Virtual Reality  

When Oculus VR was acquired by Facebook two years ago, many were scratching their heads, wondering what the social network wanted with a startup virtual reality company. Now at Facebook’s annual developers conference, the company showed off some developments of how virtual reality might take social sharing to a whole new dimension.

At Facebook's F8 developer conference recently, company CTO Mike Schroepfer gave a live on-stage demo showing some of the first early development of the Facebook/Oculus Social VR team, and highlighting the potential of Oculus VR for social interaction. See the full video below.

Although it was obviously a prepared demonstration, probably with a bit of software 'smoke and mirrors', the potential for integrating virtual reality into the Facebook user experience was brilliantly shown.

During the demo, the two participants, separated by 35 miles interacted in VR with hand gestures, voice chat and head movements. Both users were wearing Oculus headsets and hand controller devices. The interaction between each of the participants seemed to be almost as natural as if they were standing together.

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In VR they could see each other as an avatar with resolution about as good as a Nintendo Mii character, but they did explore customizing each other by creating neckties for each other and then pinning them to the other's avatar. It was clear in the demo that hand movement and low latency important to social VR interactions.

Using 360 degree photo disks the users brought up to their faces, they would visit different locations. The locations were static, but presumably the experience was very immersive through the Oculus headset.

The social app demo ended the pair taking a virtual selfie inside what looked like a 360-degree photo of London, and uploading it to their Facebook account by dropping the virtual photo into a digital mailbox.

Although it was a brief glimpse, Facebook's acquisition of Oculus is really starting to make sense, and the technology could really take off. In the future we really be able to meet up with each other anywhere in the world, or in any world for that matter and share experiences. What we do there, only time will tell.

SOURCE  Upload VR

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Artificial Intelligence  

Facebook founder Mark Zuckerberg  has posted that he is intending to build himself an artificial intelligence assistant to help run his house and work with him in his business. In his own words, "You can think of it kind of like Jarvis in Iron Man."

Along with learning Mandarin, reading two books every month, and meet a new person every day, Facebook founder and new father Mark Zuckerberg just announced an ambitious new goal for himself: he is planning on creating J.A.R.V.I.S.

"My personal challenge for 2016 is to build a simple AI to run my home and help me with my work. You can think of it kind of like Jarvis in Iron Man," the tech billionaire wrote on his Facebook wall.

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"I'm going to start by exploring what technology is already out there," Zuckerberg writes. "Then I'll start teaching it to understand my voice to control everything in our home -- music, lights, temperature and so on. I'll teach it to let friends in by looking at their faces when they ring the doorbell."

"My personal challenge for 2016 is to build a simple AI to run my home and help me with my work. You can think of it kind of like Jarvis in Iron Man."

Zuckerberg envisions the artificial intelligence as a parental aide: "I'll teach it to let me know if anything is going on in Max's room that I need to check on when I'm not with her."

More than just for the home, the digital assistant will help him run his business affairs too. "On the work side, it'll help me visualize data in VR to help me build better services and lead my organizations more effectively."

Every year, I take on a personal challenge to learn new things and grow outside my work at Facebook. My challenges in...
Posted by Mark Zuckerberg on Sunday, January 3, 2016

Zuckerberg plans on doing most of the work himself. " it's a different kind of rewarding to build things yourself, so this year my personal challenge is to do that. This should be a fun intellectual challenge to code this for myself. I'm looking forward to sharing what I learn over the course of the year."

What do you think, is Zuckerberg looking to take on Elon Musk as a real-world Tony Stark?

SOURCE  Mark Zuckerberg

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Artificial Intelligence  

Facebook's AI research team is working to build smart systems that can enhance people's lives. The team recently shared their AI research and the impact this work is already having.

With an ever-exploding number of posts to Facebook newsfeeds by the site's over 1 billion users, there is no way human-based systems can hope to catch up with or even keep track of the social networks user collateral. Heading up this challenge, Facebook AI Research (FAIR) has been conducting ambitious work in areas like image recognition and natural language understanding. They've already published a series of groundbreaking papers in these areas, and recently announced a few more milestones.

