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Saturday, July 16, 2016

The next wave of disruption: Graph-based machine learning | IDG Connect

Kathryn Cave, Editor at IDG Connect notes, "We look at the pros and cons of machine learning and graph technology and how the two are now working together."

Photo: IDG Connect

Machine learning (ML) is getting a lot of attention at the moment. This is partly because a slew of new companies are emerging which are using it in innovative ways. And partly because it can get easily subsumed into the fuss and furore about AI and the rise of evil robot intelligence. Graph technology, on the other hand, is something which takes more of a back seat and yet, in a lot of ways, also sits at the forefront of the big data and analytics movement. 

“We firmly believe is that it's at the intersection of machine learning and graph technology where the next evolution lies and where new disruptive companies are emerging,” says Ash Damle, Founder and CEO at Lumiata which helps healthcare organisations makes predictions.

“It's only recently that companies can use graph at true scale and, now, by integrating with ML, we're moving much more into a core understanding of artificial intelligence, deep neural networks and image recognition.”
 
So, in the simplest terms what are these two technologies?
 
At the most basic level machine learning takes large quantities of data to make predictions about future events. While graph technology is more concerned with the relationship between different data points.

Claus Jepsen, Chief Architect, R&D at Unit4 which provides enterprise applications, summarises:

“Machine Learning is really the umbrella and graph technology is a way of representing data when using machine learning.”

While Peter Duffy, CTO of capacity planning as a service provider, Sumerian adds, this means: “There is huge potential for businesses to take advantage of both.”

David Thompson, Sr. Director of Product Management at LightCyber further clarifies: “Graph technology can be considered a type or technique of machine learning, or, at a minimum, aspects of graph technology have strong application to machine learning.”
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Source: IDG Connect


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