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The Myth of Cyberspace – The New Inquiry

The Myth of Cyberspace – The New Inquiry | Systems Theory | Scoop.it

In the early 1980s, when personal computing first became a reality, the faces of glowing terminals had an almost magical aura, transubstantiating arcane passages of 1s and 0s into sensory experience. In fact, the seemingly impenetrable complexity of what was unfolding behind the screen created a sense of mystery and wonderment. We were in awe of the hackers who could unlock the code and conjure various illusions from it; they were modern magicians who seemed to travel between two worlds: reality and cyberspace. One day, we imagined, these sages of cyberspace would leave their bodies behind and fully immerse themselves in the secret world behind the screen. Such images manifested themselves through the decades in films like Tron, Hackers, and The Matrix and in the fiction narratives of the cyberpunk genre. When the public internet first emerged, images of cyberspace were already deeply embedded in our collective imagination; these images have become the primary lens through which we view and evaluate our online activity. For this reason, tracing the genealogy of the cyberspace concept reveals much about present cultural assumptions regarding our relationship with information technology.


Via luiy
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luiy's curator insight, March 18, 2013 1:38 PM

The great irony of the cyberspace concept is that, though we embraced it to resolve cognitive dissonance, it has come to cause only more of it. As Facebook, Twitter, and other social-networking sites have grown more popular, it has become undeniable that they play an important role in organizing our social lives. Our presence on these sites arguably has become so important that we begin to experience the world differently, tailoring our behavior toward producing desirable sorts of things to share on them. We all know intuitively that what we do online affects us offline and vice versa — that both comprise the same friends, the same conversations, the same events. Yet the collective fantasy of cyberspace and all its related vocabulary are so deeply embedded in our cultural logic that we cannot help but lapse into denial of these obvious truths. Our language betrays us; it obfuscates the truth of our experience.

Western culture has a long history of creating such dualisms when confronted with crises of meaning or identity. For example, we have long evaded questions regarding our mortality by conceptually separating matter and form, body and soul. As with cyberspace, this age-old dualism generated a subsequent need to imagine a space where soul could exist apart from body, so we imagined heaven and hell. Our uncritical acceptance of the cyberspace fantasy has imbued it with a similar sacredness; it has become part of a new secular religion, built on faith in something that is imagined but never experienced.

Religion, as Emile Durkheim famously defined it, “is a unified system of beliefs and practices relative to sacred things, that is to say, things set apart and surrounded by prohibitions — beliefs and practices that unite its adherents in a single moral community.” Cyberspace is exactly the sort of thing that we have set apart conceptually and subjected to ceaseless moralizing: It has become almost second nature to claim that “the virtual” is less intimate, authentic, or natural than “the real.” Despite its failure to compellingly describe the world we inhabit, cyberspace nevertheless thrives as a framework for making moral judgments about that same world. Cyberspace has become our Mount Olympus, the founding myth of the Internet Age. It is an article of faith, not the product of lived experience.

Of course, there is nothing intrinsically wrong with fantasy. Speculative fiction provides an important opportunity to anticipate and prepare for techno-cultural change. The problem arises when we begin to prioritize that fictional narrative over actual experience, when we let these speculations control the reality that emerges. We have allowed the myth of cyberspace to usurp reason and to shape perception in our increasingly digitally-mediated lives. Perhaps, this realization should not come as too much of a surprise. Gibson himself recognized that the creative capacities of human beings predispose us to supplanting concrete observation with abstract concepts. A passage from Memory Palace can be read almost as claiming that the cyberspace myth fulfills some broader human teleology:

You see, so we’ve always been on our way to this new place — that is no place, really — but it is real. It’s our nature to represent. We’re the animal that represents — the sole and only maker of maps. And, if our weakness has been to confuse the bright and bloody colors of our calendars with the true weather of days, and the parchment’s territory of our maps with the land spread out before us—never mind. We have always been on our way to this new place — that is no place, really — but it is real.

Support The New Inquiry. Subscribe to TNI Magazine for $2Cyberspace is not real per se but real in the sense of the Thomas theorem: “If [wo]men define situations as real, they are real in their consequences.” Real reality is not characterized by such dualisms; it is equally made of atoms and bits. The cost of upholding this mythical separation is that we have become disassociated with many aspects of our lives. If we hope to make ourselves whole again, we first need a new vocabulary, new myths, and new representations for the Web.