The research includes a system that can analyze photos, determine what they depict, and offer a verbal description—a feature that is particularly valuable for visually impaired people.

The researchers have been working to train systems to recognize patterns in the pixels so they can be as good as or better than humans at distinguishing objects in a photo from one another and then identifying each object. The process is known as "segmentation."

Teaching machines to see and understand
Facebook's AI team is working to build smart systems that can enhance people's lives. Watch this video to learn about how we're approaching AI research and the impact this work is already having.
Posted by Facebook Engineering on Tuesday, November 3, 2015

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The team has also been making advances in natural language understanding, with new developments in a new technology called Memory Networks (aka MemNets). 

MemNets add a type of short-term memory to the convolutional neural networks (CNN) that power FAIR's deep-learning systems, allowing those systems to understand language more like a human would. "Now we've scaled this system from being able to read and answer questions on tens of lines of text to being able to perform the same task on data sets exceeding 100K questions, an order of magnitude larger than previous benchmarks," report the researchers.

They have also made technological leaps in predictive analysis and unsupervised learning, The FAIR team recently started to explore these models, and has developed a system that can “watch” a series of visual tests — in this case, sets of precariously stacked blocks that may or may not fall — and predict the outcome. After just a few months' work, the system can now predict correctly 90 percent of the time, which is better than most humans.

The team claims to also be making progress in the ever-challenging AI problem of the game Go. "We’ve been working on our Go player for only a few months, but it's already on par with the other AI-powered systems that have been published, and it's already as good as a very strong human player," they claim.

"Because now that it works, there are so many doors that are open all of a sudden.."

"It used to be that there were people working on computer vision, people working on natural language understanding, people working on speech recognition, and now all of those things use deep learning in some way or another," FAIR's head Yann LeCun told Slate. "You see this convergence of a lot of fields that were very very far apart before that are now all using kind of the same techniques." 

LeCun says artificial intelligence now has real potential for sustained gains. "Now computer vision and speech recognition just work," he said. "They’re not perfect, but they work. And that enables a lot of applications, which is why you see all this excitement around deep learning and AI. Because now that it works, there are so many doors that are open all of a sudden."

According to resarcher Mike Schroepfer, "Our AI research — along with our work to explore radical new approaches to connectivity and to enable immersive new shared experiences with Oculus VR — is a long-term endeavor. It will take a lot of years of hard work to see all this through, but if we can get these new technologies right, we will be that much closer to connecting the world."

SOURCE  Facebook

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Visual Question and Answering Demo
Earlier this year, we showed some of our work on natural language understanding — specifically, a system called Memory Networks (MemNets) that can read and then answer questions about short texts. In this demo of a new system we call VQA, or visual Q&A, MemNets are combined with our image recognition technology, making it possible for people to ask the machine what's in a photo.
Posted by Facebook Engineering on Tuesday, November 3, 2015

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Artificial Intelligence  

This talk describes how convolutional neural networks work and can be used to make computers appear to "learn" and "think" in a way analogous to how the human brain works. Facebook's artificial intelligence head, Yann LeCun describes many practical applications of these networks, such as picture and facial recognition, text analysis, and more.

SOURCE  Hot Chips

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 Virtual Reality  

At this year's TechCruch Disrupt conference, Oculus VR CEO Brendan Iribe spoke about how his company's merger with Facebook represents an opportunity to build a massive online virtual reality community.

When Facebook recently purchased Oculus VR, many were left wondering what the social networking giant would want with a virtual reality start-up. Some analysts suggested that Facebook would have a chance to lead the next big computing platform. Now, Oculus CEO Brendan Iribe just told an audience at TechCrunch Disrupt about one lofty possibility for the merger: building a massively multiplayer experience (MMO) for one billion simultaneous users.

"This is going to be an MMO where we want to put a billion people in virtual reality, and a billion-person virtual-world MMO is going to require a bigger network than exists today. Why not start with Facebook and their infrastructure and their team and talent that they've built up?"

