We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care provider wanted to understand the effect of demographic and behavioral questions on predicting which services a woman would need. The third and final example is in government innovation: using online restaurant reviews from Yelp, competitors built models to forecast which restaurants were most likely to have hygiene violations when visited by health inspectors. Finally, we reflect on the unique benefits of the open, public competition model.

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshold of variables importance is accessed using the quadratic distance of variables importance while the optimal subset of selected variables is used to perform predictions. Finally the results revealed to be qualitatively better, at the selection, the prediction , and the CPU time point of view than those obtained by GLM-Lasso method.

Pruning of redundant or irrelevant instances of data is a key to every successful solution for pattern recognition. In this paper, we present a novel ranking-selection framework for low-length but highly correlated instances. Instead of working in the low-dimensional instance space, we learn a supervised projection to high-dimensional space spanned by the number of classes in the dataset under study. Imposing higher distinctions via exposing the notion of labels to the instances, lets to deploy one versus all ranking for each individual classes and selecting quality instances via adaptive thresholding of the overall scores. To prove the efficiency of our paradigm, we employ it for the purpose of texture understanding which is a hard recognition challenge due to high similarity of texture pixels and low dimensionality of their color features. Our experiments show considerable improvements in recognition performance over other local descriptors on several publicly available datasets.

IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs).

It has been the dream of science fiction fans since they first saw Captain Kirk and Spock disappear from the deck of the Enterprise, only to reappear on a planet in a haze of light. Now scientists in Russia are on a mission to bring Star Trek-style teleportation to life as part of a multi-trillion Rouble research and development drive. Russian investors say the plan isn't as far-fetched as it may seem, with much of the common technology used today inspired by sci-fi of decades gone by. The Russian government could develop the technology by 2035 as part of a 2.1 trillion ( 1.4 trillion) research and development drive. Investors say the plan isn't as far-fetched as it may seem, with much of the common technology used today inspired by sci-fi of decades gone by.

MUNICH (Reuters) – Europe's growing army of robot workers could be classed as "electronic persons" and their owners liable to paying social security for them if the European Union adopts a draft plan to address the realities of a new industrial revolution. Robots are being deployed in ever-greater numbers in factories and also taking on tasks such as personal care or surgery, raising fears over unemployment, wealth inequality and alienation. Their growing intelligence, pervasiveness and autonomy requires rethinking everything from taxation to legal liability, a draft European Parliament motion, dated May 31, suggests. Some robots are even taking on a human form. Visitors to the world's biggest travel show in March were greeted by a lifelike robot developed by Japan's Toshiba and were helped by another made by France's Aldebaran Robotics.

Gone are the days when tossing a mattress in the back of a truck was the only way to get it home from the store. The booming'boxed bed' business has taken over the mattress industry, allowing consumers to purchase their bedding online and have it shipped to their doorstep compressed inside of a box. And a new video shows the monstrous Auto-Pac 1390HCA system using a 60-ton hydraulic press and highly consistent rolling to neatly fold your mattress to be bagged and sealed for delivery. A new video shows the monstrous Auto-Pac 1390HCA system using a 60-ton hydraulic press and highly consistent rolling to neatly fold a mattress to be bagged and sealed for delivery. The first step of preparing a mattress for shipping is to cover it in a protective plastic wrap.

Google is ringing in 2014 with a spending spree, first dropping 3.2 billion to acquire Nest Technologies and now spending a reported 400 million (or more) on the UK-based artificial intelligence outfit DeepMind. It's no secret that Google has an interest in artificial intelligence; after all, technologies derived from AI research help fuel Google's core search and advertising businesses. AI also plays a key role in Google's mobile services, its autonomous cars, and its growing stable of robotics technologies. And with the addition of futurist Ray Kurzweil to its ranks in 2012, Google also has the grandfather of "strong AI" on board, a man who forecasts that intelligent machines may exist by midcentury. If all this sounds troubling, don't worry: Google's acquisition of DeepMind isn't about fusing a mechanical brain with faster-than-human robots and giving birth to the misanthropic Skynet computer network from the Terminator franchise.

We're all a bit worried about the terrifying surveillance state that becomes possible when you cross omnipresent cameras with reliable facial recognition -- but a new study suggests that some of the best algorithms are far from infallible when it comes to sorting through a million or more faces. The University of Washington's MegaFace Challenge is an open competition among public facial recognition algorithms that's been running since late last year. The idea is to see how systems that outperform humans on sets of thousands of images do when the database size is increased by an order of magnitude or two. "We're the first to suggest that face recs algorithms should be tested at'planet-scale,'" wrote the study's lead author, Ira Kemelmacher-Shlizerman, in an email to TechCrunch. "I think that many will agree it's important. The big problem is to create a public dataset and benchmark (where people can compete on the same data). Creating a benchmark is typically a lot of work but a big boost to a research area."

The U.S. Defense Department's research and development arm is offering to fund projects that will simplify the massively complex task of building models for machine learning applications. Models are a fundamental part of machine learning. Similar to algorithms, they help teach computers to, say, identify a cat in a photo, forecast weather from historical data or sort spam from legitimate email. But writing the models takes time and requires many skills. Typically, data scientists, subject matter experts and software engineers all have to come together to develop the model. When New York University researchers wanted to model block-by-block traffic flow data for the city, it took 60 person-months of work by data scientists to prepare the data for use and an additional 30 person-months to develop the model.