There is a lot you can do with Alexa. Here's how you can add skills. It was every Amazon Echo owner's nightmare. Alexa, the connected speaker, really, truly, was listening in on your conservations, and behind your back, passed on the recording of a private chit-chat to someone on your Echo contact list. This actually happened this week, according to Seattle TV station KIRO, which told the story of a Portland woman's privacy gone amuck.

This post will help you to create your first Azure Machine Learning workspace. For this, you should log in with either a free or a paid Azure subscription, or you must be using the free trial Azure Machine learning offer. First, open the Azure Management Portal https://manage.windowsazure.com, The interface contains all the tools that you need in order to create, manage, and publish your machine learning experiments on the cloud. So, to create an ML (Machine Learning), you should click on the button.

TIANJIN, May 18 (Xinhua) -- Machines that can impeccably synthesize the human voice, assist judges in sentencing, and tell customers whether a dress is a good fit, have been displayed at the second World Intelligence Congress in Tianjin. Riding the global waves of artificial intelligence (AI), China aims to improve the productivity and inject new momentum into its economy by encouraging more players to tap the fledging industry. By June 2017, about one-fourth of the world's 2,542 AI companies were in China, where around 15,700 AI patents were filed, ranking second after the United States, according to the China Internet Network Information Center. "China should seize opportunities to boost innovation in deep learning, smart algorithms and chips, and modernize industries with AI technologies," Lin Nianxiu, deputy head of the National Development and Reform Commission (NDRC), told a room of entrepreneurs, researchers and policy-makers at the congress, which was held from May 16 to 18. NDRC is China's top economic planning agency. In July last year, the State Council issued a plan for new generation AI, pledging to make the industry a major new growth engine and improve people's life by 2020 and make the country the world's center and leader for AI innovation by 2030.

Netflix's famous algorithm challenge awarded a million dollars to the best algorithm for predicting user ratings for films. But did you know that the winning algorithm was never implemented into a functional model? Netflix reported that the results of the algorithm just didn't seem to justify the engineering effort needed to bring them to a production environment. At your company, you can create the most elegant machine learning model anyone has ever seen. That's no easy feat, which is why we're presenting you with seven machine learning best practices.

The Great Wall of China, one of the Seven Wonders of the World is set to be restored with the drone assistance. Intel has teamed up with the China Foundation for Cultural Heritage Conservation in order to protect and restore this wonder. In the coming months, the drone will be deployed for aerial photography of the walls and help team gauge the Wall's current condition. "This partnership showcases how Intel's technology breakthroughs can benefit the restoration of Great Wall of China. It's a powerful example of how Intel's artificial intelligence and drone technology can positively impact the world," says Alyson Griffin, Intel's leader of global brand marketing.

Over the past several years, many of Wall Street's hottest stocks came from the world of technology. Household names such as Netflix, Amazon and Facebook emerged as top performers and helped send broader markets higher. However, where these stocks once promised ludicrous growth now stands increased competition and industry maturity. The trends of yesterday -- including video streaming, e-commerce and social media -- remain promising, but they are no longer the upstart segments they once were. But investors looking for the next high-growth opportunity may look no further than a brand-new trend that has the chance to expand exponentially over the coming years.

This sounds like an underspecified example. I mean, A3C and DQN/Q-learning aren't even the same in terms of off or on-policy learning. A3C has mostly been replaced by PPO, and on-policy SOTA has moved on from that to Impala/Unicorn. I'm not sure what is SOTA for off-policy learning, but Rainbow outperforms DQN and most of the DQN zoo. And progress here may be somewhat illusory, as the methodological papers have been pointing out: a lot of these tasks are not inherently difficult, there's so much variance in training runs, improvements may be to undocumented tweaks or just somewhat better hyperparameters...

VentureBeat has a job opening for an AI reporter with experience covering tech news via a wide variety of story types, including breaking news, analysis, and features. This is a full-time position. You have to enjoy covering the business of technology, but more important is a critical eye, an ability to develop sources, and a knack for breaking news. For this role, a demonstrable interest in AI is required, including machine learning, computer vision, natural language processing, neural networks, and self-driving cars. A readiness to cover the major tech giants as well as the little guys is a plus.

The recommendation engine is one of the biggest martech innovations of the last few years, and it's shaping our entire digital experience. For example, let's say I visit the Harvard Business Review website and read about manufacturing marketing tactics (as one does). Soon, I'm getting retargeted marketing emails recommending the latest manufacturing supply chain management articles and other related content, as well as customized recommendations the next time I visit the site. When I fire up Netflix, I know that my experience is going to be managed in part because Netflix knows I've been binging on the show Haven and it's going to suggest other supernatural crime fighting series. The development of recommendation engine technology has brought digital personalization to a whole new level.

Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline. In this paper, we introduce a novel object detection method, based on synthetic energy maps and the watershed transform, called Deep Watershed Detector (DWD). Our method is specifically tailored to deal with high resolution images that contain a large number of very small objects and is therefore able to process full pages of written music. We present state-of-the-art detection results of common music symbols and show DWD's ability to work with synthetic scores equally well as on handwritten music.