![]() |
COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. | ![]() |
University of Cambridge > Talks.cam > Microsoft Research Cambridge, public talks > Harvesting the Wisdom of Crowds
![]() Harvesting the Wisdom of CrowdsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins. This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending Conventional wisdom has it that a crowd is smarter than a single individual, and that many opinions are better than one. Several successful businesses have been formed based on this crowdsourcing theme, such as Wikipedia, InTrade and Amazon’s Mechanical Turk. The notion of crowd intelligence is very appealing, but raises many questions: Can the thoughts of single individuals really be aggregated into one giant mind? Can a crowd outperform its strongest member? What really makes a crowd tick, and what determines how well a crowd can succeed in a task? How can we incentivize crowd members to work hard, and determine the individual contribution of a member towards the performance of the entire crowd? We will examine several experiments designed to shed some light on the above questions, both in a controlled lab setting and in real-world crowdsourcing platforms. This talk is part of the Microsoft Research Cambridge, public talks series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsPembroke Papers, Pembroke College Cambridge Clinical Research Centre for Affective Disorders (C2:AD) Infant SorrowOther talksCANCELLED Jennifer Luff: Secrets, Lies, and the 'Special Relationship' in the Early Cold War What is the Market Potential of Multilingualism? A cabinet of natural history: the long-lost Paston collection Algorithmic Investigation of Large Biological Data sets Part IIB Poster Presentations Art speak |