A year after launching our crowd-source reporting project, Your Uncommon Economic Indicators, we have been asked how we sustained an online mapping and economy project for so long. The short answer is that we value new ways to collaborate with our listeners and we constantly look for innovative methods to do that. The long answer is what you will see in these pages–a guide to a collaborative reporting project between a public radio station and its audience.
CoCreating Cultures is a project and a platform to study and apply new methods for letting groups create collaborative cultures. We design for emergent human-centered complex processes. We focus on science, technology and art groups and institutions but we also work on change in organizations.
(Video) CrowdTrust manages crowdsourcing projects through dynamic social networks of verified topic experts and user communities. A prototype tool is under development to gauge the effect of collaborative newsgathering on quality, speed and cost of reporting.
Collective intelligence comprises of individual knowledge and competence of the members of a virtual community who improve the level of their expert skills by communicating and cooperating with each other
Apture has just launched a feature that could change that. “Hotspots,” which builds on Apture’s Highlights function, leverages the curiosity of the crowd to determine, quite literally, the web’s “missing links.” The feature measures and tracks the number of times users have highlighted certain text within Apture-enabled websites; and then — here’s the very, very cool part — automatically creates a cluster of contextual information to support that text, via Wikipedia, YouTube, maps, and more. It’s Apture’s in-line-but-also-expandable version of a hyperlink.
It's been three years since Jeff Howe coined the term "crowdsourcing" in his Wired article "The Rise of Crowdsourcing." The term, which describes an online, distributed problem solving and production model, is most famously represented in the business operations of companies like Threadless and InnoCentive and in contests like the Goldcorp Challenge and the Doritos Crash the Super Bowl Contest.
In each of these cases, the company has a problem it needs solved or a product it needs designed. The company broadcasts this challenge on its Web site to an online community--a crowd--and the crowd submits designs and solutions in response. Next--and this is a key component of crowdsourcing--the crowd vets the submissions of its peers, critiquing and ranking submissions until winners emerge.
Tools were made to the same monotonous design for hundreds of thousands of years and the ecological impact of people was minimal. Then suddenly—bang!—culture exploded, starting in Africa. Why then, why there?
The answer lies in a new idea, borrowed from economics, known as collective intelligence: the notion that what determines the inventiveness and rate of cultural change of a population is the amount of interaction between individuals
Researchers have turned the biochemical challenge of figuring out protein folding structures into a computer game. The best players can beat a computerized algorithm by rapidly recognizing problems that the computer can't fix.Today's issue of Nature contains a paper with a rather unusual author list. Read past the standard collection of academics, and the final author credited is... an online gaming community.
Scientists have turned to games for a variety of reasons, having studied virtual epidemics and tracked online communities and behavior, or simply used games to drum up excitement for the science. But this may be the first time that the gamers played an active role in producing the results, having solved problems in protein structure through the Foldit game.he Nature article makes it clear that researchers in other fields, including astronomy, are starting to try similar approaches to getting the public to contribute something other than spare processor time to scientific research. As long as the human brain continues to outperform computers on some tasks, researchers who can harness these differences should get a big jump in performance.
Openly sharing projects, tasks, documents and discussions exposes the collective intelligence that is largely unseen at most organizations, again making it easier for folks to find information they need.
My new side project - ShouldInvent is online, which is basically an automatic idea aggregation web application based on Twitter Search. The primary motivation is to provide inspirations for engineers in various domains.
a) Collective computational intelligence involves collaboration between software agents, with a new level of computational intelligence emerging form their collaboration. These technologies involve swarm intelligence, ant colony simulation, web services, grid computing, distributed cloud computing and multi-agent computing in general. b) Computational collective intelligence is a more multidisciplinary field. Its subject is the understanding of human collective intelligence and its augmentation by the means of ubiquitous distributed automatic symbol manipulation.
A challenge of managing a virtual team is getting timely and thorough input and participation from team members. Whether it’s voicing an opinion on an internal company policy or putting in their two cents during a creative brainstorming session for a client project, not being in the same room can leave some folks out of the mix, despite your best efforts to be inclusive. One way to ensure everyone has their say — or is at least given the opportunity to provide input — is to apply some principles of crowdsourcing to internal team communications.
The collective intelligence of an ant colony can serve as inspiration to help us solve complex human problems. Businesses in particular are finding innovative ways to apply these lessons from nature, from routing trucks to managing plane congestion on the tarmac... to making Internet search more accurate.
Collective Intelligence (CI) is complex by nature. It is not merely the sum of what a group knows, but the intelligence that emerges from it. This “emergent” behavior is of a different nature and we can see it at play in perfectly balanced ecosystems where swarm-like behavior produces astounding results that exceed the intelligence of any one creature acting alone. [
By looking at collective intelligence (CI) through four distinct lenses, this paper draws on recent research in organizational design, evolutionary economics, cognitive sciences, knowledge ecology and political economy to built a twin path forward: collective intelligence and collective leadership. It lays out elements of a framework for building this twin path beyond chaos. It is our intent to invite conversations designed to engage questions surrounding this interdependent evolutionary path. How might we develop criteria for a design capable of supporting a large range of collective intelligence phenomena in an integrated way?
STDP is a simple idea, but it has been shown to be a surprisingly powerful way that the brain uses for rapid pattern recognition and classification . It turns out that using STDP, neurons naturally learn to specialize in detecting certain patterns in their inputs, even in the presence of lots of noise.
So what in the world does this have to do with social networks? There is an intriguing analogy between networks of neurons operating by the STDP rule and the emerging structure and functioning of real-time social networks like Twitter.
The immediate question this raises, of why we are so much better at collective endeavors than the ancients, can be answered fairly simply. To a first approximation, the answer is: brute force and massive literacy. We teach nearly everyone to read and write, and to do it, by historical standards, at a high level. This lets us staff large bureaucracies (by some estimates, over 40% of the US workforce does data-handling), which lets us run an industrial economy (the trains run on time), which makes us rich enough to afford to educate everyone and keep them in bureaucratic employment, with some surplus left over to expand the system. This would do us no good if our ideas of administration were as shabby as those of our ancestors in the dark ages, but they're not: we inherited those of the ancient empires, and have had quite a while to improve upon them (and improvements are made easier and faster by the large number of administrators and the high standard of literacy).
My colleague Ankit Sharma at the London School of Economics (LSE) recently sent me his research paper entitled “Crowdsourcing Critical Success Factor Model” (PDF). It’s definitely worth a read. Ankit is interested in better understanding the “dynamic and innovative discipline of crowdsourcing by developing a critical success factor model for it.”
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