Download Research Tools
Imagine a street dance in which the participants interact not just with their flesh-and-blood counterparts but also with lights and sounds controlled by the dancers’ own movements. That’s what visitors to SummerSalt, an outdoor arts festival in Melbourne, Australia, experienced. The self-choreographed event came courtesy of Encounters, an installation created by the Microsoft Research Centre for Social Natural User Interfaces (SocialNUI), a joint research facility sponsored by Microsoft, the University of Melbourne, and the state government of Victoria. Held in a special exhibition area on the grounds of the university’s Victorian College of the Arts, Encounters featured three Kinect v2 sensors installed overhead.
During a VIP Encounters event on the evening of February 21, several hundred people took part in a Kinect “walk through,” during which dancers and other performers from Victorian College of the Arts mingled with the crowd to create social interactions captured by the Kinect sensors. The results were spectacular visual and audio effects, as the participants came to recognize that their movements and gestures controlled the music and sound effects as well as the light displays on an enormous outdoor screen.
Social interactions facilitated by natural user interfaces were the focus of the Encounters event.
Researchers from SocialNUI conducted qualitative interviews while members of the public interacted with their Kinect-generated effects, probing for insights into the social implications of the experience. As Frank Vetere, director of SocialNUI, explained, “The center explores the social aspects of natural user interfaces, so we are interested in the way people form, come together and explore the public space. And we are interested in the way people might claim and re-orient the public space. This is an important part of taking technological developments outside of our lab and reaching out to the public and other groups within the University.”
Su Baker, director of the Victorian College of the Arts, said, “One of the great crossovers that’s happening now in art is [its] relationship [with] emerging technologies, and we have a number of students with a real interest in how emerging technologies can be used in their work.”
This unique, cross-disciplinary collaboration was a wonderful success, delighting not only the NUI researchers and art students but also the public participants.
—John Warren, Senior Research Program Manager, Microsoft Research
Machine learning is the cornerstone of today’s modern data analysis. The gurus of “big data” analytics are all well versed in machine learning, but most domain specialists still must hire data scientists to meet their data-analysis needs. It's inevitable, though, that the data-modeling chain will become largely automated—simplified to the point where off-the-shelf data transformation tools will be as pervasive as those for word processing and spreadsheets. Data analysis will then be like driving a car: the user will focus on the route to the destination, without worrying about how the engine works.
We refer to this vision as the automation of machine learning, or AutoML for short. To help advance towards this grand goal, ChaLearn, an organization that promotes machine-learning challenges, has launched a contest to help democratize machine learning. Built on the new CodaLab platform, the contest offers US$30,000 in prizes donated by Microsoft. More than 60 teams already have entered the contest during the Prep round, and now, until October 15, 2015, you can enter any of five additional rounds: novice, intermediate, advanced, expert, or master. Visit the ChaLearn Automatic Machine Learning Challenge site to see the deadlines for each round. You can enter even if you have not participated in previous rounds.
Five rounds remain in the Automatic Machine Learning Challenge, each round consisting of AutoML and Tweakathon phases.
The contest problems are drawn from a variety of domains. They include challenges in the classification of text, the prediction of customer satisfaction, the recognition of objects in photographs, the recognition of actions in video data, as well as problems involving speech recognition, credit ratings, medical diagnoses, drug effects, and the prediction of protein structures.
Five datasets of progressive difficulty are introduced during each round. The rounds alternate between (1) AutoML phases, during which submitted code is blind tested in limited time on our platform, using datasets you have never seen before; and (2) Tweakathon phases, in which you are given time to improve your methods by tweaking them on those datasets and running them on your own systems, without computational resource limitation and without requirement of code submission.
During the novice round, which runs through April 14, you will encounter only binary classification problems, with no missing values and no categorical variables. All the datasets are formatted as simple data tables—no sparse matrix format, though one dataset does include a lot of zeros. The classes are balanced. The number of features does not exceed 2,000, and the number of examples does not exceed 6,000. The metric of evaluation is simply classification accuracy.
For more details, read our white paper.
Enter the AutoML challenge for a rich learning and research experience, and a chance to win!
—Isabelle Guyon, President, ChaLearn; Evelyne Viegas, Director, Microsoft Research; Rich Caruana, Senior Researcher, Microsoft Research
More than half of the world’s population now lives in cities and suburbs, and as just about any of these billions of people can tell you, urban traffic can be a nightmare. Cars stack up bumper-to-bumper, clogging our highways, jangling our nerves, taxing our patience, polluting our air, and taking a toll on our productivity. In short, traffic jams impair on our emotional, physical, and economic wellbeing.
A study by the Brazilian National Association of Public Transport showed that the country’s traffic exacted an economic toll of about US$7.2 million in 1998. Unfortunately, it’s only getting worse; there are now about three times as many vehicles in Brazil, making traffic exponentially worse, according to Fernando de Oliveira Pessoa, a traffic expert in Belo Horizonte, Brazil’s sixth-largest city.
Microsoft Research has joined forces with the Federal University of Minas Gerais, home to one of Brazil’s foremost computer science programs, to tackle the seemingly intractable problem of traffic jams. The immediate objective of this research is to predict traffic conditions over the next 15 minutes to an hour, so that drivers can be forewarned of likely traffic snarls.
The aptly named Traffic Prediction Project plans to combine all available traffic data—including both historic and current information gleaned from transportation departments, Bing traffic maps, road cameras and sensors, and the social networks of the drivers themselves—to create a solution that gets motorists from point A to point B with minimal stop-and-go. The use of historic data and information from social networks are both unique aspects of the project.
By using algorithms to process all these data, the project team intends to predict traffic jams accurately so that drivers can make smart, real-time choices, like taking an alternative route, using public transit, or maybe even just postponing a trip. The predictions should also be invaluable to traffic planners, especially when they are working to accommodate traffic from special events and when planning for future transportation needs.
Achieving reliable predictions will involve processing terabytes of data, which is why the researchers are using Microsoft Azure as the platform for the service. The exceptional scalability, immense storage capacity, and prodigious computational power of Microsoft Azure makes it the perfect resource for this data-intensive project. And because Microsoft Azure is cloud-based, running the Traffic Prediction service on Azure makes it accessible to all users, in real time, all of the time.
To date, the researchers have tested their prediction model in some of the world’s most traffic-challenged cities: New York, Los Angeles, London, and Chicago. The model achieved a prediction accuracy of 80 percent, and that was based on using only traffic-flow data. The researchers expect the accuracy to increase to 90 percent when traffic incidents and data from social networks are folded in.
So the next time your highway resembles a long, thin parking lot, you might calm yourself by contemplating how Microsoft Azure and the Traffic Prediction Project might help you avoid such tie-ups in the future.
—Juliana Salles, Senior Program Manager, Microsoft Research