Stay on Top of Emerging Technology Trends
Get updates impacting your industry from our GigaOm Research Community
A few years ago, there was a shift in the world of machine learning.
Companies, such as Skytree and Context Relevant, began popping up, promising to make it easier for companies outside of big banks and web giants to run machine learning algorithms and to do it at a scale congruent with the big data promise they were being pitched. Soon, there were many startups promising bigger, faster, easier machine learning. Machine learning became the new black as it became baked into untold software packages and services — machine learning for marketing, machine learning for security, machine learning for operations, and on and on and on.
Eventually, deep learning emerged from the shadows and became a newer, shinier version of machine learning. It, too, was very difficult and required serious expertise to do. Until it didn’t. Now, deep learning is the focus of numerous startups, all promising to make it easy for companies and developers of all stripes to deploy.
But it’s not just startups leading the charge in this democratization of data science — large IT companies are also getting in on the act. In fact, Microsoft now has a corporate vice president of machine learning. His name is Joseph Sirosh, and we spoke with him on this week’s Structure Show podcast. Here are some highlights from that interview, but it’s worth listening to the whole thing for his take on Microsoft’s latest news (including support for R and Python in its Azure ML cloud service) and competition in the cloud computing space.
You can also catch Sirosh — and lots of other machine learning and big data experts and executives — at our Structure Data conference next month in New York. We’ll be highlighting the newest techniques in taking advantage of data, and talking to the people building businesses around them and applying them to solve real-world problems.
Why the rise of machine learning and why now
“I think the cloud has transformed [machine learning], the big data revolution has transformed it,” Sirosh said. “But 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.”
Asked why whether all of this is possible without the cloud, Sirosh thinks it is, but — like most things — it would be a lot more difficult.
“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.”
Deep learning made easy and easier
Sirosh said he began his career in neural networks and actually earned his Ph.D. studying them, so he’s happy to see deep learning emerge as a legitimately useful technology for mainstream users.
“My take on deep learning is actually this,” he explained. “It 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.”
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 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.”
How easy can all of this really be?
As long as there are applications that can hide its complexity, Sirosh has a vision for machine learning that’s much broader than even the world of enterprise IT sales.
“Well, we are actually going after a broad audience with something like machine learning,” he said. “We want to make it as simple as possible, even for students in a high school or in college. In my way of thinking about it, if you’re doing statistics in high school, you should be able to use [a] machine learning tool, run R code and statistical analysis on it. And you can teach machine learning and statistical analysis using this tool if you so choose to.”
Is Microsoft evolving from an operating system company to a data company?
Not entirely, but Sirosh did suggest that Microsoft sees a shift happening in the IT world and is moving fast to ride the wave.
“I think you should even first ask, ‘How big is the world of data to computing itself?'” he said. “I would say that in the future, a huge part of the value being generated in the field of computing . . . is going to come from data, as opposed to storage and operating systems and basic infrastructure. It’s the data that is most valuable. And if that is where in the computing industry most of the value is going to be generated, well that is one place where Microsoft will generate a lot of its value, as well.”