Twitter has acquired Gnip, a social data provider that we have covered in the past. According to Chris Moody of Gnip, “Combining forces with Twitter allows us to go much faster and much deeper. We’ll be able to support a broader set of use cases across a diverse set of users including brands, universities, agencies, and developers big and small. Joining Twitter also provides us access to resources and infrastructure to scale to the next level and offer new products and solutions. This acquisition signals clear recognition that investments in social data are healthier than ever. Our customers can continue to build and innovate on one of the world’s largest and most trusted providers of social data and the foundation for innovation is now even stronger. We will continue to serve you with the best data products available and will be introducing new offerings with Twitter to better meet your needs and help you continue to deliver truly innovative solutions.” Read more
Rick Delgado of Tech Cocktail reports, “The one thing most businesses strive for is to make sure their customers are happy.In the past, the approach to this was fairly simple, engaging the customer through cheerful face-to-face interaction and seeing to all their needs personally. But as technology advances, more and more business dealings are taking place online, away from the face-to-face model that served so well for years. Many businesses are now turning to machine learning as the solution to improve customer interaction.” Read more
Bill Franks of the International Institute for Analytics recently opined, “In recent years, the use of the term Machine Learning has surged. What I struggle with is that many traditional data mining and statistical functions are being folded underneath the machine learning umbrella. There is no harm in this except that I don’t think that the general community understands that, in many cases, traditional algorithms are just getting a new label with a lot of hype and buzz appeal. Simply classifying algorithms in the machine learning category doesn’t mean that the algorithms have fundamentally changed in any way.” Read more
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
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
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.”
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
The Times of India recently wrote, “Who needs an army of lawyers when you have a computer? When Minneapolis attorney William Greene faced the task of combing through 1.3 million electronic documents in a recent case, he turned to a so-called smart computer programme. Three associates selected relevant documents from a smaller sample, ‘teaching’ their reasoning to the computer. The software’s algorithms then sorted the remaining material by importance. ‘We were able to get the information we needed after reviewing only 2.3% of the documents,’ said Greene, a Minneapolis-based partner at law firm Stinson Leonard Street LLP. Artificial intelligence has arrived in the American workplace, spawning tools that replicate human judgments that were too complicated and subtle to distill into instructions for a computer. Algorithms that ‘learn’ from past examples relieve engineers of the need to write out every command.” Read more
NEW YORK, March 19, 2014 /PRNewswire/ — The New York Genome Center (NYGC) and IBM today announced an initiative to accelerate a new era of genomic medicine with the use of IBM’s Watson cognitive system. IBM and NYGC will test a unique Watson prototype designed specifically for genomic research as a tool to help oncologists deliver more personalized care to cancer patients.
NYGC and its medical partner institutions plan to initially evaluate Watson’s ability to help oncologists develop more personalized care to patients with glioblastoma, an aggressive and malignant brain cancer that kills more than 13,000 people in the U.S. each year. Read more
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