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On the verge of machine learning and machine intelligence

On the verge of machine learning and machine intelligence | Systems Theory | Scoop.it
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New weblog published - On the verge of machine learning and machine intelligence - you can find it here

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The real cyborgs - in-depth feature about people merging with machines

The real cyborgs - in-depth feature about people merging with machines | Systems Theory | Scoop.it
Read about the pioneers of our “post-human” future and see how they're implanting technology into their bodies and brains.
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Neuronal Network 3-D Models Reveal Organizational Principles Of Sensory Cortex - Science 2.0

Neuronal Network 3-D Models Reveal Organizational Principles Of Sensory Cortex - Science 2.0 | Systems Theory | Scoop.it
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Neuronal Network 3-D Models Reveal Organizational Principles Of Sensory Cortex
Science 2.0
In their April publication in Cerebral Cortex, Max Planck Institute for Biological Cybernetics researchers Dr.
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Edge Of Chaos

A short video and text composition exploring the term edge of chaos from complexity systems theory Produced by: http://complexitylab.io/media Twitter: ...
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Chinese Search Company Baidu Built a Giant Artificial-Intelligence Supercomputer | MIT Technology Review

Chinese Search Company Baidu Built a Giant Artificial-Intelligence Supercomputer | MIT Technology Review | Systems Theory | Scoop.it
A supercomputer specialized for the machine-learning technique known as deep learning could help software understand us better.
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Physicists Are Philosophers, Too

Physicists Are Philosophers, Too | Systems Theory | Scoop.it
In his final essay the late physicist Victor Stenger argues for the validity of philosophy in the context of modern theoretical physics
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Complexity Theory Overview

In this video we will be giving an overview to the areas of complexity theory by looking at the major theoretical frameworks that are considered to form part of it ...
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The dawn of artificial intelligence

The dawn of artificial intelligence | Systems Theory | Scoop.it
        “THE development of full artificial intelligence could spell the end of the human race,” Stephen Hawking warns. Elon Musk fears that...

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The enigma of context within network-centric environments

The enigma of context within network-centric environments | Systems Theory | Scoop.it
The enigma of context within network-centric environments. . ???aop.label???. doi: 10.1080/23335777.2015.1036776
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New paper published

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What happens when the world turns into one giant brain | GigaOM Tech News

What happens when the world turns into one giant brain | GigaOM Tech News | Systems Theory | Scoop.it

These days, many of us in consumer and enterprise tech companies are working on predictive systems that provide modest but valuable augmentation of human intelligence and business processes. I think this scale of ambition is a good fit for the current state of the art in machine learning and probabilistic inference. Think personal assistants like Siri or Google Now, predictive analytics in the enterprise for churn detection and ad campaign targeting, and personalized news apps like Prismatic.

 

But I think that the long term story is much more exciting, and much further from our experience with synthetic intelligence to date. I believe that we are on the path to building the equivalent of global-scale nervous systems. I’m thinking Gaia’s brain: distributed but unified intelligences that gather data from sensors all over the world, and that synthesize those data streams to perceive the overall state of the planet as naturally as we perceive with our own sensory systems. This isn’t just big data–this is big inference.

 

To make this idea of a global intelligence more concrete, consider the startup Premise. As a first step toward the kind of perceptual systems that I am talking about, Premise is using various signals from the public internet as a set of massively distributed sensory organs, and then leveraging this information to develop more informative economic indexes.

 

Now consider what other problems such systems could solve in the coming decades. We could gain a true understanding of the climate system on a granular but global level. We could track and coordinate every vehicle on the planet, to improve energy efficiency and optimize scheduling to all but eliminate traffic jams. Or moving from vehicles to parts and materials, we could create and manage truly robust supply chains that maintain efficiency and resilience in the face of unexpected events. The possibilities go on, and are truly awesome.

 

Click headline to read more--


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Why the Flash Crash Really Matters

Why the Flash Crash Really Matters | Systems Theory | Scoop.it

So which explanation captures the dynamics that led to the crash? Could an ordinary order in the futures market or a lone market manipulator really cause the crash? The simple answer is that this is the wrong question to ask. From the perspective of the joint report and the enforcement action, the Flash Crash was a fluke, an idiosyncratic event caused by an unexpected glitch in the markets. But it was far from being a fluke. Instead, the Flash Crash reveals that we need a fundamentally different understanding of how modern financial markets work. We believe that it shows us that markets are governed by the same principle as earthquakes and avalanches: self-organized criticality.

 

http://nautil.us/issue/23/dominoes/why-the-flash-crash-really-matters


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PSFK Explains: What is Cognitive Computing? - PSFK (blog)

PSFK Explains: What is Cognitive Computing? - PSFK (blog) | Systems Theory | Scoop.it
Cognitive systems can make sense of data, weigh decisions and advise to reach a set goal, functioning as an intelligent assistant to its user.

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Kenneth Boulding, General Systems Theory (1956)

Kenneth Boulding

General Systems Theory
The Skeleton of Science

(1956)

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What is Complexity Theory? - Kieran D. Kelly

What is Complexity Theory? - Kieran D. Kelly | Systems Theory | Scoop.it
Spontaneous Order & Complexity does not arise in defiance of The Second Law of Thermodynamics but with the help of it!... Complexity is Coarse Entropy!...

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Philippe Vallat's curator insight, May 12, 11:31 AM

Excellent and clear article, describing many of the concepts of complexity theory. Must read!