While Iribe suggests that a billion-person MMO is "going to take a bigger network than exists in the world today," he says Facebook's network makes a great place to start, and suggested it could be a Metaverse that joins disparate virtual worlds.

Image Source - TechCrunch

"This is going to be an MMO where we want to put a billion people in virtual reality, and a billion-person virtual-world MMO is going to require a bigger network than exists today."

The Oculus CEO says that the company's new Seattle offices — headed by Valve veteran Michael Abrash — will become an R&D lab that will engage with universities and work with students on virtual reality. "We think this is going to be one of the most researched areas in decades to come," says Iribe.

Growing to one billion users may also have been the reason Oculus decided to join up with Facebook instead of a traditional gaming company. Iribe says Oculus is still as committed to games as ever, the company realized that a focus on games could artificially limit its reach. "Do you want to build a platform that has a billion users on it, or only 10, 20, or 50 million?" asks Iribe, noting that dedicated game systems don't sell nearly as well as mobile devices in the grand scheme of things.

"With Oculus ... if you're putting on this pair of glasses and you're going to be face-to-face communicating with people and you're going to be jumping in and out of this new set of virtual worlds, this was going to be the largest MMO (massively multiuser online community) ever made," Iribe said.

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Obviously, the billion-person MMO is a long ways off, but the company has its eye on a stepping stone: Oculus hopes to convince players that they're having a "real conversation" with another person. For now, the uncanny valley of imperfect video game graphics will keep people from having photorealistic faces, but even cartoony ones might be enough to get us to the tipping point.

According to Iribe,"[I]f you let go, you can have a real conversation with a person. That's the holy grail we're trying to get to." Essentially he is talking about the future of videoconferencing, or what will replace it entirely.

This statement seems to echo Ray Kurzweil's predictions on the advancement of virtual reality:

These are not just sort of places to play although we’ll do that as well but these would be places to interact with other people and it will be an extension of real reality just the second life is today and for some people it’s a game, for some people it’s quite serious, it’s a place to be and this place to be, a virtual reality will become more and more realistic, more and more full immersion, more and more detailed and more and more imaginative.

Along with talking about the Facebook merger, Iribe also demonstrated a mind-blowing new generation of virtual reality coming in the future from Oculus that will challenge people's perception of what's real and what's not.

"This avatar you're looking at, your brain will be convinced it's a person, because it is," he said. "It's just the virtual representation of a person ... (but) you'll be there going, 'I know that's you. It doesn't quite look like you -- you're a cartoon or you've changed your hair -- but I can tell it's you.' And your brain will be convinced."

You can watch the full video of Iribe’s talk here.


SOURCE  The Verge

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 Facial Recognition  

Facebook's new AI Group has been working on a deep learning project called DeepFace, to develop facial recognition software which maps 3D facial features allowing facial recognition from any angle.

Research at the newly organized Facebook AI Group has already yielded a system that recognizes faces almost as well as a human. Called DeepFace, the work is not yet a part of the Facebook system, but promises incredible possibilities for photographic recognition and other applications.

"Our method reaches an accuracy of 97.25% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 25%, closely approaching human-level performance."

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The system has reached an accuracy of 97.25% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 25%, closely approaching human-level performance (determined for the same dataset at a mean of 97.5%).

So far DeepFace remains purely a research project. Facebook has released a research paper on the project and the researchers will present the work at the IEEE Conference on Computer Vision and Pattern Recognition in June.

DeepFace uses two processes to recognize faces. First it corrects the angle of a face so that the person in the picture faces forward, using a default 3D model of an forward-looking face. This means the faces do not have to be in a fixed orientation to be recognized by the system.

Next the deep learning algorithms are applied using a simulated neural network works to create a numerical description of the reoriented face. If DeepFace comes up with similar enough descriptions from two different images, it decides they must show the same face.

The researchers have trained DeepFace on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities, where each identity has an average of over a thousand samples.

This dataset has allowed for the accurate model-based alignment with the large facial database generalize remarkably well to faces in uncontrolled situations.

