Where do we draw the line between crowd-sourcing pattern recognition
as a menial task and crowd-sourcing pattern recognition and data interpretation to
implement collaborative intelligence?

Just as the boundary between non-life and life is unclear and subject to debate, so the distinction between crowd-sourcing as an instance of anonymous collective intelligence activity and crowd-sourcing as an instance of credited "open source" collaborative intelligence harnessing social networks can be debated, e.g.

• Art production – SwarmSketch is a crowd-sourced art experiment.
• Climate change – Peter Norvig, Director of Research at Google, is investigating the crowd-sourced perception of climate change;Thomas Malone, Director of the MIT Center for Collective Intelligence is exploring the potential for crowd-sourcing action on climate change and has established the MIT Climate Collab;
• DARPA Network Challenge– a competition to explore how the Internet and social networking can support timely communication, wide-area team-building, and urgent mobilization to solve broad-scope, time-critical problems.
• Patent review – New York Law School and the US Patent and Trademark Office are experimenting with peer-to-peer patent review.
• Transit planning –  government experiment in crowd-sourcing transit planning.
• Wikipedia – founder Jimmy Wales on the subtle distinction between structure and principles argues that it's an illusion that crowd-sourcing results "just happen." Similarly collaborative intelligence requires principles and frameworks to enable the integration of contributions to achieve productive results.

Roberto Suro wrote: "The ability to search and analyze petabytes of information is transforming the ways we think about academic research and content analysis. The Lab is building a facility to tackle the challenges of big data and will be establishing collaborative projects with partners both on and off campus. Social networks, datasets, written documents and the web itself can be searched on a vast scale and in unprecedented combinations. Moreover the searches can pursue the relationships between people and places as well as the evolution of concepts."

Can next generation crowd-sourcing innovate better via collaborative problem-solving?

Principles of collaborative intelligence applied first on a small scale to support innovation networks can grow, adapt, and evolve as innovation networks manifest emergent attributes seen in complex, life-like systems, such as self-organisation and evolutionary adaptation.

Luis Von Ahn's 2005 Ph.D. thesis coined the term "human computation" to describe methods that combine human brainpower with computers to solve problems that neither could solve alone. One of his most brilliant ideas capitalizes on his original invention of the captcha code. The idea:

• approximately 200 million CAPTCHAs are typed every day around the world;
• each CAPTCHA takes nearly 10 seconds of time;
• about 500,000 hours of human time are wasted every day typing CAPTCHAs.

They wondered how all this human effort could be used for the greater good of humanity and came up with

reCAPTCHA – harnessing (crowd-sourcing) human effort to digitize books, one word at a time. reCAPTCHA is a free CAPTCHA service that helps to digitize books, newspapers and old time radio shows. More here on reCAPTCHA as a concept to digitize books using human character recognition abilities.

The term "crowdsourcing," which combines "crowd" and "outsourcing," was coined a year later by Jeff Howe in his June 2006 Wired magazine article "The Rise of Crowdsourcing."

Crowd-sourcing repetitive or piecework tasks has traditionally tended to focus on recruiting large numbers of people to perform tasks that cannot easily be automated for computers, e.g. Lukas Biewald, co-founder and CEO of CrowdFlower (formerly Dolores Labs), provides labor-on-demand via access to an always-on, scalable online workforce; Vikram Dendi leads Microsoft's Research on Crowdsourcing, focusing on language translation.

Innocentive has launched a platform for open innovation. Companies pose challenges; innovators are rewarded if they solve them. On February 22, 2011 Innocentive posed its big question:

Can Open Innovation Save the Planet? InnoCentive and Environmental Defense Fund (EDF) have launched a partnership to explore the potential environmental benefits of crowdsourcing ideas and solutions to business sustainability challenges.

Aniket (Nikki) Kittur leads the Carnegie Mellon team creating the tool Crowdforge to tap the writing skills of many. Already blogs have launched, using this tool. Kittur notes that the amount of information available to individuals today is enormous and rapidly increasing. Continued progress in science, education, and technology depends upon making sense of, and finding insights in, overwhelming amounts of data.

Research Question 1. What makes systems successful? Human cognition, while unparalleled at discovering patterns and linking seemingly-disparate concepts, is also limited in the amount of information it can process at once. One promising solution to this problem is through social collaboration, in which groups of individuals collaborate to produce knowledge and solve problems that exceed a single individual's cognitive capacity. Emerging online paradigms that aggregate the efforts of many individuals, such as Digg, del.icio.us, Wikipedia, and Mechanical Turk, prove the power of collaborative intelligence.

Research Question 2. What will the next generation of systems look like? Kittur describes a series of vehicles to harness the power of the crowds for complex and creative information processing tasks, such as Wikipedia, Mechanical Turk.

Kittur is interested in techniques to extend social collaboration to support insight and discovery. His research focuses on understanding and augmenting how humans make sense of large amounts of information and the dynamics of social collaborative systems like Wikipedia and Amazon's Mechanical Turk, and how visualization and machine learning tools can increase their effectiveness. To understand these social collaborative systems he also studies human information processing in categorization, schema induction, and memory, complementing empirical experiments with statistical and computational modeling, and techniques frown from visualization, data mining, and machine learning.

Study of collaborative intelligence suggests that innovation networks manifest emergent attributes seen in complex, life-like systems, such as self-organisation and evolutionary adaptation.

References
Surowiecki, James. 2004. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Boston: Little, Brown ISBN 0-316-86173-1

©2011 Zann Gill Please attribute, linking to this site.
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