Deep Learning is About to Go Mainstream

 Artificial Intelligence  

Microsoft's release of Azure Machine Learning (ML) shows that the technology has reached wide commercialization with widely available interface and usability. The technology is spreading to more and more areas, making big data analysis available to a large user base.

Deep learning is the key technology of a great number of new start-ups in Silicon Valley and elsewhere. The artificial intelligence technology is increasingly being embedded into other systems from marketing, to health applications and the list continues to expand.

Microsoft is firmly behind the development and expansion of deep learning, and has recently released Azure Machine Learning.  The system includes the algorithms for image recognition and other applications that have recently produced astonishing results.

According to Microsoft, Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments. Machine Learning Studio features a library of time-saving sample experiments, R and Python packages and best-in-class algorithms from Microsoft businesses like Xbox and Bing. Azure ML also supports R and Python custom code, which can be dropped directly into a workspace.

"I think the cloud has transformed [machine learning], the big data revolution has transformed it," Microsoft's vice president of machine learning, Joseph Sirosh says. "At the end of the day, I think the opportunity that is available now because of the vast amount of data that is being collected from everywhere . . . is what is making machine learning even more attractive. . . . As most of behavior, in many ways, comes online on the internet, the opportunity to use the data generated on interactions on websites and software to tailor customer experiences, to provide better experiences for customers, to also generate new revenue opportunities and save money — all of those become viable and attractive."

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Sirosh joined Microsoft in fall 2013 from Amazon.com Inc. where he was vice president for the Global Inventory Platform and chief technology officer of the core retail business. In this role he had responsibility for the science and software behind Amazon’s supply chain and order fulfillment systems, as well as the central Machine Learning group, which he built and led.

During his nine years at Amazon, he managed a variety of teams including forecasting, inventory, supply chain and fulfillment, fraud prevention systems, data warehouse, and a novel data-driven seller lending business. Before joining Amazon, he worked for Fair Isaac Corp. as vice president of research and development. He is passionate about machine learning and its applications and has been active in the field since 1990.

"[Machine learning] is a continuing evolution in that field, we just have now gotten to the level where we have identified great algorithmic tricks that allow you to take performance and accuracy to the next level."

"The cloud makes it easy to integrate data, it makes it easy to, in place, do machine learning on top of it, and then you can publish applications on the same cloud,” he said. “And all of this process happens in one place and much faster, and that changes the game quite a bit."

Sirosh began his career in neural networks and actually earned his Ph.D. studying them.  He is happy to see deep learning emerge as a legitimately useful technology for mainstream users.

"[Machine learning] is a continuing evolution in that field, we just have now gotten to the level where we have identified great algorithmic tricks that allow you to take performance and accuracy to the next level.”

"It's not just about the graphics and workflow, it's also about providing a lot of examples," says Sirosh about the libraries in the new software package.

Deep learning is also an area where Microsoft sees a big opportunity to bring its expertise in building easily consumable applications to bear. Azure ML (machine learning) already makes it relatively easy to train deep neural networks using the same types of methods as its researchers do, Sirosh noted, but users can expect even more in the months to come.

“We will also provide fully trained neural networks,” he said. “We have a tremendous amount of data in images and text data and so on inside of Bing. We will use our massive compute power to learn predictive models from this data and offer some of those pre-trained, canned neural networks in the future in the product so that people will find it very easy to use.”

Sirosh talks about the release of Azure Machine Learning about halfway through the embedded podcast below:



SOURCE  GigaOm

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