The performance of the final software was tested against a standard data set that researchers use to benchmark face-processing software, which has also been used to measure how humans fare at matching faces.

The study authors conclude:

Our work demonstrates that coupling a 3D model-based alignment with large capacity feedforward models can effectively learn from many examples to overcome the drawbacks and limitations of previous methods. The ability to present a marked improvement in face recognition, which is a central field of computer vision that is both heavily researched and rapidly progressing, attests to the potential of such coupling to become significant in other vision domains as well.

Neeraj Kumar, a researcher at the University of Washington who has worked on face verification and recognition, told MIT Technology Review that Facebook’s results show how finding enough data to feed into a large neural network can allow for significant improvements in machine-learning software. “I’d bet that a lot of the gain here comes from what deep learning generally provides: being able to leverage huge amounts of outside data in a much higher-capacity learning model,” he says.

With augmented reality increasingly becoming part of computer systems and hardware, facial recognition has the potential to be increasingly ubiquitous as well.  Systems like DeepFace hint at just how accurate such systems may be, and also point to a future where anonymity and privacy may be difficult or even impossible to maintain.


SOURCE  Facebook

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 Artificial Intelligence  

Facebook has named prominent New York University Professor Yann LeCun the director of a newly opened laboratory devoted to research in artificial intelligence and deep learning.

Recently, Facebook announced that one of the most prominent artificial-intelligence researchers in the world, Yann LeCun, will come onboard the company to lead a massive new artificial intelligence project spanning offices in London, California, and New York. Facebook founder and CEO Mark Zuckerberg and chief technical officer Michael Schroepfer announced LeCun’s hiring at the Neural Information Processing Systems Conference in Lake Tahoe, an annual gathering of the AI community.

According to Gary Marcus, the move points to a new layer in Facebook’s ambitions, as well as a shift in the research and development of artificial intelligence.

In September, Facebook formed their AI Group to do world-class artificial intelligence research using all the knowledge that people have shared on the social network. The goal of the company is to use new methods in AI to help make sense of all the content that people share so they can generate new insights about the world and answer people's questions.

LeCun himself describes his aim as having "the long-term goal of bringing about major advances in artificial intelligence."

LeCun, the current director of New York University's Center for Data Science, first taught computers how to read handwritten digits using data provided by the United States Postal Service. Many A.T.M.s now use LeCun’s techniques to automatically read checks.

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The technology was powered by what he termed convolutional neural networks. Its descendants continue to live on at Google, I.B.M., Microsoft, N.E.C., and Baidu. LeCun is also a pioneer in what’s known as deep learning, a kind of sophisticated, machine-learning technique that excels at interpreting the meanings and context of symbols and images.

If Facebook can use deep learning algorithms to recognize faces in its huge library of users' photos, it can then reliably predict your behavior on its social network.  This means it can serve you ads you’re more likely to click on.

“I could even imagine Facebook identifying the brand of a product in the background of an image and then using that information to target advertisements related to that brand to the user who uploaded the image,” George Dahl, a PhD student who works with Geoff Hinton in the deep learning group at the University of Toronto, told Wired.

As LeCun begins work on the new AI lab at Facebook, Hinton is already well established at Google, working on deep learning improvements to search and other applications. Artificial intelligence is on the verge of a major renaissance, poised to overhaul the way data is analyzed across so many of the online services we use every day, according to Wired's Cade Metz.

Where in the past, most AI research was done in the ivory towers of academia, Facebook and Google now are offering top AI researchers resources that academic institutions can no longer match. What this means for the long-term scientific health of our nation remains to be seen.


SOURCE  Wired

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 Computer Science  

A new algorithm that tags photos based on the relationships that people in images already have with each other has been developed at the University of Toronto. The algorithm uses the name and location of existing photo tags to build a "relationship graph," where personal connections in the images are calculated.

A new algorithm designed at the University of Toronto has the power to profoundly change the way we find photos among the billions on social media sites such as Facebook and Flickr.  This month, the United States Patent and Trademark Office have issued  patent #8,611,673 on this technology.

