We examine designs for crowdsourcing contests, where participants compete for rewards given to superior solutions of a task. We theoretically analyze tradeoffs between the expectation and variance of the principal's utility (i.e. the best solution's quality), and empirically test our theoretical predictions using a controlled experiment on Amazon Mechanical Turk. Our evaluation method is also crowdsourcing based and relies on the peer prediction mechanism. Our theoretical analysis shows an expectation-variance tradeoff of the principal's utility in such contests through a Pareto efficient frontier. In particular, we show that the simple contest with 2 authors and the 2-pair contest have good theoretical properties. In contrast, our empirical results show that the 2-pair contest is the superior design among all designs tested, achieving the highest expectation and lowest variance of the principal's utility.
Scientists in Toronto are developing an artificial intelligence system that would help people with Alzheimer's disease or other cognitive impairments live safely at home. The Toronto Rehabilitation Institute is working with University of Toronto researchers to make home-based computer systems that would assist elderly people with memory loss in living independently. More than 750,000 Canadians will have Alzheimer's or a related dementia by 2031, according to the researchers. "Often when a person gets moderate to severe levels of impairment, they are taken out of their home and put into a care facility," lead scientist Alex Mihailidis said in a written statement. "We are using artificial intelligence to support aging-in-place so that people can remain in their homes for as long as possible."
Researchers compared how well children learnt from an iPad app to how well they learnt speaking in-person with an instructor. Millions of devastated Tinder users are forced to spend a... Is Apple expanding into digital GLASSES? Report suggests the... WhatsApp finally launches video calls: Feature will come to... The app that lets the colorblind see the world in a new... Millions of devastated Tinder users are forced to spend a... Is Apple expanding into digital GLASSES? Report suggests the... WhatsApp finally launches video calls: Feature will come to...
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Over two days of testimony before Congress earlier this month, Facebook founder and CEO Mark Zuckerberg dodged a litany of questions from lawmakers about how the data of 87 million Americans ended up in the hands of voter profiling firm Cambridge Analytica. The spectacle put a spotlight on the company's murky data-collection and sharing practices, and sparked a much-needed discussion about if and how to hold companies accountable for their handling of user data. However much deserved, Facebook has, so far, born the brunt of public scrutiny for what has unfortunately become standard practice for web platforms and services. As the Ranking Digital Rights 2018 Corporate Accountability Index--an annual ranking of the some of the world's most powerful internet, mobile, and telecommunications companies that was released this week--shows, companies across the board lack transparency about what user data they collect and share, and tell us alarmingly little about their data-sharing agreements with advertisers or other third parties.
Researchers are combining Twitter, citizen science and artificial intelligence (AI) techniques to develop an early-warning system for flood-prone communities in urban areas. In a study, published in the journal Computers & Geosciences, the researchers showed how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding.