The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.

Motion control problems have become standard benchmarks for reinforcement learning, and deep RL methods have been shown to be effective for a diverse suite of tasks ranging from manipulation to locomotion. However, characters trained with deep RL often exhibit unnatural behaviours, bearing artifacts such as jittering, asymmetric gaits, and excessive movement of limbs. Can we train our characters to produce more natural behaviours? A wealth of inspiration can be drawn from computer graphics, where the physics-based simulation of natural movements have been a subject of intense study for decades. The greater emphasis placed on motion quality is often motivated by applications in film, visual effects, and games.

Before my eyes were even open, I spoke to my smart assistant this morning: "Alexa," I asked hopefully, "what time is it?" She kindly replied, "The time is seven twenty-seven a.m." I did not need her to tell me the morning was going to be a struggle. An hour later, dropping the kids off at school, I realized their violins were left in haste by the front door. As I made the U-turn in the school parking lot, I thought to myself, "Inconceivable! I have all this technology surrounding me! How long do I have to wait for this to get easier?"

Free users of Spotify could soon benefit from premium account features thanks to an updated version of the app. Sources say the update is designed to make the service easier to use in a bid to boost subscribers after launching on the stock market last week. Mobile users with free plans will be able to access playlists faster and have greater control over how they listen to music on playlists, sources say. Free users of Spotify will soon benefit from premium account features, thanks to an updated version of the app, sources say. At the moment, the free plan prevents users from selecting tracks within a playlist.

Machines don't actually have bias. AI doesn't'want' something to be true or false for reasons that can't be explained through logic. Unfortunately human bias exists in machine learning from the creation of an algorithm to the interpretation of data – and until now hardly anyone has tried to solve this huge problem. A team of scientists from Czech Republic and Germany recently conducted research to determine the effect human cognitive bias has on interpreting the output used to create machine learning rules. The team's white paper explains how 20 different cognitive biases could potentially alter the development of machine learning rules and proposes methods for "debiasing" them. Biases such as "confirmation bias" (when a person accepts a result because it confirms a previous belief) or "availability bias" (placing greater emphasis on information relevant to the individual than equally valuable information of less familiarity) can render the interpretation of machine learning data pointless.

Artificial intelligence will solve Facebook's most vexing problems, chief executive Mark Zuckerberg insists. He just can't say when, or how. Zuckerberg referred to AI technology about 23 times during his five-hour testimony before a joint Senate committee hearing Tuesday, saying that it would one day be smart, sophisticated and eagle-eyed enough to fight against a vast variety of platform-spoiling misbehavior, including fake news, hate speech, discriminatory ads and terrorist propaganda. Over the next five to 10 years, he said, artificial intelligence would prove a champion for the world's largest social network in resolving its most pressing crises on a global scale -- while also helping the company dodge pesky questions about censorship, fairness and human moderation. "We started off in my dorm room with not a lot of resources and not having the AI technology to be able to proactively identify a lot of this stuff," Zuckerberg told the lawmakers, referring to Facebook's famous origin story.

As I reflect on cloud computing and the media industry since last year's NAB, I see two emerging trends. First, content creators and broadcasters such as Rakuten, RTL, and Al Jazeera are increasingly using the global reach, hybrid model, and elastic scale of Azure to create, manage, and distribute their content. Second, AI-powered tools for extracting insights from content are becoming an integral part of the content creation, management and distribution workflows with customers such as Endemol Shine Group, and Zone TV. Therefore, at this year's NAB, we are focused on helping you modernize your media workflows, so you can get the best of cloud computing and AI. We made a number of investments to enable better content production workflows in Azure, including the recent acquisition of Avere Systems.

Ok, I just skimmed through the video but why on earth did you get the names of key places and cities right, but messed up the title? Like those fancy chairs kings sit on. Not the spiky things that grow in the wilderness. You don't want to sit on those.

It's a custom mixtape of 30 songs they've never listened to before but will probably love, and it's pretty much magic. It makes me feel seen. It knows my musical tastes better than any person in my entire life ever has, and I'm consistently delighted by how satisfyingly just right it is every week, with tracks I probably would never have found myself or known I would like. As it turns out, I'm not alone in my obsession with Discover Weekly. The user base goes crazy for it, which has driven Spotify to rethink its focus, and invest more resources into algorithm-based playlists.