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Using Word2vec for Music Recommendations – Towards Data Science

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Streaming services have changed the way in which we experience content. While recommendation systems previously focused on presenting you with content you might want to purchase for later consumption, modern streaming platforms have to focus instead on recommending content you can, and will want to, enjoy in the moment. Since any piece of content is immediately accessible, the streaming model enables new methods of discovery in the form of personalized radios or recommendation playlists, in which the focus is now more on generating sequences of similar songs that go well together. With now over 700 million songs streamed every month, Anghami is the leading music streaming platform in the MENA region. What this also means, is that the amount of data generated by all those streams proves to be an invaluable training set that we can use to teach machine learning models to better understand user tastes, and improve our music recommendations.


Researchers from University of Melbourne using machine learning to find fake news on Twitter OpenGovAsia

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Researchers at the University of Melbourne are using machine learning to distinguish false information from the truth on social media platform, Twitter. Increasing numbers of people rely on their social media feeds for news. But algorithms on social media platforms prioritise engagement over accuracy, and unscrupulous content creators can easily create and post misleading or even outright false information', motivated by financial, political or other reasons. Professor Stephan Winer and Marie Truelove from the Melbourne School of Engineering have developed a framework to assess whether a tweet is a witness account from a first-hand experience or not, relying on the principle that witness accounts are more trustworthy than hearsay. The framework analyses details of a tweet to determine whether it is a witness account.


Top 10 Technology Trends for 2018: IEEE Computer Society Predicts the Future of Tech

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"The Computer Society's predictions, based on a deep-dive analysis by a team of leading technology experts, identify top-trending technologies that hold extensive disruptive potential for 2018," said Jean-Luc Gaudiot, IEEE Computer Society President. "The vast computing community depends on the Computer Society as the provider for relevant technology news and information, and our predictions directly align with our commitment to keeping our community well-informed and prepared for the changing technological landscape of the future." Dejan Milojicic, Hewlett Packard Enterprise Distinguished Technologist and IEEE Computer Society past president, said, "The following year we will witness some of the most intriguing dilemmas in the future of technology. Will deep learning and AI indeed expand deployment domains or remain within the realms of neural networks? Will cryptocurrency technologies keep their extraordinary evolution or experience a bubble burst?


The Data Science Process With Azure Machine Learning

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It's no secret today that all our applications and devices are generating tons of data; thus making data analytics a very hot topic. Microsoft Azure has all the tools necessary to ingest, manage, and process all this data, which is also known as Big Data. However, all this data in and of itself is not useful unless processed, interpreted, and visualized correctly. Another power behind the data acquired through the years is to make Predictive Analytics, that is, using the data to make forecasts and predictions. But, by only using the data gathered, it is difficult to make an analysis.


Want Disruptive Change? There's An Algorithm For That (Or Soon Will Be)

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Trust me – it's not you. Our world really is more unpredictable than ever. Even the best-laid strategies are being disrupted, whether they are focused on the workplace's culture, technical environment, market dynamics, customer behavior, or business processes. But central to these uncertainties is one constant: an algorithm guiding every step along the evolutionary trail to digital transformation. "Each company has a predictable algorithm that's driving its business model," said Sathya Narasimhan, senior director for Partner Business Development at SAP, on a live episode of Coffee Break with Game Changers Radio, presented by SAP and produced and moderated by SAP's Bonnie D. Graham.



PODCAST: Machine Learning, AgTech and Tensorflow HPE Newsroom

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The age of highly accessible, open source machine learning tools is upon us. No longer niche, everyone -- from data scientists to Japanese cucumber farmers -- is using machine-learning technologies. But what is machine learning? Machine learning is exactly what it sounds like -- software that can learn to solve a problem. Using large sets of data, an algorithm can be trained to understand that data.


Who Runs Retail? The Machines

@machinelearnbot

The arrival of Big Data and the ability to process it have led to improvements in machine learning, creating new use cases for this technology. As a result, machine learning, algorithms that execute tasks without preprogrammed rules, has become a crucial part of a retailer's operations.


Use Machine Learning To Become A Marketing Rock Star

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Though not as glamorous and fame-filled as your favorite singer, marketing professionals can transform themselves into marketing rock stars, finding and playing the latest and greatest harmonies to push your brand to the top of the billboard charts. How can you find the next guitar riff or sound that will enhance your rockstar capabilities? First, ask yourself: Are you currently planning your marketing campaigns for the next period? Even if you are not, you should step back and reflect if you are able measure the impact on your brand and on your customer experience. Often, external agencies have had to track the brand visibility of television content, which has been a time-consuming manual business.


Can AI win the war against fake news?

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It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us. One algorithm meant to shine a light in the darkness is AdVerif.ai,