Predictive software gets continually smarter

October 31, 2012 10:15 pm | Updated 10:18 pm IST

File - In this Nov. 18, 2010 file photo, a magnifying glass is used to illustrate an excerpt from the Top Internet Service Goggle Maps, recorded in Bremen, Germany. The U.S. Justice Department approved Google's acquisition Friday, Dec. 2, 2011, of online advertising service AdMeld after concluding the deal wouldn't diminish competition in one of the Internet's most lucrative marketing niches. (AP Photo/dapd, Joerg Sarbach, File)

File - In this Nov. 18, 2010 file photo, a magnifying glass is used to illustrate an excerpt from the Top Internet Service Goggle Maps, recorded in Bremen, Germany. The U.S. Justice Department approved Google's acquisition Friday, Dec. 2, 2011, of online advertising service AdMeld after concluding the deal wouldn't diminish competition in one of the Internet's most lucrative marketing niches. (AP Photo/dapd, Joerg Sarbach, File)

Eric Horvitz joined Microsoft Research 20 years ago with a medical degree, a Ph.D. in computer science and no plans to stay. “I thought I'd be here six months,” he said.

He remained at MSR, as Microsoft's advanced research arm is known, for the fast computers and the chance to work with a growing team of big brains interested in cutting-edge research. His goal was to build predictive software that could get continually smarter.

In a few months, Horvitz, 54, may get his long-awaited payoff: the advanced computing technologies he has spent decades working on are being incorporated into numerous Microsoft products.

Next year's version of the Excel spreadsheet program, part of the Office suite of software, will be able to comb very large amounts of data. For example, it could scan 12 million Twitter posts and create charts to show which Oscar nominee was getting the most buzz.

A new version of Outlook, the email program, is being tested that employs Horvitz's machine-learning specialty to review users’ email habits. It could be able to suggest whether a user wants to read each message that comes in.

Elsewhere, Microsoft's machine-learning software will crawl internal corporate computer systems much the way the company's Bing search engine crawls the Internet looking for websites and the links among them. The idea is to predict which software applications are most likely to fail when seemingly unrelated programs are tweaked.

If its new products work as advertised, Microsoft will find itself in a position it has not occupied for the last few years: relevant to where technology is going.

While researchers at MSR helped develop Bing to compete with Google, the unit was widely viewed as a pretty playground where Bill Gates had indulged his flights of fancy. Now, it is beginning to put Microsoft close to the centre of a number of new businesses, like algorithm stores and speech recognition services. “We have more data in many ways than Google,” said Qi Lu, who oversees search, online advertising and the MSN portal at Microsoft. MSR owes its increased prominence as much to the transformation of the computing industry as to its own hard work. The explosion of data from sensors, connected devices and powerful cloud computing centres has created the Big Data industry. Computers are needed to find patterns in the mountains of data produced each day.

In the long term, Microsoft hopes to combine even more machine learning with its cloud computing system, called Azure, to rent out data sets and algorithms so businesses can build their own prediction engines. The hope is that Microsoft may eventually sell services created by software, in addition to the software itself.

“Azure is a real threat to Amazon Web Services, Google and other cloud companies because of its installed base," said Anthony Goldbloom, the founder of Kaggle, a predictive analytics company. “They have data from places like Bing and Xbox, and in Excel they have the world's most widely used analysis software.”

Machine learning involves computers deriving meaning and making predictions from things like language, intentions and behavior. When search engines like Google or Bing offer “did you mean?” alternatives to a misspelled query, they are employing machine learning.

Horvitz, now a distinguished scientist at MSR, uses machine learning to analyze 25,000 variables and predict hospital patients' readmission risk. He has also used it to deduce the likelihood of traffic jams on a holiday when rain is expected. — New York Times News Service

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