Developed by Parham Aarabi, a professor in The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, and his former Master’s student Ron Appel, the search tool uses tag locations to quantify relationships between individuals, even those not tagged in any given photo.

"Essentially, we found that if people are standing close together or are tagged close together inside images – in one image it doesn't tell you a lot of information. But across hundreds of images that someone has on Facebook, it's a very good indicator of how close they are in real life, in a social sense," Aarabi told CTVNews.ca in a phone interview.

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Imagine you and your mother are pictured together, building a sandcastle at the beach. You’re both tagged in the photo quite close together. In the next photo, you and your father are eating watermelon. You’re both tagged. Because of your close ‘tagging’ relationship with both your mother in the first picture and your father in the second, the algorithm can determine that a relationship exists between those two and quantify how strong it may be.

In a third photo, you fly a kite with both parents, but only your mother is tagged. Given the strength of your ‘tagging’ relationship with your parents, when you search for photos of your father the algorithm can return the untagged photo because of the very high likelihood he’s pictured.

“Two things are happening: we understand relationships, and we can search images better,” says Professor Aarabi.

The nimble algorithm, called relational social image search, achieves high reliability without using computationally intensive object- or facial-recognition software.

“If you want to search a trillion photos, normally that takes at least a trillion operations. It’s based on the number of photos you have,” says Aarabi. “Facebook has almost half a trillion photos, but a billion users—it’s almost a 500 order of magnitude difference. Our algorithm is simply based on the number of tags, not on the number of photos, which makes it more efficient to search than standard approaches.”

Work on this project began in 2005 in Professor Aarabi’s Mobile Applications Lab, Canada’s first lab space for mobile application development.

Currently the algorithm’s interface is primarily for research, but Aarabi aims to see it incorporated on the back-end of large image databases or social networks. “I envision the interface would be exactly like you use Facebook search—for users, nothing would change. They would just get better results,” says Aarabi.

While testing the algorithm, Aarabi and Appel discovered an unforeseen application: a new way to generate maps. They tagged a few photographs of buildings around the University of Toronto and ran them through the system with a bunch of untagged campus photos. “The result we got was of almost a pseudo-map of the campus from all these photos we had taken, which was very interesting,” says Aarabi.


SOURCE  University of Toronto

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 Artificial Intelligence  

With artificial intelligence, a technology Facebook CEO Mark Zuckerberg highlighted as a key research area at a recent earnings call, the social network may soon understand not only what we do on the Facebook, but what we mean, or what we really want.

On a recent earnings call with investment analysts, Facebook CEO Mark Zuckerberg announced the company has a new Artificial Intelligence unit and, also, the company is separately developing a new speech recognition product.

According to Zuckerberg,

In September, we formed the Facebook AI Group to do world-class artificial intelligence research using all the knowledge that people have shared on Facebook. The goal here is to use new approaches in AI to help make sense of all the content that people share so we can generate new insights about the world to answer people's questions

Facebook AI being based on "all the content people share" seems like a reference to the over 1.2 trillion user posts the company is now trying to make searchable.

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Zuckererg continued:

We started assembling a team of some of the best people in the field to work on these problems. We also announced the acquisition of Mobile Technologies, a speech recognition and machine translation company that will help expand our work in the field beyond just photo recognition to voice. Over time, I think it is going to be possible to build services that are much more natural to interact with and can help solving many more problems than any existing technologies today. I am excited that we are working on this problem and I am looking forward to doing a lot more here.

Such a natural interaction model indicates Facebook wants to harmonize an AI and speech recognition system. Mobile Technologies work in translation software will also help Facebook be more competitive globally, and expand their software capabilities beyond an English-focused underpinning.  Josh Costine at TechCrunch suggests, "If Facebook can find a way to surmount this hurdle and allow us to communicate despite our different tongues, it could promote greater racial and international tolerance." Of course, It could also help advertisers reach a much wider audience without very much extra work.

