Posts tagged crowdsourcing
Metaknowledge functions as a powerful bullshit detector. It can separate crowd members who actually know something from those who are guessing wildly or just parroting what everyone else says. ‘The crowd community has been insufficiently ambitious in what it tries to extract from the crowd,’ Prelec says. ‘The crowd is wise, but not in the way the error-correcting intuition assumed. There’s more information there.’ The bullshit detector isn’t perfect, but it’s the best you can do whenever you don’t know the answer yourself and have to rely on other people’s opinion. Which eyewitness do you believe? Which talking head on TV? Which scientist commenting on some controversial topic? If they demonstrate superior metaknowledge, you can take that as a sign of their superior knowledge.
via https://aeon.co/essays/a-mathematical-bs-detector-can-boost-the-wisdom-of-crowds
When providing directions to a place, web and mobile mapping services are all able to suggest the shortest route. The goal of this work is to automatically suggest routes that are not only short but also emotionally pleasant. To quantify the extent to which urban locations are pleasant, we use data from a crowd-sourcing platform that shows two street scenes in London (out of hundreds), and a user votes on which one looks more beautiful, quiet, and happy. We consider votes from more than 3.3K individuals and translate them into quantitative measures of location perceptions. We arrange those locations into a graph upon which we learn pleasant routes. Based on a quantitative validation, we find that, compared to the shortest routes, the recommended ones add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy.
http://arxiv.org/abs/1407.1031
“There’s no shortage of guidelines these days on how to ‘prepare for the future.’ […] foresight engines are pulling in thousands of citizens to re-imagine the future of governance, cities, and peacebuilding. They’re generating over 1,800 paths out of poverty and through the Good Judgment Project, 3,000 regular citizens are making forecasts on a range of issues – from political developments in North Korea to Venezuelan gas subsidies.”
http://europeandcis.undp.org/blog/2014/04/04/the-future-is-now-heres-how-were-planning-to-catch-up/
Our empirical and theoretical findings have cautionary implications for the future of social search, and crowdsourcing in general. Social search is surprisingly efficient, cheap, easy to implement, and multi-functional across aplications. But there are also surprises in the amount of evildoing that the social searchers will stumble upon while recruiting.
http://socialphysics.media.mit.edu/2014/02/02/a-letter-from-the-social-search-frontier-good-news-bad-news/
The Descriptive Camera works a lot like a regular camera—point it at subject and press the shutter button to capture the scene. However, instead of producing an image, this prototype outputs a text description of the scene. Modern digital cameras capture gobs of parsable metadata about photos such as the camera’s settings, the location of the photo, the date, and time, but they don’t output any information about the content of the photo. The Descriptive Camera only outputs the metadata about the content.
http://mattrichardson.com/Descriptive-Camera/