Taylor Soper of Geek Wire reports, “For the three entrepreneurs building pol.is, the problem was simple: Big groups of people trying to communicate effectively about a certain topic online was largely inefficient. That’s why they started pol.is, a new Seattle company that has developed a way to combine polling data from hundreds of people with machine learning and interactive data visualization. The end result is a simple, clean way for anyone from college professors to market researchers to efficiently collect and package large amounts of data while enabling users to spur conversation based on all the input. ‘Getting large groups of people to communicate effectively is really painful,’ said CEO Colin Megill. ‘We are solving that’.” Read more
Posts Tagged ‘Machine Learning’
Thinknum is a startup with the mission: disrupting financial analysis.
In his work as a quantitative strategist at Goldman Sachs, Thinknum co-founder Gregory Ugwi saw firsthand the trials and tribulations financial analysts went through to digest companies’ financial reports and then build their own research reports about their expectations for future performance based on past numbers. The U.S. SEC’s mandate that companies disclose their financial data using XBRL (eXtensible Business Reporting Language) was supposed to help them, as well as investors of all stripes and sizes that want to better understand what’s going on at the companies they’re interested in.
“The SEC has mandated that all companies have to release their numbers in a machine-readable format, and that’s XBRL (eXtensible Business Reporting Language),” says Ugwi. The positive side of that is that anyone can now get the stats on companies from Google to Wal-Mart, but the downside is that by and large, they can’t do it in a user-friendly way.
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
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
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