And it could be just what the science needs. But physics is still about 15 years behind the cutting edge in this area, Kégl says. Machine learning-known in physics circles as multivariate analysis-played a small role in the 2012 discovery of the Higgs. “If due to this challenge physicists of the collaboration discover they have a friendly machine learning expert in the lab next door and they try to work together, that’s even better than just getting a new algorithm.” The team spent about 18 months working on organizing the contest in the hopes that it would create just this kind of crossover, Rousseau says. “It captures their imagination.”Ī couple of the top contenders are physicists, but most come from outside the particle physics community. “People love this type of problem,” Noah-Vanhoucke says. Names appear and drop off of the leaderboard every day. But the Higgs contest, which does not end until September, has already drawn almost 970. Most of them will attract between 300 and 500 teams, Noah-Vanhoucke says. Kaggle is currently running about 20 contests on its site. Often contestants play for cash, but they have also competed for the chance to interview for data scientist positions at Facebook, Yelp and Walmart. “We’re trying to be the home of data science on the internet,” she says. Kaggle contests attract a mixed crowd of professional data scientists looking for fresh challenges, grad students and postdocs looking to test their skills, and newbies looking to get their feet wet, says Joyce Noah-Vanhoucke, Kaggle data scientist and head of competitions. They have asked data scientists to foresee the creditworthiness of loan applicants, to predict the toxicity of molecular compounds and to determine the sentiment of lines from movie reviews on the film-rating site Rotten Tomatoes. The company running the contest, Kaggle, based in San Francisco, holds such challenges for research institutions and also businesses such as Liberty Mutual, Allstate, Merck, MasterCard and General Electric. The contest was conceived of by a six-person group led by two senior researchers at France’s national scientific research center, CNRS: physicist David Rousseau, who served from 2010 to 2012 as software coordinator for the ATLAS experiment, and machine-learning expert Balázs Kégl, who since 2007 has been looking for ways to bring machine learning into particle physics. In addition, whoever has the most useable algorithm will be invited to CERN to see the ATLAS detector and discuss machine learning with LHC scientists. When Kaggle receives a submission, it grades, in real time, just a portion of it-to prevent people from gaming the system-and then places the contestant on its public leaderboard.Īt the end of the Higgs contest, Kaggle will reveal whose algorithm did the best job analyzing the full dataset. When they’re ready, they unleash the algorithms on the unlabeled collision data and try to figure out where the Higgs is hiding.Ĭontestants submit their answers online to Kaggle, a company that holds the answer key. They must use this labeled fraction to train their algorithms to find patterns that point to the Higgs boson. Contestants receive all of these details, but only 250,000 of the collisions are labeled “Higgs” or “non-Higgs.” The data for each collision contains 30 details-including variables such as the energy and direction of the particles coming out of it. The collisions can be sorted into two groups: those with a Higgs boson and those without. The contest works like this: Participants receive data from 800,000 simulated particle collisions from the ATLAS experiment at the Large Hadron Collider. They’re vying for prizes up to $7000, but according to contest organizers, the real winner might be the particle physics community, whose new connections with the world of data science could push them toward new methods of discovery. More than 1000 individuals have already joined the race. Scientists have created a contest that invites anyone to use machine learning-the kind of computing that allows Facebook to spot your friends in photos and Netflix to recommend your next film-to search for the Higgs boson.
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