Business

Artificial Intelligence Company Inbenta Receives $2M in Series A Funding

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Sunnyvale, CA (PRWEB) April 17, 2014 — Inbenta, the Semantic Search Engine provider, announces it has closed a $2M Series A funding from a group of investors led by “Amérigo Chile Early Stage and Growth”. Amerigo is an international network of technological Venture Capital funds which forms part of Telefónica’s commitment to boosting technological innovation around the world. Inbenta will use the funds to continue to scale out their A.I. based Semantic Search platform for enterprise customer care solutions while expanding operations worldwide. Read more

Gnip Acquired by Twitter

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Twitter has acquired Gnip, a social data provider that we have covered in the past. According to Chris Moody of Gnip, “Combining forces with Twitter allows us to go much faster and much deeper. We’ll be able to support a broader set of use cases across a diverse set of users including brands, universities, agencies, and developers big and small. Joining Twitter also provides us access to resources and infrastructure to scale to the next level and offer new products and solutions. This acquisition signals clear recognition that investments in social data are healthier than ever. Our customers can continue to build and innovate on one of the world’s largest and most trusted providers of social data and the foundation for innovation is now even stronger. We will continue to serve you with the best data products available and will be introducing new offerings with Twitter to better meet your needs and help you continue to deliver truly innovative solutions.” Read more

Improving Customer Interaction with Machine Learning

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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

A Better Definition for Machine Learning

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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

RPI President Calls for Improved Data Analytics, Connectivity

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According to a recent article out of RPI, “Universities must make new and innovative connections to harness the full power and potential of this data-driven era, Rensselaer Polytechnic Institute President Shirley Ann Jackson said [Tuesday] in a keynote address at the Internet2 Global Summit in Denver, Colorado. Deriving ‘insights from the massive amounts of web-based data that humanity is producing about itself, during the ordinary course of every day…. may be the greatest intellectual challenge and opportunity we all face in academic life,’ President Jackson told the gathering of academic, business, and government leaders in the arena of information technology. ‘Today, we analyze less than 1 percent of the data we capture, even though the answers to many of the great global challenges lie within this overabundant natural resource,’ Jackson said. The challenge, she notes, is finding new ways to address the volume, velocity, variety, and veracity of the data.” Read more

Get a Grip on Big Data with ‘Content Intelligence’

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Jeremy Bentley of KMWorld recently wrote, “The age of the Internet has made us accustomed to having all the information we could want readily available at our fingertips – quite literally so, thanks to laptops, tablets, smartphones and other devices… Unfortunately, we rarely experience the same level of data accessibility in our workplaces, where internal information assets can be massive and hugely complex—and not at all easy to access search with the pinpoint precision that is usually required to find a very specific document or piece of content. Addressing this challenge should be high on the priorities list of any organization aiming to extract value efficiently from unstructured content. But it is proving to be no easy task.” Read more

Semantic Search and the Data Center

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Haim Koshchitzky of Sys-Con Media recently wrote, “Enterprise applications can ‘live’ in many places and their logs might be scattered and unstandardized. First generation log analysis tools made some of the log data searchable, but the onus was on the developer to know what to look for. That process could take many hours, potentially leading to unacceptable downtime for critical applications. Proprietary log formats also confuse and confound conventional keyword search. That’s why semantic search can be so helpful. It uses machine intelligence to understand the context of words, so it becomes possible for a Google user to type ‘cheap flights to Tel Aviv on February 10th’ rather than just ‘cheap flights’ and receive a listing of actual flights rather than links to airline discounters. Bing Facebook, Google and some vertical search engines include semantic technology to better understand natural language. It saves time and creates a better experience.” Read more

Load-Control: Semantria Takes On The Social Media Surge Infrastructure Challenge

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Image courtesy: Flickr/Webtreats

Semantria is tackling some of the challenges that come with being a cloud-based social media services provider startup. The company offers a text and sentiment analysis service (which you can read about here) to clients and partners. That includes companies like Sprinklr, which manages the social customer experience for other brands with the help of Semantria’s API for analyzing social signals about those clients.

The good news is that with growing social data volumes, there’s a growing need for semantically-oriented services like Semantria’s that help businesses make sense of that information for themselves or their clients. The downside is that a huge surge in volume of social mentions around a company, its product, or anything else can hit such services hard in the pocketbook when it comes to acquiring the cloud infrastructure to handle the tidal wave.

“Everybody suffers from this kind of thing,” says Semantria founder and CEO Oleg Rogynskyy. “We experience it daily.”

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Twitris Measures Sentiment About Indian Elections

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Oneindia News recently shared a new case study of how Twitris was used to measure sentiment about the current elections in India. The article begins, “Based on 900,000 tweets collected from 15 states about three major political parties (BJP, Congress and AAP), our analysis shows how people talked about and reacted to each political party. Using Twitris, their Collective Social Intelligence platform, the researchers at the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) at Wright State University processed each tweet to compute sentiment about the mentioned political party. One parameter to measure popularity is to check which political party gets most positive sentiment or least negative sentiment. Just counting negative (or positive) sentiments on a politician provides, as in this Deccan Herald story, provides little useful information about the state of electorate.” Read more

Machine Learning’s Future: Fortune 500 Buys In, Manufacturing Sees The Light

STServerMartin 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.”

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