According to a recent article out of RPI, “Universities must make new and innovative connections to harness the full power and potential of this data-driven era, Rensselaer Polytechnic Institute President Shirley Ann Jackson said [Tuesday] in a keynote address at the Internet2 Global Summit in Denver, Colorado. Deriving ‘insights from the massive amounts of web-based data that humanity is producing about itself, during the ordinary course of every day…. may be the greatest intellectual challenge and opportunity we all face in academic life,’ President Jackson told the gathering of academic, business, and government leaders in the arena of information technology. ‘Today, we analyze less than 1 percent of the data we capture, even though the answers to many of the great global challenges lie within this overabundant natural resource,’ Jackson said. The challenge, she notes, is finding new ways to address the volume, velocity, variety, and veracity of the data.” Read more
Jeremy Bentley of KMWorld recently wrote, “The age of the Internet has made us accustomed to having all the information we could want readily available at our fingertips – quite literally so, thanks to laptops, tablets, smartphones and other devices… Unfortunately, we rarely experience the same level of data accessibility in our workplaces, where internal information assets can be massive and hugely complex—and not at all easy to access search with the pinpoint precision that is usually required to find a very specific document or piece of content. Addressing this challenge should be high on the priorities list of any organization aiming to extract value efficiently from unstructured content. But it is proving to be no easy task.” Read more
Haim Koshchitzky of Sys-Con Media recently wrote, “Enterprise applications can ‘live’ in many places and their logs might be scattered and unstandardized. First generation log analysis tools made some of the log data searchable, but the onus was on the developer to know what to look for. That process could take many hours, potentially leading to unacceptable downtime for critical applications. Proprietary log formats also confuse and confound conventional keyword search. That’s why semantic search can be so helpful. It uses machine intelligence to understand the context of words, so it becomes possible for a Google user to type ‘cheap flights to Tel Aviv on February 10th’ rather than just ‘cheap flights’ and receive a listing of actual flights rather than links to airline discounters. Bing Facebook, Google and some vertical search engines include semantic technology to better understand natural language. It saves time and creates a better experience.” Read more
Oneindia News recently shared a new case study of how Twitris was used to measure sentiment about the current elections in India. The article begins, “Based on 900,000 tweets collected from 15 states about three major political parties (BJP, Congress and AAP), our analysis shows how people talked about and reacted to each political party. Using Twitris, their Collective Social Intelligence platform, the researchers at the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) at Wright State University processed each tweet to compute sentiment about the mentioned political party. One parameter to measure popularity is to check which political party gets most positive sentiment or least negative sentiment. Just counting negative (or positive) sentiments on a politician provides, as in this Deccan Herald story, provides little useful information about the state of electorate.” Read more
Martin Hack, CEO and co-founder of machine learning company Skytree, has a prediction to make: “In the next three to five years we will see a machine learning system in every Fortune 500 company.” In fact, he says, it’s already happening, and not just among the high-tech companies in that ranking but also among the “bread and butter” enterprises.
“They know they need advanced analytics to get ahead in the game or stay competitive,” Hack says. For that, he says, they need machine learning algorithms for analyzing their Big Data sets, and they need to be able to deploy them quickly and easily — even if those who will be doing the deployments are coming from at best a background of basic analytics and business intelligence.
“There just aren’t enough data scientists to go around,” he says. It’s very tough to fill those roles in most companies, he says, “so like it or not, we have to make it much, much easier for people to digest and use this.”
Ron Callari of Inventor Spot recently wrote, “It’s hard to say, looking twenty to thirty years into the future, just how different the digital landscape will look. Semantic Technology, Augmented Reality, Virtual Reality and Web 3.0 are presently only toddling along in their infant stage. What they will look like in the next few decades is only guesswork on our part. However if we were pressed to gamble on the outcome, a smart man’s wager might be that the last two digital super powers left standing will be Google and Facebook [with the possible exception of China]. A CNN Money report describes this evolution as analogous to the ‘Cold War,’ to conjure up imagery of what transpired between America and the Soviet Union, post World War II.” Read more
Dominic Basulto of The Washington Post recently wrote, “For more than 50 years, we’ve been hearing about the promise of artificial intelligence and intelligent machines, but most of the big success stories to date – the IBM Watsons of the world – have been the result of massive efforts by universities and corporate R&D labs rather than by emerging start-ups. That could change soon, as artificial intelligence shows signs of becoming the next big trend for tech start-ups in Silicon Valley. First of all, there’s the anecdotal evidence about deals getting done for promising new AI startups. One of the most talked about VC deals in March, for example, was a $40 million round for Vicarious FPC, an artificial intelligence company that had so much hype around it that the biggest names of the tech world – including Mark Zuckerberg and Elon Musk (and Ashton Kutcher) – lined up to participate.” Read more
Martin Hack of Wired recently wrote, “When Amazon recommends a book you would like, Google predicts that you should leave now to get to your meeting on time, and Pandora magically creates your ideal playlist, these are examples of machine learning over a Big Data stream. With Big Data projected to drive enterprise IT spending to $242 Billion according to Gartner, Big Data is here to stay, and as a result, more businesses of every size are getting into the game. To many enterprise organizations Big Data represents a strategic asset — it reflects the aggregate experience of the organization. Each customer, partner, or supplier response or non-response, transaction, defection, credit default, and complaint provides the enterprise the experience from which to learn.” Read more
Last week, David Amerland of Forbes wrote, “At the heart of the semantic Web is connectivity. The key is the ability of one set of data to be connected to a different set of data—with fresh meaning arising from the connection. If that sounds like a souped-up version of the word-association game, you might ask, ‘So what?’ The value lies in the clarity of the picture that emerges… Consider that the BYOD trend that’s underway requires the development of trust inside the organization. Trust is needed not just as part of the natural evolution of the internal structure of the enterprise, but also for it to respond better and faster to marketplace events that can wrong-foot it. In other words, no business can expect to survive if it remains the same.” Read more
NEXT PAGE >>