of the science behind problem solving, how it functions in the brain and how we can do it better.

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Collecting just the right data: When you can’t collect all the data you need, a new algorithm tells you which to target.

Collecting just the right data: When you can’t collect all the data you need, a new algorithm tells you which to target. | collectibles from scoop.it | Scoop.it

Much artificial-intelligence research addresses the problem of making predictions based on large data sets. An obvious example is the recommendation engines at retail sites like Amazon and Netflix.

 

But some types of data are harder to collect than online click histories —information about geological formations thousands of feet underground, for instance. And in other applications — such as trying to predict the path of a storm — there may just not be enough time to crunch all the available data.

 

Dan Levine, an MIT graduate student in aeronautics and astronautics, and his advisor, Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics, have developed a new technique that could help with both problems. For a range of common applications in which data is either difficult to collect or too time-consuming to process, the technique can identify the subset of data items that will yield the most reliable predictions. So geologists trying to assess the extent of underground petroleum deposits, or meteorologists trying to forecast the weather, can make do with just a few, targeted measurements, saving time and money.


Via Ashish Umre
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The Brain Is Not Computable: Why Singularity Will Not Happen and Humans Will Assimilate Machines

The Brain Is Not Computable: Why Singularity Will Not Happen and Humans Will Assimilate Machines | collectibles from scoop.it | Scoop.it
A leading neuroscientist says Kurzweil’s Singularity isn’t going to happen. Instead, humans will assimilate machines.

 

Miguel Nicolelis, a top neuroscientist at Duke University, says computers will never replicate the human brain and that the technological Singularity is “a bunch of hot air.”

 

“The brain is not computable and no engineering can reproduce it,” says Nicolelis, author of several pioneering papers on brain-machine interfaces.

 

The Singularity, of course, is that moment when a computer super-intelligence emerges and changes the world in ways beyond our comprehension.

 

Among the idea’s promoters are futurist Ray Kurzweil, recently hired on at Google as a director of engineering, who has been predicting that not only will machine intelligence exceed our own, but people will be able to download their thoughts and memories into computers (see “Ray Kurzweil Plans to Create a Mind at Google—and Have It Serve You”). 

 

Nicolelis calls that idea sheer bunk. “Downloads will never happen,” he said during remarks made at the annual meeting of the American Association for the Advancement of Science in Boston on Sunday. “There are a lot of people selling the idea that you can mimic the brain with a computer.”

 

The debate over whether the brain is a kind of computer has been running for decades. Many scientists think it’s possible, in theory, for a computer to equal the brain given sufficient computer power and an understanding of how the brain works.

 

Kurzweil delves into the idea of “reverse-engineering” the brain in his latest book, How to Create a Mind: The Secret of Human Thought Revealed, in which he says even though the brain may be immensely complex, “the fact that it contains many billions of cells and trillions of connections does not necessarily make its primary method complex.”

 

But Nicolelis is in a camp that thinks that human consciousness (and if you believe in it, the soul) simply can’t be replicated in silicon. That’s because its most important features are the result of unpredictable, nonlinear interactions among billions of cells, Nicolelis says.

 

“You can’t predict whether the stock market will go up or down because you can’t compute it,” he says. “You could have all the computer chips ever in the world and you won’t create a consciousness.”

 

The neuroscientist, originally from Brazil, instead thinks that humans will increasingly subsume machines (an idea, incidentally, that’s also part of Kurzweil’s predictions).

 

In a study published last week, for instance, Nicolelis’s group at Duke used brain implants to allow mice to sense infrared light, something mammals can’t normally perceive. They did it by wiring a head-mounted infrared sensor to electrodes implanted into a part of the brain called the somatosensory cortex.

 

The experiment, in which several mice were able to follow sensory cues from the infrared detector to obtain a reward, was the first ever to use a neural implant to add a new sense to an animal, Nicolelis says.  


Via Dr. Stefan Gruenwald
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Bernhard H. Schmitz's comment, July 16, 2:33 PM
I agree that too many people think it would be sufficient to plug a bunch of neurons together and consciousness will happen. Ridiculous. But I am convinced that it is not necessary to simulate a brain or reverse engineer it. Brains are developed by random incidents and evolution - and it's a mess. I am strongly convinced that a conscious mechanism will be developed from scratch. And it will outwit us.
Bernhard H. Schmitz's curator insight, July 16, 2:37 PM

I agree that too many people think it would be sufficient to plug a bunch of neurons together and consciousness will happen. Ridiculous. But I am convinced that it is not necessary to simulate a brain or reverse engineer it. Brains are developed by random incidents and evolution - and it's a mess. I am strongly convinced that a conscious mechanism will be developed from scratch. And it will outwit us.

Marco Bertolini's comment, July 17, 2:52 AM
@ Bernard Schmitz : I think you have a point there and I like the verty elegant way you put it : a conscious mecanism coming out from the chaos.