You are here:  Home  >  Uncategorized  >  Current Article

Take Cognitive Computing To The Masses

By   /  May 27, 2014  /  No Comments

ianby Jennifer Zaino

Cognitive computing vendor Saffron Technology is thinking about how to bring cognitive computing to the masses. Chief product officer Ian Hersey mentioned in this Dataversity article – which describes Saffron’s take on cognitive computing – the desire to “democratize” the technology. How?

“One thing we see at Saffron is getting this technology out through various partnerships,” Hersey says. “But we need to make the technology approachable, and in the early market the key is to build some apps around it that will make this technology very easy to use and solve particular business problems at hand in a way they weren’t solved before.”

Its Sierra API that lets developers interface with its Saffron Associative MemoryBase comes into play in that last mile to the user. “You’ve got to have systems to plug with other systems – that’s where the API comes in because we can never build it all,” he says. The vendor itself has focused primarily on use cases in the manufacturing and medical areas, as well as energy and defense, for its Natural Intelligence platform.

Another way to facilitate first steps into cognitive computing is to provide the option to leverage the cloud. For some with very sensitive data, that might not be the right approach, and Saffron will continue supporting its technology’s use on customer sites. “But others are less sensitive and want to get up and running quickly, so running that for them in a private cloud or on Amazon is fine,” says Hersey. “I think of the cloud as simply a way to provide a service to software platform and its associative apps very easily to people.”

Hersey also says he is looking forward to driving Saffron forward in “taking context further and informing the analysis of text in general.” Consider the potential impact of leveraging this for something like recommendation engines. Rather than just using approaches like collaborative filtering, it could be possible to take into account a user’s entire experience across social media and search services and mobile devices that normally would be invisible to a recommendation engine.

“If I take all that context into account about my interests and other things, my recommendations of what to watch next could employ much more context of what I am actually interested in and across my devices,” he says. “The future of really smart systems is incorporating that very large context to assist people in whatever they want to do at that moment,” wherever they are.

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

You might also like...

Smart Data Webinar: Advances in Natural Language Processing II – NL Generation

Read More →