Tag Archives: natural

Automating big-data analysis : MIT Research

System that replaces human intuition with algorithms outperforms 615 of 906 human teams.

By Larry Hardesty


Big-data analysis consists of searching for buried patterns that have some kind of predictive power. But choosing which “features” of the data to analyze usually requires some human intuition. In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too. To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets. Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions. In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

“We view the Data Science Machine as a natural complement to human intelligence,” says Max Kanter, whose MIT master’s thesis in computer science is the basis of the Data Science Machine. “There’s so much data out there to be analyzed. And right now it’s just sitting there not doing anything. So maybe we can come up with a solution that will at least get us started on it, at least get us moving.”

Between the lines

Kanter and his thesis advisor, Kalyan Veeramachaneni, a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), describe the Data Science Machine in a paper that Kanter will present next week at the IEEE International Conference on Data Science and Advanced Analytics.

Veeramachaneni co-leads the Anyscale Learning for All group at CSAIL, which applies machine-learning techniques to practical problems in big-data analysis, such as determining the power-generation capacity of wind-farm sites or predicting which students are at risk fordropping out of online courses.

“What we observed from our experience solving a number of data science problems for industry is that one of the very critical steps is called feature engineering,” Veeramachaneni says. “The first thing you have to do is identify what variables to extract from the database or compose, and for that, you have to come up with a lot of ideas.”

In predicting dropout, for instance, two crucial indicators proved to be how long before a deadline a student begins working on a problem set and how much time the student spends on the course website relative to his or her classmates. MIT’s online-learning platform MITxdoesn’t record either of those statistics, but it does collect data from which they can be inferred.

Featured composition

Kanter and Veeramachaneni use a couple of tricks to manufacture candidate features for data analyses. One is to exploit structural relationships inherent in database design. Databases typically store different types of data in different tables, indicating the correlations between them using numerical identifiers. The Data Science Machine tracks these correlations, using them as a cue to feature construction.

For instance, one table might list retail items and their costs; another might list items included in individual customers’ purchases. The Data Science Machine would begin by importing costs from the first table into the second. Then, taking its cue from the association of several different items in the second table with the same purchase number, it would execute a suite of operations to generate candidate features: total cost per order, average cost per order, minimum cost per order, and so on. As numerical identifiers proliferate across tables, the Data Science Machine layers operations on top of each other, finding minima of averages, averages of sums, and so on.

It also looks for so-called categorical data, which appear to be restricted to a limited range of values, such as days of the week or brand names. It then generates further feature candidates by dividing up existing features across categories.

Once it’s produced an array of candidates, it reduces their number by identifying those whose values seem to be correlated. Then it starts testing its reduced set of features on sample data, recombining them in different ways to optimize the accuracy of the predictions they yield.

“The Data Science Machine is one of those unbelievable projects where applying cutting-edge research to solve practical problems opens an entirely new way of looking at the problem,” says Margo Seltzer, a professor of computer science at Harvard University who was not involved in the work. “I think what they’ve done is going to become the standard quickly — very quickly.”

Source: MIT News Office

 

Fig. 1: ESO/S. Ramstedt (Uppsala University, Sweden) & W. Vlemmings (Chalmers University of Technology, Sweden)

ALMA reveals Mira’s secret life

Studying red giant stars tells astronomers about the future of the Sun — and about how previous generations of stars spread the elements needed for life across the Universe. One of the most famous red giants in the sky is called Mira A, part of the binary system Mira which lies about 400 light-years from Earth. In this image ALMA reveals Mira’s secret life.

Mira A is an old star, already starting to throw out the products of its life’s work into space for recycling. Mira A’s companion, known as Mira B, orbits it at twice the distance from the Sun to Neptune.

Mira A is known to have a slow wind, which gently molds the surrounding material. ALMA has now confirmed that Mira’s companion is a very different kind of star, with a very different wind. Mira B is a hot, dense white dwarf with a fierce and fast stellar wind.

Fig. 1: ESO/S. Ramstedt (Uppsala University, Sweden) & W. Vlemmings (Chalmers University of Technology, Sweden)
Fig. 1: ESO/S. Ramstedt (Uppsala University, Sweden) & W. Vlemmings (Chalmers University of Technology, Sweden)

New observations show how the winds from the two stars have created a fascinating, beautiful and complex nebula. The remarkable heart-shaped bubble at the center is created by Mira B’s energetic wind inside Mira A’s more relaxed outflow. The heart, which formed some time in the last 400 years or so, and the rest of the gas surrounding the pair show that they have long been building this strange and beautiful environment together.

By looking at stars like Mira A and Mira B scientists hope to discover how our galaxy’s double stars differ from single stars in how they give back what they have created to the Milky Way’s stellar ecosystem. Despite their distance from one another, Mira A and its companion have had a strong effect on one another and demonstrate how double stars can influence their environments and leave clues for scientists to decipher.

Other old and dying stars also have bizarre surroundings, as astronomers have seen using both ALMA and other telescopes. But it’s not always clear whether the stars are single, like the Sun, or double, like Mira. Mira A, its mysterious partner and their heart-shaped bubble are all part of this story.

More information

The new observations of Mira A and its partner are presented in this paper.

The Atacama Large Millimeter/submillimeter Array (ALMA), an international astronomy facility, is a partnership of the European Organisation for Astronomical Research in the Southern Hemisphere (ESO), the U.S. National Science Foundation (NSF) and the National Institutes of Natural Sciences (NINS) of Japan in cooperation with the Republic of Chile. ALMA is funded by ESO on behalf of its Member States, by NSF in cooperation with the National Research Council of Canada (NRC) and the National Science Council of Taiwan (NSC) and by NINS in cooperation with the Academia Sinica (AS) in Taiwan and the Korea Astronomy and Space Science Institute (KASI).

ALMA construction and operations are led by ESO on behalf of its Member States; by the National Radio Astronomy Observatory (NRAO), managed by Associated Universities, Inc. (AUI), on behalf of North America; and by the National Astronomical Observatory of Japan (NAOJ) on behalf of East Asia. The Joint ALMA Observatory (JAO) provides the unified leadership and management of the construction, commissioning and operation of ALMA.

Source: ALMA Observatory