Posts Tagged ‘Machine Learning’

IBM Taps Global Network of Innovation Centers to Fuel Linux on Power Systems for Big Data and Cloud Computing

ibmCHICAGO, Aug. 22, 2014 /PRNewswire/ — At the LinuxCon North America conference last week, IBM (NYSE: IBM) announced it is tapping into its global network of over 50 IBM Innovation Centers and IBM Client Centers to help IBM Business Partners, IT professionals, academics, and entrepreneurs develop and deliver new Big Data and cloud computing software applications for clients using Linux on IBM Power Systems servers. Read more

The Many Possibilities of Machine Learning and AI

6829374625_11533e23f5Lars Hard of Beta News recently wrote, “Artificial intelligence (AI) has become a bit of a buzzword among technology professionals (and even within the mainstream public) but truthfully, most people do not know how it works or how it is already being integrated within leading enterprise businesses. AI for businesses is today mostly made up of machine learning, wherein algorithms are applied in order to teach systems to learn from data to automate and optimize processes and predict outcomes and gain insights. This simplifies, scales and even introduces new important processes and solutions for complex business problems as machine learning applications learn and improve over time. From medical diagnostics systems, search and recommendation engines, robotics, risk management systems, to security systems, in the future nearly everything connected to the internet will use a form of a machine learning algorithm in order to bring value.” Read more

Blab Builds The Conversation Graph

blab2We have the Knowledge Graph, the Enterprise Graph, the Researcher Graph, the Supply Chain Graph – and now, the Conversation Graph, too

That’s how Blab characterizes the work it’s doing to add structure to the chaotic world of online conversation, normalizing and patterning the world’s discussions across 50,000 social network, news outlet, blog, video and other channels, regardless of language – to the tune of some hundred million posts per day and 1 million predictions per minute. Near realtime predictions, says CEO Randy Browning, of what a target audience will be interested in a 72-hour forward-looking window based on what they’re talking about now, so that customers can tailor their buying strategies for AdWords or search terms as well as create or deploy content that’s relevant to those interests.

“We predict what will be important to people so they can buy search terms or AdWords at a great price before the market or Google sees it,” he says. That’s the main reason customers turn to Blab today, with optimizing their own content taking second place. Crisis management is the third deployment rationale. “If a brand has multiple issues, we can tell them which will be significant or which will be a blip and then fade away, so they can get a predictive understanding of where to focus their resources to mitigate issues coming down the pike.

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Yahoo Labs Hopes to Change the Future of Content Consumption

yahooDerrick Harris of GigaOM reports, “When it comes to the future of web content… Yahoo might just have the inside track on innovation. I spoke recently with Ron Brachman, the head of Yahoo Labs, who’s now managing a team of 250 (and growing) researchers around the world. They’re experts in fields such as computational advertising, personalization and human-computer interaction, and they’re all focused on the company’s driving mission of putting the right content in front of the right people at the right time. However, Yahoo Labs’ biggest focus appears to be on machine learning, a discipline that can easily touch nearly every part of a data-driven company like Yahoo. Labs now has a dedicated machine learning group based in New York; some are working on what Brachman calls ‘hardcore science and some theory,’ while others are building a platform that will open up machine learning capabilities across Yahoo’s employee base.” Read more

Medallia Raises $50M to Decode Customer Sentiment

medGeorge Anders of Forbes reports, “Medallia is $50 million richer, thanks to a new infusion from one of Silicon Valley’s top venture firms: Sequoia Capital. The new money will help the Palo Alto, Calif., customer-insights company expand geographically and tackle one of software’s trickiest challenges: decoding the noisy rumbles of public sentiment. Medallia helps big companies such as  Nordstrom, Best Western, Lego and Telstra figure out what customers really think about various products and services. A generation ago, direct feedback was scarce. Now, if anything, there’s too much of it. Add up everything being expressed on Twitter, Yelp, TripAdvisor, e-mail surveys and old-fashioned comment cards — and company executives can feel as if they’re drowning in too much information that keeps arriving hourly in haphazard form.” Read more

Yahoo Acquires Local Search App Zofari

zofariMenchie Mendoza of TechTimes recently wrote, “Affectionately described as a ‘Pandora for places,’ Zofari’s acquisition seemed to have attracted less attention when the deal was announced last week. Zofari uses natural language processing, machine learning, and third party data to collect information that matches up the user with places which the user may find interesting. The financial terms of the acquisition have not been revealed. On Zofari’s official site, the company confirmed that four of its employees are joining Yahoo. They are identified as Oliver Su, Shahzad Aziz, Jason Kobilka and Nate Weinstein. ‘After meeting some of the amazing folks on the Yahoo Search team and hearing about their vision, the decision for our team to join Yahoo was an easy one,’ said in the announcement. ‘We can’t talk about what we’re working on yet, but needless to say we are very, very excited’.” Read more

EverString Raises $12M to Bring Machine Learning to Clients

logo-esJonathan Vanian of GigaOM reports, “EverString, a big data startup that helps companies identify prospective sales leads and new clients though predictive analytics, has raised $12 million in a series A funding round. Lightspeed Venture Partners led the round, which also included existing investors Sequoia Capital and IDG Ventures. While there are a host of marketing analytics services in the market like Silverpop and Eloqua that businesses use to aggregate numerous sales leads and find potential customers, EverString’s technology goes beyond whatever data is hosted internally within a company and branches out to the open web, explained EverString’s co-founder and CEO, Vincent Yang.” Read more

Startup Adatao Raises $13M to Bring Search to Big Data

adataoDeborah Gage of The Wall Street Journal reports, “Making big data stores as easy to search as Internet data has been a holy grail for the software industry, and it’s become a more pressing problem since the growth of the big data software Hadoop, which holds enormous amounts of data. Adatao Inc., a startup based in Sunnyvale, Calif., has raised nearly $13 million in Series A funding led by Andreessen Horowitz to take on the challenge. Founded in 2012 by veterans of Google Inc., Yahoo Inc. and the Army Research Lab, the company combines machine learning, natural language processing and in-memory (i.e. fast) computing to create a system in which users can write queries in ordinary English or one of several computer languages-—Smart Query, SQL, Scala, Java, Python or R–and get results in less time than it takes to speak their questions.” Read more

5 Ways to Add Machine Learning Java

javaSerdar Yegulalp of InfoWorld recently wrote, “After spending decades in the shadows as a specialty discipline, machine learning is suddenly front and center as a business tool. The hard part, though, is making it useful, especially to the developers and budding data scientists who are being tasked with the job. To that end, we rounded up some of the most common and useful open source machine learning tools we’ve spotted in the wild.” Read more

Big Data Holds Great Power, But Only if You Know How to Mine It

27498728_b84f8817aaChloe Green of Information Age recently wrote, “Handling immense data sets requires a combination of scientific and technological skills to determine how data is stored, searched and accessed. In science, the importance of data scientists in ensuring that data is handled correctly from the outset is not underestimated; other industries can learn from the scientific approach. Text-mining tools and the use of relevant taxonomies are essential. If we think about big data as a huge number of data points in some multi-dimensional space, the problem is one of analysis, i.e. frequently finding very similar or very dissimilar points which cannot be compared. In life sciences, taxonomies assign data points a class, thus comparison of two points is as easy as looking up other data points in the same class.” Read more

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