From Horizon Project
Time-to-Adoption: Four to Five Years
 Collective Intelligence
Collective intelligence is a term for the kinds of knowledge and understanding that emerge from large groups of individuals. It can be explicit, in the form of knowledge gathered and recorded by many people (for example, the Wikipedia—http://www.wikipedia.org— is the result of collective intelligence); but perhaps more interesting, and more powerful, is the tacit intelligence that results from the data generated by the activities of many people over time. Discovering and harnessing the intelligence in such data allows us to do things like make accurate predictions about preferences and behaviors, understand and map relationships, and gauge the relative significance of ideas and events.
Examples of uses for this type of intelligence already exist in industry. Google’s PageRank system, which assigns value to a web page based on the number of other pages that link to it, uses collective intelligence to determine which web pages are most likely to be relevant in a list of search results. Amazon.com also takes advantage of collective intelligence to recommend purchases that you might like based on your previous purchases and those of your friends—and even based on what other people have also bought, whether you know them or not. LinkedIn makes very accurate recommendations of people you might know, or want to know, based on your extended connections.
The applications of collective intelligence to education are not fully understood. Certainly it is easy to envision ways to apply collective intelligence-based recommendation systems: if you found topic X interesting, you might also like to learn about topic Y; now that you understand concept Q, you might be ready to tackle concept Z. Other applications are likely to be uncovered as we continue to find ways to tap into the collective intelligence embedded in the data on the Internet.
 Relevance for Teaching, Learning & Creative Expression
- Natural applications exist for research in datasets generated by lots of human activity.
- Games that capture data (eg, tagging or word association) can give insight to language study.
- Social encyclopedias like Cellphedia (http://www.cellphedia.com) give students a chance to ask questions or to be the experts with the answers.
- Google Image Labeler and the ESP Game tag photos by matching keywords typed by thousands of players: http://images.google.com/imagelabeler/, http://www.espgame.org
- ReCaptcha provides anti-spam tools that simultaneously digitize scanned text: http://recaptcha.net
 For Further Reading
10 Semantic Apps to Watch
(Richard MacManus, Read/Write Web, November 29, 2007)
This blog post describes ten “semantic apps,” or applications that take advantage of the kinds of data provided by collective intelligence, that are currently in development. http://www.readwriteweb.com/archives/10_semantic_apps_to_watch.php
Panel on Collective Intelligence
(Moderated by David Thorburn, MIT World, October 7, 2007)
This panel discussion, featuring Thomas W. Malone, Alex Pentland, and Karim R. Lakhani, discusses the question of whether a group of people working with smart machines can achieve a greater degree of intelligence than humans or machines alone. Presented as a two-hour video. http://mitworld.mit.edu/video/494/
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