Why would a social networking system want to develop AI? Bhiksha Raj, a machine learning expert at Carnegie Mellon University, told Singularity Hub, “Places like Google and Facebook now have so much data that the kinds of networks you can train and the kinds of things you can learn from that have basically been unimaginable.”
So conceivably, Facebook in the future may know more about you, than you know yourself.




SOURCE  Business Insider

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 Futurism  

In a creative infographic, Clear High Speed Internet looks at the future, from social media to nanotechnology.  Thank you to Arthur Murray for sharing this infographic with us.

T he future. We all think about it, practically lust for it. That's why we have to have the latest technology, the fastest network, the most high-definition screen. Even our forefathers, back in the day before there were wired phones, much less wireless ones, or world travel, much less travel to other worlds (or moons), dreamed of what was to come.

Technology is essential to our futures. Check out some visions of what's to come, including ours (which are subject to change with that time travel thing that's coming).

SOURCE  Clear High Speed Internet

By Arthur Murray

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 The Singularity

In Ray Kurzweil's The Singularity Is Near, he discusses three main technologies that will impact the future: G, N and R for genetics, nanotechnology and robotics (which includes the most powerful technology, AI). But did the futurist leave out one very important development that is presently impacting society, economics and people's lives in a very substantial way?

Ray Kurzweil carefully explained in The Singularity Is Near how Genetics (G), Nanotechnology (N) and Robotics (R) are the primary exponential technologies of the coming Singularity.

These "the three overlapping revolutions," he says they will characterize the first half of the twenty-first century. He goes on to say, "These (GNR technologies) will usher in the beginning of the Singularity. We are in the early stages of the 'G' revolution today. By understanding the information processes underlying life, we are starting to learn to reprogram our biology to achieve the virtual elimination of disease, dramatic expansion of human potential, and radical life extension."

Kurzweil then says regarding nanotechnology, "The 'N' revolution will enable us to redesign and rebuild - molecule by molecule - our bodies and brains and the world with which we interact, going far beyond the limitations of biology."

 Of the three (GNR), Ray Kurzweil believes that the most powerful impending revolution is the 'R' revolution as it encompasses artificial intelligence.   He writes, "Human-level robots with their intelligence derived from our own but redesigned to far exceed human capabilities represent the most significant transformation, because intelligence is the most powerful 'force' in the universe.  Intelligence, if sufficiently advanced, is, well, smart enough to anticipate and overcome any obstacles that stand in its path." —

The 'R' acronym essentially encompasses I.J. Goode's "intelligence explosion," that is for many itself synonymous with the Singularity.

Are the GNR technologies enough though?  Could Kurzweil have overlooked other factors that will power and accelerate the technological Singularity?

One possible acronym that might be 'C' for connectedness, or connectivity, communication, or even cooperation.

We are now well into the development and implementation of 'C' technologies from the telegraph to the telephone, to the internet. As these technologies continually become faster, cheaper and closer to direct incorporation into our bodies and minds, they are a key component of the technological Singularity.

Consider the impact of the internet itself.  In The Singularity Is Near, Kurzweil does not really lump the Internet into a G, N or R category, and obviously the world-wide-web impacts all of these areas, from allowing scientists and researchers to communicate there ideas with unprecedented speed and reach.

For nanotechnology, Kurzweil also mentions that the devices we will implant into our own bloodstreams will communicate with intelligent software and other online services.  Artificial intelligence systems will obviously interact and communicate with people and other AIs via the internet as well.

In Abundance: The Future Is Better Than You Think, Steven Kotler and Peter Diamandis also explore 'C' technologies that are empowering the Singularity from competitions, to crowd sourcing and on-line education.  These tools empower the technophilanthopists and DIY innovators Diamadis and Kotler describe to further accelerate technological development.  Information and communications technologies are, as a result, key building blocks of their Abundance Pyramid.

