"Fluther, a startup that crowdsources answers for user questions, just raised $600,000 from some of Silicon Valley’s better-known investors. They include Netscape founder Marc Andreessen of newly-formed venture capital firm Andreessen Horowitz, Ron Conway (who has invested in dozens of start-ups in Fluther’s space), Dave McClure and Naval Ravikant. Twitter co-founder Biz Stone is also an advisor.
Although there are already many crowdsourced question-and-answer sites, including Yahoo Answers, WikiAnswers and newer variants like Hunch and Aardvark, which digs through your social network for people to answer, Fluther says it’s different because it works in real-time and finds people across its entire network to answer your question.
The site is very intuitive. You type in a question and wait for people to respond. You can see live if someone is crafting a response, and they can see your reaction in return as you type it. Fluther says questions on the site average about 14 responses."...
The sorting algorithm and Sidewiki's ability to display notes about the same topic on various sites make Sidewiki somewhat unique. Google uses a quote from a speech by President Obama as an example here. Sidewiki will recognize the quote on multiple sites and aggregate them together, no matter whether somebody commented on this quote on Little Green Footballs or Daily Kos. You can also leave a comment about the entire page, of course.
For some popular sites that haven't been annotated yet, Google will also pop up a notification that comments exist, but the sidebar will actually be filled with related blog posts, which is another feature that makes Google stand out from the competition in this field."...
"Netflix, the movie rental company, has decided its million-dollar-prize competition was such a good investment that it is planning another one. The company’s challenge, begun in October 2006, was both geeky and formidable: come up with a recommendation software that could do a better job accurately predicting the movies customers would like than Netflix’s in-house software, Cinematch. To qualify for the prize, entries had to be at least 10 percent better than Cinematch.
The winner, formally announced Monday morning, is a seven-person team of statisticians, machine-learning experts and computer engineers from the United States, Austria, Canada and Israel. The multinational team calls itself BellKor’s Pragmatic Chaos. The group — a merger of teams — was the longtime frontrunner in the contest, and in late June it finally surpassed the 10 percent barrier. Under the rules of the contest, that set off a 30-day period in which other teams could try to beat them.
...
The Netflix contest has been widely followed because its lessons could extend well beyond improving movie picks. The researchers from around the world were grappling with a huge data set — 100 million movie ratings — and the challenges of large-scale predictive modeling, which can be applied across the fields of science, commerce and politics.
The way teams came together, especially late in the contest, and the improved results that were achieved suggest that this kind of Internet-enabled approach, known as crowdsourcing, can be applied to complex scientific and business challenges.
That certainly seemed to be a principal lesson for the winners. The blending of different statistical and machine-learning techniques “only works well if you combine models that approach the problem differently,” said Chris Volinsky, a scientist at AT&T Research and a leader of the Bellkor team. “That’s why collaboration has been so effective, because different people approach problems differently.”
Yet the sort of sophisticated teamwork deployed in the Netflix contest, it seems, is a tricky business. Over three years, thousands of teams from 186 countries made submissions. Yet only two could breach the 10-percent hurdle. “Having these big collaborations may be great for innovation, but it’s very, very difficult,” said Greg McAlpin, a software consultant and a leader of the Ensemble. “Out of thousands, you have only two that succeeded. The big lesson for me was that most of those collaborations don’t work.”
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The new contest is going to present the contestants with demographic and behavioral data, and they will be asked to model individuals’ “taste profiles,” the company said. The data set of more than 100 million entries will include information about renters’ ages, gender, ZIP codes, genre ratings and previously chosen movies. Unlike the first challenge, the contest will have no specific accuracy target. Instead, $500,000 will be awarded to the team in the lead after six months, and $500,000 to the leader after 18 months.
The payoff for Netflix? “Accurately predicting the movies Netflix members will love is a key component of our service,” said Neil Hunt, chief product officer."
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