Posts Tagged ‘Machine Learning’

Newsle Joins LinkedIn

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Jonah Varon and Axel Hansen’s semantic news app Newsle has become part of LinkedIn, and so have Varon and Hansen (plus their engineering team.) According to Newsle, “We founded Newsle with a simple goal: to deliver important news about the people who matter to you. Three years and 2 million users later, we’re happy to say that we’re well on the way toward realizing that goal. LinkedIn is equally passionate about offering insights that can help professionals better do their jobs and will help us accelerate our efforts by making Newsle available to its members. We’re delighted that our team of engineers — Liz, Farid, Shane, and Nick — will be making the move with us to LinkedIn. We’ll keep Newsle online as a standalone service as we combine all the functionality  with LinkedIn’s core services.” Read more

Smarter Enterprise Data with Machine Learning and Cognitive Computing

Watson

Jorge Garcia of Wired recently wrote, “IBM’s recent announcements of three new services based in Watson technology make it clear that there is pressure in the enterprise software space to incorporate new technologies, both in hardware and software, in order to keep pace with modern business. It seems we are approaching another turning point in technology where many concepts that were previously limited to academic research or very narrow industry niches are now being considered for mainstream enterprise software applications. Machine learning, along with many other disciplines within the field of artificial intelligence and cognitive systems, is gaining popularity, and it may in the not so distant future have a colossal impact on the software industry. This first part of my series on machine learning explores some basic concepts of the discipline and its potential for transforming the business intelligence and analytics space.” Read more

GraphLab Create Aims To Be The Complete Package For Data Scientists

glabData scientists can add another tool to their toolset today: GraphLab has launched GraphLab Create 1.0, which bundles up everything starting from tools for data cleaning and engineering through to state-of-the-art machine learning and predictive analytics capabilities.

Think of it, company execs say, as the single platform that data scientists or engineers can leverage to unleash their creativity in building new data products, enabling them to write code at scale on their own laptops. The driving concept behind the solution, they say, is to make large-scale machine learning and predictive analytics easy enough that companies won’t have to hire huge teams of data scientists and engineers and build the big hardware infrastructures that lie behind many of today’s Big Data-intensive products. And, the data scientists and engineers that do use it won’t need to be experts at machine-learning algorithms – just experienced enough to write Python code.

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Microsoft Ups Its Stake in Machine Learning

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Pedro Hernandez of eWeek reports, “‘Mobile-first, cloud-first’ may be Microsoft’s new mantra, but another term has been has been increasingly creeping into the company’s lexicon of late. As one of the components of Microsoft’s growing slate of smart services, machine learning is also guiding part of the company’s product strategy, according to Microsoft Research Distinguished Scientist John Platt. First, it helps to know how his company classifies machine learning (ML). ‘In general, ML converts data sets into pieces of software, known as ‘models,’ that can represent the data set and generalize to make predictions on new data,’ explained Platt in Microsoft’s new Machine Learning Blog.” Read more

Versium Leverages Microsoft Azure Machine Learning For New Predictive GivingScore Solution To Improve Fundraising

versiumpixVersium, which earlier this year launched its Predictive FraudScore solution (covered here) today releases its Predictive GivingScore solution, designed to help charitable institutions and political organizations better predict who is likely to donate, be a repeat donator, or make the more significant contribution. PredictiveGiving Score is the latest of the company’s predictive Score products, which also include churn, social influencer and shopper scoring – and it’s by no means the last.

It was built with Microsoft Azure Machine Learning, a managed cloud service for building predictive analytics solutions publicly unveiled just a short time ago. CEO Chris Matty says that platform is an aid to Versium in rapidly building its new score solutions. (Just shy of ten Versium scoring products are currently in use or in development.) Azure ML, Matty notes, contains dozens of machine learning algorithms and mathematical computation models it leverages to easily and effectively experiment, create and tune models to get the highest accuracy in predictive scoring solutions.

“Once we have a score built it just takes little tuning. But when we are building a new score we need to look at some different models and see what works better,” he says. “We want to move quickly by evaluating the different models, and we can visualize very easily the process of building the predictive model.”

