Christopher Tozzi of The VAR Guy reports, “PredictionIO, the open source machine learning platform, has received a big boost with the announcement of $2.5 million in seed funding, which it plans to use to make its automated data interpretation and prediction platform widely available to open source developers. PredictionIO’s goal is to make it easy for developers and companies of all sizes to integrate machine learning —i.e., software that can interpret data intelligently to make automated decisions and predictions—into their products. ‘PredictionIO aims to be the Machine Learning server behind every application,’ according to the company. ‘Building Machine Learning in software will be as common as search soon with PredictionIO’.” Read more
Posts Tagged ‘Machine Learning’
Daniel Day of News at Princeton reports, “Princeton University has established the Center for Statistics and Machine Learning. John Storey, a professor of molecular biology and the Lewis-Sigler Institute for Integrative Genomics, has been named the center’s director. The center will anchor the teaching of and research in statistics and machine learning on campus, Storey said, offering an undergraduate certificate as well as graduate training in the field.” Read more
Peter Rothman of h+ Magazine writes, “I recently got together with Ron Kaplan who is a well known artificial intelligence researcher in the area of natural language processing. Ron is a Distinguished Scientist at Nuance Communications. The conversation is about 1 hour long and the main theme was the recent comments about dangers from artificial intelligence made by Professor Stephen Hawking and also Elon Musk, Eugene Goostman the chatbot that supposedly passed the Turing Test. Beyond this, the conversation ranges near and far covering and whether it is ridiculous to suggest that Siri is a conscious being, reflective computing, NL interfaces and access to knowledge, communicating with wives, the effects of my diet, and the future of human languages when universal translation becomes widely available.” Read more
Technorati Media is looking for a Data Scientist – Machine Learning in San Francisco, CA. According to the post, “We currently have an opportunity for a talented Data Scientist – Machine Learning to help take our business to the next level. You‘ll play a key role in the definition, design, and development of the Technorati Advertising and Data platform, which currently services hundreds of millions of requests per day. Design, implement and optimize algorithms for performance and revenue in a in highly distributed environment- a variety of advanced massively scalable technologies such as multi-threaded distributed systems, big data pipelines, machine learning, predictive algorithms and more. Work with large (terabytes of data, billions of daily transactions) structured and unstructured data sets.” Read more
Senzari Unveils MusicGraph.ai at the GraphLab Conference 2014, Showcasing the Music Industry’s First Graph Analytics and Intelligence Engine
SAN FRANCISCO–(BUSINESS WIRE)–Today Senzari® introduced MusicGraph.ai, the first web-based graph analytics and intelligence engine for the music industry at the GraphLab Conference 2014, the annual gathering of leading data scientists and machine learning experts. MusicGraph.ai will serve as the primary dashboard for MusicGraph®, where API clients will be able to view detailed reports on their API usage and manage their account. More importantly, through this dashboard, they will also be able to access a comprehensive library of algorithms to extract even more value from the world’s most extensive repository of music data.
“We believe that in MusicGraph’s first iteration we democratized music data access, and now, with MusicGraph.ai, we are democratizing music knowledge. Most impressively, our team was able to make this leap possible in just months, not years.” Read more
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
Data 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.
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
Versium, 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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