Massachusetts Institute of Technology
CCI RESEARCH PROJECTS
Creating new examples of collective intelligence
Climate CoLab
Using new collaboration tools, this project is attempting to harness the collective intelligence of large numbers of people to address the problem of global climate change.
Collective Prediction
This project will attempt to combine human and machine intelligence in flexible new ways to make accurate predictions about future events such as product sales, political events, and outcomes of medical treatments.
Deliberatorium
This project is exploring how to integrate ideas from argumentation theory and social computing to help large numbers of people enumerate the issues, ideas, and tradeoffs for complex problems with much greater signal-to-noise and much more systematic organization than existing (e.g. forum, wiki, or idea-sharing) technologies.
Nonlinear Negotiation
This project is investigating ways to help large numbers of individuals come to agreements about complex problems with many interdependent issues.
Enabling Knowledge Management
This project explores how emerging media technologies, including social computing and virtual reality, can enable new more powerful modes of knowledge management.
Studying collective intelligence in today's organizations
Distributed Collaboration
Using interviews and case studies, this project will examine the various technologies and practices used for distributed collaboration in organizations.
Sensible Organizations
This project is using new sensors embedded in wearable "social badges" as a kind of "information microscope" to systematically analyze organizations at a much finer grained level than has been done before.
Collaborative Innovation Networks
The goal of this research project is to help organizations increase knowledge worker productivity and innovation by studying Collaborative Innovation Networks (COINs).
Coolhunting - Identifying Trends Through Online Social Media Analysis
In this project we study a wide range of methods for predictive analytics (coolhunting) mostly based on social network analysis and the emerging science of collaboration.
Developing theories of collective intelligence
Genome of Collective Intelligence
The Genome of Collective Intelligene project is developing a taxonomy of organizational building blocks, or genes, that can be combined and recombined to harness the intelligence of crowds. .
Measuring Collective Intelligence
Intelligence tests can predict the performance of individuals across a broad range of tasks. Imagine if we had an instrument that could predict the performance of groups—combinations of people assisted by computers, telecommunications links, and other man-made devices—across a range of relevant tasks. The goal of this project is to determine whether such an instrument is feasible, and if so, to develop and test it.
CSAIL/UID RESEARCH PROJECTS
Crowd Computing
Turkit
TurKit is a Java/JavaScript API for running iterative tasks on Mechanical Turk. You can safely re-execute TurKit programs without re-running costly side effects on Mechanical Turk, like creating new HITs, but still write your program in a straightforward imperative manner - there is no need to unravel the program into a state machine.
Soylent
Soylent is a word processor with a crowd inside: an add-in to Microsoft Word that uses crowd contributions to perform interactive document shortening, proofreading, and human-language macros
Twitinfo
TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100% of manually labeled peaks, facilitating a relatively complete view of each event studied.
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