Crowd computing

Eric Brown, co-author of "The Effective CIO", alluded to the term "Crowdcomputing" in 2009.It is an overarching term which defines the myriad tools that enable idea sharing, non-hierarchical decision making and the full utilization of the world’s massive "cognitive surplus”-the ability of the world’s population to collaborate on large, sometimes global projects. Crowd computing brings together the strengths of crowdsourcing, automation and machine learning.

Prof. Rob Miller of MIT further defines crowd computing as “harnessing the power of people out in the web to do tasks that are hard for individual users or computers to do alone. Like cloud computing, crowd computing offers elastic, on-demand human resources that can drive new applications and new ways of thinking about technology.”

Crowd computing offers a harmonious amalgamation of both cloud computing and crowdsourcing. It combines human intelligence (the crowd) with artificial intelligence (the cloud) in order to produce quality results at unprecedented speed. Scientists and historians are already utilizing this process to complete time-consuming research, and many businesses are beginning to realize its potential for cutting costs and increasing productivity. Crowd computing very well may be on its way to changing the way humans live and operate in our society by using artificial intelligence in combination with the human mind.

Srini Devadas, professor of electrical engineering and computer science at the MIT Computer Science and Artificial Intelligence Laboratory, asserts that “crowd computing will complement the cloud as one of two burgeoning infrastructures that will enable the world to become more ‘collectively intelligent’.”

Four key process and technology innovations are the core of crowdcomputing:

• Microtasking. Work is broken into small components that are easier to complete by the crowd.
• Automation. Machines complete repetitive work, leaving judgement work to humans
• Hybrid Crowd: Higher value work and a greater volume of work can be completed when specialists, crowd workers and machines work together.
• AI: Machine learning creates a cascade of knowledge that enables more and more automation and continuously optimizes cost and quality.

Crowdcomputing tools and platforms

Businesses and society in general increasingly rely on the combined intelligence, knowledge, bandwidth and life experiences of the ‘crowd to improve processes, make decisions, identify solutions to complex problems and monitor changes in consumer taste. Companies like Amazon and Google saw early-on the potential for crowd computing. In 1995, Amazon created Mechanical Turk to deal with its internal problem of sorting its massive inventory. The platform organizes people from around the globe to ‘work efficiently as a giant machine.” Google uses a captcha to help digitize books. Major sites like Facebook and Twitter rely on the crowd to power the translation that spreads their service around the globe.

Enterprise Crowd Computing

The premise of leveraging the triumverate of crowd sourcing, automation and machine learning accelerates the speed, improves the accuracy and reduces the cost of work historically completed through outsourced workers and frees internal analysts to do higher-value work. The enterprise, as a result, experiences exponentially greater productivity.

The author on innovation Braden Kelley deems this shift a ‘revolution’ and credits crowdcomputing with the redesign of work that is now possible thanks to new technology tools and business architecture thinking that will allow man and machine to work more efficiently together than ever before.

CrowdComputing Systems, Inc. first brought crowd computing to the enterprise level in 2012 through a software platform with technology that was originally designed at MIT for fraud-detection work; it was later deemed to have more relevance for enterprise crowdsourcing and machine intelligence.

The company’s platform automates tasks and combines human labor sourced from companies such as Elance, Odesk and Amazon Mechanical Turk to create, manage and enhance an on-demand content and data workforce.

History

In the late 1800s, a team of British archeologists in Egypt stumbled upon a half million pieces of 2000-year-old papyrus, each with remarkably well-preserved text requiring translation. The pieces were shipped from the desert to Oxford University, where generations of scholars have been working to decipher their writings ever since. After over a hundred years, only about 15 percent of the collection had been completed. These manuscripts contain remarkably significant pieces of history, including the controversial Gospel of Thomas and the lost comedies of Athenian playwright Menander. In 2011, however, the scholars decided to speed up the process by leveraging the crowd. They launched a website, Ancient Lives, with a game that tasks members to translate small bits of the text from home. As of November of 2011, users had already provided 4 million transcriptions, helping to identify Thucydides, Aristophanes, Plutarch’s “On the Cleverness of Animals” and much more.

Another early example includes when, at the end of the 18th century, the British Royal Astronomers distributed spreadsheets by mail, asking the crowd to help them create maps of the stars and the seas. It reached its height in the United States during the 1930s, when the government employed hundreds of “human computers” to work on the WPA and the Manhattan Project. The word “computer,” as it applies to our modern-day devices, came directly from these mathematically minded men and women.

An additional early example of crowd computing was the discovery of a gold deposit location at the Moribund Red Lake Mine in Northern Ontario. Using all available data, the company, Goldcorp, Inc. had been unable to identify the location of new deposits on their land. In desperation, the CEO put all relevant geological data on the web and created a contest, open to anyone in the world. An obscure firm in Australia used their software and algorithms to crack the puzzle. As a result, the company found an additional 8 million ounces of gold at the mine. The only cost was the nominal prize money awarded.

The modern day microchip made using large crowds for mechanical computation less attractive in the second half of the twentieth century. However, as the volume of data online grew, it became clear to companies like Amazon and Google that there were some things humans were simply better at doing than machines.

See Also

• Citizen Science
• Crowdsourcing

References

• Brown, Eric J. and William A. Yarberry, Jr. (2009). The Effective CIO. Boca Raton: Taylor & Francis.
• Shirky, Clay. Cognitive Surplus: Creativity and Generosity in a Connected Age (2010) - ISBN 978-1-59420-253-7
• Surowiecki, J. (2005). The Wisdom of Crowds. New York: Random House, Inc.
• Cheminformatics Crowd Computing for Tuberculosis Drug Discovery (3C4TB) Project Page