"They don't have to reinvent the wheel." SyntaxNet is the latest among artificial-intelligence software systems to be released by large tech companies under an open-source license, which allows anyone to access, use and tweak the software. Google released SyntaxNet, its natural language parsing framework, as an open source project on Thursday, adding another option to the growing set of tools for creating applications that utilize artificial intelligence (AI). Amazon has suddenly made a remarkable entrance into the world of open-source software for deep learning, a type of artificial intelligence. SyntaxNet is just one of the numerous artificial intelligence software systems that have been launched by the tech giants under the open source license.

WASHINGTON – Top Republicans suggested on Sunday that Donald Trump needs more public policy schooling, particularly on foreign affairs, to earn the confidence of a fractured party and show he's ready to take on likely Democratic presidential nominee Hillary Clinton. "I think he's going to need to learn. He's going to need to understand really completely … how complex this world is," said Trump's top Senate ally and top foreign policy adviser, Sen. Jeff Sessions, R-Ala. To Rep. Tom Cole, R-Okla., Trump "is a work in progress," more so than most candidates. "Usually you know a lot more about a candidate because they've run for other things. And he does have a shoot-from-the-hip style."

Large-scale classification, where we need to classify hundreds of thousands or millions of items into thousands of classes, is becoming increasingly common in this age of Big Data… So far, however, very little has been published on how large-scale classification has been carried out in practice, even though there are many interesting questions about such cases. Today's paper is a case study on large-scale classification of products at Walmart. The requirement is to classify 10M products into 5000 categories based on fairly minimal product descriptions. Oh, and new products turn up all the time, and the set of categories is continuously evolving. Many learning solutions assume that we can take a random sample from the universe of items, manually label the sample to create training data, then train a classifier.

While in Hollywood films hi-tech robots are portrayed as a threat, Chinese engineers might be able to change your mind. We take a closer look at the role artificial intelligence might play in the future RT LIVE http://rt.com/on-air Follow us on Twitter http://twitter.com/RT_com Follow us on Google http://plus.google.com/ RT (Russia Today) is a global news network broadcasting from Moscow and Washington studios.

Google it today and you'll see that the European Commission has turned up the heat in its long-running probe into anti-competitive behaviour by the web's most popular search engine. EC competition chief, Margrethe Vestager, issued formal objections alleging that Google abuses its dominant position in the market of "general internet search". In particular, the EC claims that Google artificially boosts its own products in returning Google comparison shopping results in its service "Google Shopping", even if those products aren't the best or cheapest – the "most relevant", as the Commission puts it – for consumers. Since taking office in November 2014, Vestager has made the Google inquiry a top priority, signalling a willingness to consider court battles and hefty fines if Google and other digital giants don't fall into line with European competition law. In this, she has displayed a distinct shift from her predecessor, Joaquín Almunia, whose multiple attempts to achieve private settlement with Google fell apart a year ago, before descending into a political and economic boxing match.

The Defense Department of the United States is reportedly seeking entrepreneurial Silicon Valley bigwigs to build out the future of defense with the help of artificial intelligence technology. Can artificial intelligence (AI) be used as a defense armor? Well, the Defense Department of the United States is reportedly seeking entrepreneurial Silicon Valley bigwigs to build out the future of defense with the help of AI technology. With the emergence of artificial intelligence, Washington has sought the expertise of Silicon Valley's technology leaders. In fact, U.S. Defense Secretary Ashton B. Carter visited the Valley for the fourth time to talk about the importance of incorporating artificial intelligence in America's warfare weapons, New York Times notes.

Toyota employed AI technology onto their website for buyers to have a better experience. Artificial intelligence is one of the hottest topics today. It has been used in medicine, games, government services and automobiles to name a few. In fact, according to new reports, Toyota websites are already powered by an AI framework. Phys.org reported that one of the largest automobile companies in the world has already used AI on their websites.

Video Friday is your weekly selection of awesome robotics videos, collected by your dance-challenged Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. SNUMAX is a "multi-functional soft robot" developed by Seoul National University's Biorobotics Laboratory, which won the RoboSoft Grand Challenge this year. "Boomf is the noise a marshmallow makes when it falls through your letterbox and lands on your doormat."

Research indicates that IoT and Machine Learning are more valuable to utilities when used in combination but there are hurdles to overcome first. Machine learning and IoT will enable utilities to better realize the next generation of the power grid: a distributed system with power flows among millions of things like distributed energy resources (DERs), microgrids and in-home devices. All of which will help utilities deliver clean reliable energy and greater customer choice. Utility respondents to new research from SAS and Zpryme, The Autonomous Grid, indicated that IoT and machine learning were more than market hype. These technologies are already delivering actionable results, say respondents.

Creating applications with the ability to learn from available data and making decisions based on the prediction results obtained from the learned models has become a trend-setting aspect in the software industry. Therefore, almost all applications that do predictive modeling uses one or more of these already available machine learning engines. With the ability to directly interact with these engines with simple API calls, intricacy of integration has alleviated rapidly. One of the common practices is training models with machine learning engines and building predictive APIs on top of them to retrieve prediction results for a new event. This article discusses how to train a machine learning model with WSO2 Machine Learner and create a predictive API with WSO2 API Manager with the trained model.