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Wolfram's Image Recognition Reflects a Big Shift in AI | WIRED

Wolfram's Image Recognition Reflects a Big Shift in AI | WIRED | Systems Theory | Scoop.it
What's changed is the amount of computing power we have at our disposal. We can now run these systems across dozens, hundreds, even thousands of high-powered processors.
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Cyber-physical systems' final frontier: The human body - ScienceBlog.com

Cyber-physical systems' final frontier: The human body - ScienceBlog.com | Systems Theory | Scoop.it
May 14, 2015 | ScienceBlog.com Today the National Science Foundation (NSF) announced two, five-year, center-scale awards totaling $8.75 million to advance the state-of-the-art in medical and cyber-physical systems (CPS). One …
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Complex Adaptive Systems: 3 Control Systems

In this module we are going to talk about control systems from the perspective of systems theory and cybernetics, we will firstly define what they are and give ...
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From Science Fiction to Reality: The Evolution of Artificial Intelligence | WIRED

From Science Fiction to Reality: The Evolution of Artificial Intelligence | WIRED | Systems Theory | Scoop.it
Getty Images What was once just a figment of the imagination of some our most famous science fiction writers, artificial intelligence (AI) is taking root in our everyday lives. We’re still a few years away from having robots at our beck and call, but AI has already had a profound impact in more subtle ways.…

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The Current State of Machine Intelligence

The Current State of Machine Intelligence | Systems Theory | Scoop.it

A few years ago, investors and startups were chasing “big data”. Now we’re seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or collectively “machine intelligence”. The Bloomberg Beta fund, which is focused on the future of work, has been investing in these approaches.

 

Computers are learning to think, read, and write. They’re also picking up human sensory function, with the ability to see and hear (arguably to touch, taste, and smell, though those have been of a lesser focus).


Machine intelligence technologies cut across a vast array of problem types (from classification and clustering to natural language processing and computer vision) and methods (from support vector machines to deep belief networks). All of these technologies are reflected on this landscape.


What this landscape doesn’t include, however important, is “big data” technologies. Some have used this term interchangeably with machine learning and artificial intelligence, but I want to focus on the intelligence methods rather than data, storage, and computation pieces of the puzzle for this landscape (though of course data technologies enable machine intelligence).


We’ve seen a few great articles recently outlining why machine intelligence is experiencing a resurgence, documenting the enabling factors of this resurgence. Kevin Kelly, for example chalks it up to cheap parallel computing, large datasets, and better algorithms.


Machine intelligence is enabling applications we already expect like automated assistants (Siri), adorable robots (Jibo), and identifying people in images (like the highly effective but unfortunately named DeepFace). However, it’s also doing the unexpected: protecting children from sex trafficking, reducing the chemical content in the lettuce we eat, helping us buy shoes online that fit our feet precisely, anddestroying 80's classic video games.


Big companies have a disproportionate advantage, especially those that build consumer products. The giants in search (Google, Baidu), social networks (Facebook, LinkedIn, Pinterest), content (Netflix, Yahoo!), mobile (Apple) and e-commerce (Amazon) are in an incredible position. They have massive datasets and constant consumer interactions that enable tight feedback loops for their algorithms (and these factors combine to create powerful network effects) — and they have the most to gain from the low hanging fruit that machine intelligence bears.
Best-in-class personalization and recommendation algorithms have enabled these companies’ success (it’s both impressive and disconcerting that Facebook recommends you add the person you had a crush on in college and Netflix tees up that perfect guilty pleasure sitcom).
Now they are all competing in a new battlefield: the move to mobile. Winning mobile will require lots of machine intelligence: state of the art natural language interfaces (like Apple’s Siri), visual search (like Amazon’s “FireFly”), and dynamic question answering technology that tells you the answer instead of providing a menu of links (all of the search companies are wrestling with this).Large enterprise companies (IBM and Microsoft) have also made incredible strides in the field, though they don’t have the same human-facing requirements so are focusing their attention more on knowledge representation tasks on large industry datasets, like IBM Watson’s application to assist doctors with diagnoses.
Via Dr. Stefan Gruenwald
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John Vollenbroek's curator insight, April 25, 2:53 AM

I like this overview

pbernardon's curator insight, April 26, 2:33 AM

Une infographie et une cartographie claire et très intéressante sur l'intelligence artificielle et les usages induits que les organisations vont devoir s'approprier.

 

#bigdata 

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IBM advances bring quantum computing closer to reality - Computerworld

IBM advances bring quantum computing closer to reality - Computerworld | Systems Theory | Scoop.it
IBM scientists say they have made two critical advances in an industrywide effort to build a practical quantum computer, shaving years off the time expected to have a working system.
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Baidu built a supercomputer for deep learning

Baidu built a supercomputer for deep learning | Systems Theory | Scoop.it
Chinese search engine company Baidu says it has built the world’s most-accurate computer vision system, dubbed Deep Image, which runs on a supercomputer optimized for deep learning algorithms. Baidu claims a 5.98 percent error rate on the ImageNet object classification benchmark; a team from Google won the 2014 ImageNet competition with a 6.66 percent error rate. In…
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