Machine Learning Shaping the New World of Cognitive Computing

By Darryl K. Taft  |  Posted 2015-11-30 Print this article Print
Machine Learning

Machine Learning Studio publishes models as Web services that can easily be linked to custom apps or business intelligence (BI) tools based on Excel spreadsheets. The technology provides an interactive, visual work space to easily build, test, and iterate on a predictive analysis model.

The predictive analytics software solution can be embedded in applications or used along with the cloud-based Microsoft Power BI analysis solution.

Azure Machine Learning Studio is a component of Azure Machine Learning (Azure ML), which is a cloud service that helps users implement the machine learning process. It runs on Microsoft Azure and can work with very large amounts of data and be accessed from anywhere in the world.

“The idea of machine learning has been around for quite a while,” Chappell said. “Because we now have so much more data, machine learning has become useful in more areas. Yet unless the technology of machine learning gets more accessible, we won’t be able to use our big data to derive better solutions to problems, and thus build better applications," he said.

"A primary goal of Azure ML is to address this challenge by making machine learning easier to use," said Chappell. While data scientists are still required to make these systems work, the Azure ML cloud service can help less-specialized people play a bigger role in bringing machine learning into the mainstream, he said.

"Going forward, expect data-derived models to become more common components in new applications,” Chapel said.

Many organizations are using Azure ML to dip their toes into the machine learning waters. For instance, Northwestern University’s Kellogg School of Management uses Azure ML to introduce MBA students to predictive analytics.

Professor Florian Zettelmeyer, director of the Program on Data Analytics at Kellogg, said he uses Azure ML with his students because its drag-and-drop interface and library of sample experiments and algorithms offer a smoother transition to advanced analytics than requiring students to become technology specialists and learning to code.

In another example, Microsoft, based in Redmond, WA, is working with the nearby Tacoma Public School District to use Azure ML to provide predictive analytics on which students may be at risk of failing a course or dropping out. The Microsoft Data and Decision Sciences Group (DDSG) created a proof-of-concept (POC) data model centered on Azure Machine Learning.

Still Early Days

So while machine learning shows an enormous amount of promise for helping enterprises become smarter, it is still early days.

Gualtieri warns against thinking of machine learning as a singular approach to analyzing data. There are dozens of specialized classes of algorithms that focus on specific problem domains, he said. For example, some machine learning algorithms are designed to analyze images or video to identify objects or predict emotional state from facial expressions.

Others are used to make personalized product recommendations for customers. Search engines use machine learning algorithms to continuously improve search results. What makes machine learning algorithms unique is that they are designed to identify patterns or make predictions by analyzing historical data that is representative of the domain, he said.

Also, “Don't get tripped up on the ‘learning’ part of machine learning,” Gualtieri said. “Learning means that the algorithms analyze sets of data to look for patterns and/or correlations that result in insights. Those insights can become deeper and more accurate as new data sets are analyzed by the algorithms.”

For his part, IBM’s High notes  that 80 percent of the world’s data is in forms that traditional computing systems have not been able to interpret properly for their meaning, which only cognitive computing effectively discern.

That is why machine learning “is germane to every industry that is today subject to qualitative information—sometimes referred to as unstructured information—which includes stuff that humans write down to express their ideas, vocal expressions of human language and visual expressions of human language," he said.

"We need a system that is capable of tapping into all the data that today we essentially ignore or let pass us by because we don't have the time for it.”



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