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
Posts Tagged ‘Machine Learning’
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
Learning at large-scale. That’s the work Declara is undertaking with its CognitiveGraph platform that leverages semantic search, social platforms and predictive analytics to build context-specific learning pathways for the individuals involved in mass learning efforts. Think, for example, of teachers in a country working to re-educate all its educators, or retail and manufacturing workers in parts of the world who need new skill sets because machines have taken on the work these people used to do.
Adults don’t have the luxury of just being focused on learning, so “we try to help them learn more effectively and quickly, using the CognitiveGraph as a way of knowing where to start from and how to get them to positive outcomes faster,” says co-founder and CEO Ramona Pierson. Its intelligent learning platform will determine what mentors and information exist within a closed private network or on the Web relative to supporting a user’s learning needs; what of all that will be the best fit for a particular user; and then match that learner to the best pathway to acquire the new skills. Among the technologies Declara is leveraging is Elasticsearch (which the Semantic Web Blog discussed most recently here) realtime search and analytics capabilities to turn data into insights.
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.”
Nick Stockton of Quartz reports, “Computers stole your job; now they know your pain. Using a combination of facial recognition software and machine learning algorithms, researchers have trained computers to be dramatically better than humans at reading pained facial expressions. And they’re working on new programs to help clue you into what your friend, coworker, or client is feeling. In a study released Friday (paywall) in the journal Current Biology, researchers asked 170 subjects whether the expressions of pain shown on faces in a series of videos were real or faked. They found that the humans’ collective empathetic ability was about the same as a coin flip—they read the expressions correctly only 50% of the time. Even after researchers trained the subjects to read the subtle, involuntary muscle triggers that experts use to tell when an emotion is being faked, they were only right 55% of the time.” 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
PALO ALTO, Calif., March 25, 2014 /PRNewswire-iReach/ — EngageClick (http://www.engageclick.com), the predictive and personalized multi-screen advertising platform that delivers superior ad engagement and performance, emerged out of stealth mode today. The EngageClick ad platform differentiates itself by applying data-driven technology that uses cognitive science, machine-learning technology and big data analytics to perform predictive segmentation, and subsequently delivers dynamic smart ads with automatic and incremental optimization across multiple screens. EngageClick maximizes advertiser ROI and increases media yield, helping ad agencies and brands increase consumer engagement and ad performance, at scale. Read more
BLOOMINGTON, Ind. — By understanding, managing and inferring patterns from data, machine learning has brought us self-driving vehicles, spam filters and smartphone personal assistants. Now an Indiana University Bloomington computer scientist has received $1.4 million to give machine learning more muscle by making it applicable to greater amounts of more diverse data.
Chung-chieh “Ken” Shan, an assistant professor in the School of Informatics and Computing, will receive the funding from the U.S. Defense Department’s Defense Advanced Research Projects Agency over 46 months. 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
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