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Spiderbook’s SpiderGraph: Linking Datasets To Help You Sell Better

spiderpix1Startup Spiderbook, which is building a linked dataset of companies and their partners, customers, suppliers, and people involved in those deals, has recently closed its seed round for $1 million. The next-generation sales intelligence company was co-founded by CEO Alan Fletcher, who was a vp of product engineering, IT and operations at Oracle, and Aman Naimat, who has been working in the realm of CRM software since he was 19 years old and also has a background in natural language processing. Along with other core members of the team, the company puts natural language processing and machine learning technology to work to help sales people better connect the dots that explain business relationships, extracting information from unstructured text to sell more effectively.

State-of-the-art CRM, says Naimat, by itself doesn’t help salespeople sell. Since the days of Salesforce, which he worked on at IBM and Oracle, it has remained the same thing, he says, “just evolving with better technology. But basically it is an internal-facing administration tool to give management visibility, not to help a salesperson sell or create business relationships.”

Built from billions of data elements extracted from everything from SEC filings to press releases to blogs to Facebook posts, Spiderbook’s SpiderGraph is taking on that challenge, starting with the goal of helping salespeople understand who is the right contact to talk to, how he or she can meet that person (through shared contacts, for instance), and who competitors are, including those providing technology or other products already in use at the company. “We have created a graph of customers, competition, and suppliers for every company that is all interconnected,” he says.

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The Varied Definitions of Machine Learning

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James Kobielus of Info World recently shared his thoughts on the best definition for machine learning. He writes, “Increasingly, the term ‘machine learning’ is… beginning to acquire a catch-all status. Or, at the very least, machine learning has become a convenient handle that today’s data scientists use to refer to the wide range of leading-edge techniques for automating knowledge and pattern discovery from fresh data, much of it unstructured. People’s working definitions of machine learning seem to be creeping into broader, vaguer territory. That’s my impression from reading the recent article “Learning and Teaching Machine Learning: A Personal Journey.” In it, author Joseph R. Barr of San Diego State University and True Bearing Analytics discusses both the history of machine learning and his own education in the topic. He states that ‘it’s safe to regard machine learning, data mining, predictive analysis, and advanced analytics as more or less synonymous’.” Read more

Big Data Startup Roletroll Disrupts Job Search

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New York, NY, June 20, 2014 –(PR.com)– Former Wall St trader Adam Grealish launches Roletroll.com, a job recommendation engine for finance and tech jobs. The site uses unstructured data and statistics, collectively known as big data, to match users with jobs based on their unique skills and experiences.

 

Roletroll is a Brooklyn, NY-based start-up serving New York, Chicago and San Francisco. Job seekers upload their resumes, and computer programs do the job-search grunt work for them, aggregating jobs from multiple sources and identifying jobs that are a particularly good fit. Already Roletroll has scored over 5 million individual job matches. Read more

New York Times Turns to Machine Learning for Better Understanding of Readers

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Matthew Ingram of GigaOM recently wrote, “You might not think an applied mathematician who does research in biology and has a PhD in theoretical physics would have much to offer a 163-year-old newspaper publisher, but Chris Wiggins, head of the data science team at the New York Times, told attendees at the Structure conference in San Francisco that machine learning can do much the same thing for media companies as it does for research biologists: namely, make sense of a whole pile of data.” Read more

Extracting Value from Big Data Requires Machine Learning

Involuntary Commitment

James Kobielus of InfoWorld recently wrote, “Machine-generated log data is the dark matter of the big data cosmos. It is generated at every layer, node, and component within distributed information technology ecosystems, including smartphones and Internet-of-things endpoints… Clearly, automation is key to finding insights within log data, especially as it all scales into big data territory. Automation can ensure that data collection, analytical processing, and rule- and event-driven responses to what the data reveals are executed as rapidly as the data flows. Key enablers for scalable log-analysis automation include machine-data integration middleware, business rules management systems, semantic analysis, stream computing platforms, and machine-learning algorithms.? Read more

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