With dozens of powerful text mining features, including access to free and premium Gnip Twitter data, DiscoverText provides software tools to quickly and accurately evaluate text data. Data scientists know that cleaning data can be very time consuming. Users of DiscoverText build custom machine classifiers or "sifters" to find the most relevant items. DiscoverText shortens a process that used to last weeks or months; our machine-learning sifters are created in hours or just a few minutes. We support technical integrations with Twitter and SurveyMonkey.
I've long been both paranoid and optimistic about the promise and potential of artificial intelligence to disrupt -- well, almost everything. Last year, I was struck by how fast machine learning was developing and I was concerned that both Nokia and I had been a little slow on the uptake. What could I do to educate myself and help the company along? As chairman of Nokia, I was fortunate to be able to worm my way onto the calendars of several of the world's top AI researchers. But I only understood bits and pieces of what they told me, and I became frustrated when some of my discussion partners seemed more intent on showing off their own advanced understanding of the topic than truly wanting me to get a handle on "how does it really work."
Here's how Google Play is using AI to improve search Trying to find an app on the Google Play Store can be an exercise in frustration, especially if it's an eagerly anticipated app or new release. Things don't get much better for mega-popular apps, as the search results are often cluttered with irrelevant results. Fortunately, Google is working on a solution, using machine learning to get better results. "Searches by topic require more than simply indexing apps by query terms; they require an understanding of the topics associated with an app," the team of software engineers wrote. The work required machine-learning approaches, but one big challenge for machine learning was the size of the data-set to work with.
Join our experts and learn more about How can organizations benefit from #MachineLearning? #saschat on December 9th at 3.00 CEST pic.twitter.com/GCNJDPnT3A SAS Institute France (@SASFrance)Mon, Dec 05 2016 15:04:01 Q1: #MachineLearning is a hot topic and is taking hold. Peter Pugh-Jones (@rubricsinger)Fri, Dec 09 2016 14:11:39 A1: With increased processing power and data science skills more and more complex decisions can be made using machine learning. Henrikki Hervonen (@henkkuh)Fri, Dec 09 2016 14:09:04 #saschat A1: Also the biggest tech companies have used a machine learning approach to deal with our personal data. Marcel Lemahieu (@MarcelLemahieu)Fri, Dec 09 2016 14:10:36 A1. #machinelearningn has gained momentum with the tremendous computing power we have today.
Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions--about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results--may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming. Information extraction--or automatically classifying data items stored as plain text--is thus a major topic of artificial-intelligence research. Last week, at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, researchers from MIT's Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head.