Posts Tagged ‘algorithm’

New Activity Recognition Algorithm Can Figure Out What’s Happening in a Video


Larry Hardesty of MIT News Office reports, “With the commodification of digital cameras, digital video has become so easy to produce that human beings can have trouble keeping up with it. Among the tools that computer scientists are developing to make the profusion of video more useful are algorithms for activity recognition — or determining what the people on camera are doing when. At the Conference on Computer Vision and Pattern Recognition in June, Hamed Pirsiavash, a postdoc at MIT, and his former thesis advisor, Deva Ramanan of the University of California at Irvine, will present a new activity-recognition algorithm that has several advantages over its predecessors.” Read more

Spurring Group Communication with Machine Learning at


Taylor Soper of Geek Wire reports, “For the three entrepreneurs building, 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, 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

DataRPM Secures $5.1 Million in Series A to Advance Cognitive BI Platform


FAIRFAX, Va.–(BUSINESS WIRE)–DataRPM, the industry pioneer in cognitive business intelligence, today announced that it has closed a $5.1 million Series A funding round. Led by InterWest Partners and joined by CIT GAP Funds, the round will be used to accelerate DataRPM’s global go-to-market strategy. DataRPM changes the way individuals work with data, making analytics more accessible and easier to use by solving the two main barriers to the adoption of data analysis – time consuming data modeling and usability. The DataRPM business intelligence (BI) platform removes those barriers, automating the data modeling process and employing a natural language question-and-answer interface to simplify data analysis and visualization. Read more

Echo Nest Chooses Your Playlist, and Knows Who You’ll Vote For

echo nest

Tom Vanderbilt of Wired recently wrote, “The Echo Nest helps music services from Spotify to Rdio and Rhapsody suggest tunes you’ll like. But your playlists also teach its algorithms what movies you’ll watch — and even how you’ll vote… The Echo Nest claims it reaches around 100 million listeners per month, by powering music discovery services such as Spotify, Rdio, Rhapsody and VEVO, and delivering musical connections where none may have existed before… Staring at the sprawling projection up on the wall, which resembles Mark Lombardi’s unsettlingly internecine drawings of political conspiracies, one finds Polish reggae wedged roughly between Romanian pop and K-hop (or Korean hip-hop), closer in musical space to Chicago soul than it is to Finnish hip-hop.” Read more

Making a Better Algorithm: Machine Learning & Biology


Michael Goldberg of Data Informed reports, “When Lars Hård, an artificial intelligence veteran, discusses developing a recommendation engine for a consumer product like perfume, he talks about harvesting many kinds of data. Product data. Consumer search engine data. Design data having to do with colors that men or women prefer. Pricing data. To Hård, founder and chief technology officer at Expertmaker, data in all its structured and unstructured forms represents signals that machine learning systems pick up to refine results for future interactions. The data, and those interactions, also figure into Hård’s work of applying principles of evolutionary biology to both business use cases, like shopping assistants, and medical research.” Read more

Researchers Find New Machine Learning Method for Effectively Flagging Post-Stroke Dangers


PHILADELPHIA, October 4, 2013 — A team of experts in neurocritical care, engineering, and informatics, with the Perelman School of Medicine at the University of Pennsylvania, have devised a new way to detect which stroke patients may be at risk of a serious adverse event following a ruptured brain aneurysm. This new, data-driven machine learning model, involves an algorithm for computers to combine results from various uninvasive tests to predict a secondary event. Preliminary results were released at the Neurocritical Care Society Annual Meeting in Philadelphia. Read more

Google Changes Search Algorithm to Handle More Complex Queries


Claire Cain Miller of The New York Times reports, “Google on Thursday announced one of the biggest changes to its search engine, a rewriting of its algorithm to handle more complex queries that affects 90 percent of all searches. The change, which represents a new approach to search for Google, required the biggest changes to the company’s search algorithm since 2000. Now, Google, the world’s most popular search engine, will focus more on trying to understand the meanings of and relationships among things, as opposed to its original strategy of matching keywords.” Read more

TipSense Apps Make Your Decisions Easier

Josh Constine of TechCrunch recently shared some of the cooler features of TipSense, a great company from last year’s SemTechBiz Start-Up Competition. Constine writes, “No one wants to read thousands of reviews. You just want answers. Luckily there’s TipSense, a new startup whose algorithm sorts big messy data sets. TipSense’s site DishTip tells you what to order at restaurants, for example, while its AppCrawlr deduces an app’s best and worst features and lays them out with competitors on a comparison chart. TipSense is so smart I bet it gets acquired… or at least fields plenty of buyout offers. That’s because while people won’t shut up about big data, few companies have viscerally proven to consumers why it’s important. David Schorr built and bootstrapped TipSense over the last four years to change that. I met him at SXSW, was very impressed, and he agreed to let me write the first official interview with him about his stealthy startup.” Read more

Where Was that Picture Taken? Carnegie Mellon Can Tell You

John Biggs of TechCrunch recently discussed an intriguing program at Carnegie Mellon that uses a complex algorithm and Google Street View to identify cities based on their unique traits. The project description states, “Given a large repository of geotagged imagery, we seek to automatically find visual elements, e.g. windows, balconies, and street signs, that are most distinctive for a certain geo-spatial area, for example the city of Paris. This is a tremendously difficult task as the visual features distinguishing architectural elements of different places can be very subtle.” Read more

Tracking People in the News with Newsle, a web app that tracks people in the news, has released a new version featuring instant news alerts about users’ friends, colleagues, favorite public figures, or themselves. The startup also announced $600,000 in seed funding from Lerer Media Ventures, SV Angel, and an independent investor. According to the company website, “Newsle’s private beta launched in January 2011, and was covered by TechCrunch. The current version is a major evolution of the original concept. Newsle now combs the web continuously, analyzing over 1 million articles each day – every major news article and blog post published online, as well as most minor ones. Newsle’s core technology is its disambiguation algorithm, which determines whether an article mentioning “John Smith” is about the right person.” Read more