Another 'C' acronym word that might even be included is culture.  As Ronald Wright points out in Nonzero: The Logic of Human Destiny:

Next humans started with a completely second kind of evolution: cultural evolution (the evolutions of ideas, memes, and technologies).  Amazingly, that evolution has sustained the trajectory that biological evolution has established towards greater complexity and cooperation.

Kotler and Diamandis point out that the cultural evolution brought about the most powerful tools for cooperation the world has ever seen.

Economist Jeffrey Sachs also points out, "In the world's most remote villages the conversation now often turns to the most up-to-date political and cultural events, all empowered by cell phones even more than radio and television."

Possibly a 'C' could also represent the rise of the global brain.  The global represents the worldwide intelligent network formed by all the individuals of this planet, together with the information and communication technologies that connect them into a self-organizing whole.

According the Wikipedia definition of the global brain: as the internet becomes faster, more intelligent, and more encompassing, it increasingly ties us together into a single information processing system, which functions like a nervous system for the planet Earth. The intelligence of this network is collective or distributed: it is not centralized or localized in any particular individual, organization or computer system. It rather emerges from the dynamic networks of interactions between its components, a property typical of complex adaptive systems.

Image Source: Stefan Larsson

Cybernetisist Cliff Joslyn points out that,

We are seeing a vast increase in the variety of kinds of activities and information available to individuals. This does not just facilitate human freedom, it effectively is human freedom in the sense of there being an increasing variety of possible states of human experience and actions. But on the other hand, constraints are introduced from the global level through economic and technological processes, and “canalization” and self-organizing around norms, protocols, and economic structures. 

The 'C' technologies that are helping give rise to the global brain will also make use of the cognitive surplus, a term conceived of  by Clay Shirky who notes that we are experiencing an era where people like to produce and share just as much, if not more than they like to consume. With technology allowing the production and sharing as never before we will see a new era of participation that will lead to big change.

As the newly formed Global Brain Institute looks to the phenomenon of the global brain,

We see people, machines and software systems as agents that communicate via a complex network of communication links. Problems, observations, questions or opportunities define challenges that may incite these agents to act.

Challenges that cannot be fully resolved by a single agent are normally propagated to one or more other agents, along the links in the network. These agents contribute their own expertise to resolving the challenge, and if necessary propagate the challenge further, until it is fully resolved. Thus, the skills and knowledge of the different agents are pooled into a collective intelligence much more powerful than the one of its individual members.

The propagation of challenges across the global network is a complex, self-organizing process, similar to the "spreading activation" that characterizes thinking in the human brain. This process will typically change the network by reinforcing useful links, while weakening the others. Thus, the network learns or adapts to new challenges, becoming more intelligent in the process.

With the Internet of Things also bringing our machines online it is essential that the thinking around 'C' technologies not be limited to person-to-person communication or cooperation.  Today, there are about two Internet-connected devices for every man, woman and child on the planet. By 2025, analysts are forecasting that this ratio will rise past six. Therefore can expect to grow to nearly 50 billion Internet-connected devices in the next decade.

"Eventually," Kurzweil writes "we will merge with these technologies." At that point, the Singularity, humans will become immortal and capable of changing their forms and environments at will, Kurzweil believes.

"There will be no distinction, post-Singularity, between human and machine nor between physical and virtual reality," Kurzweil writes in his book.

As Eric Savitz from Forbes points out, "This fabric of technology will allow us to measure systems on a global scale and at the same time offer a never before seen resolution. This capability to bring the big picture into sharp focus enables us to provide a real-time digital representation of our constantly changing world. This is the first step in a journey to explain the world will live in, our role in it and our impact upon it."

The connectedness expressed via concepts like the global brain, to this end, represents also how humanity will potentially merge with machine intelligence.  Based on the idea that the global brain will arise from the connections as a new form of intelligence, perhaps it belongs as part of Kurzweil's 'R' technologies?

There is no denying that GNR technologies are powering the exponential growth of human capbility that will ultimately create a technoligical Singularity, but without 'C' technologies as well, none of the others would be possible, or even matter. What else do you think Kurzweil potentially left out of his definition of  the Singularity?

By 33rd Square

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