You might know motion capture as the tech that transformed Andy Serkis into Gollum, but now it can transform everyday people into animated avatars in virtual worlds, and all in real-time. Motion capture--which uses body sensors, ultra-precise cameras, and modeling software to create 3D animations from real-life human movement--is now taking on location-based virtual reality, or LBVR. PCWorld visited a leading motion capture company called Vicon in Oxford, England to learn how mocap has evolved to take on this new frontier in entertainment. If you've watched behind-the-scenes footage of how motion capture (or mocap) works, you've probably seen actors in skintight lycra suits covered with golf ball-sized sensors. Normally, dozens of infrared cameras track these sensors to model an actor's movements.

Ugly is a film built on beautiful contradictions. The characters have odd, blocky proportions and stumble around like a group of drunk boxers. The town, too, is comprised of weirdly angular cars, buildings, bicycles and trash bags. There's a consistency to the art style, though, that resembles origami and the papercraft video game Tearaway. And the lighting, a stunning mixture of pastel pinks and blues, gives every frame a warm, inviting sheen.

As it gears up to move into a new home (a Galaxy Home, to be specific), Bixby is far from ready. Samsung's digital assistant has become infamous for its tardiness, and even after showing up late to the AI party, Bixby doesn't have much to show for the extra time. It's not smarter than the rest and doesn't offer any new tricks, even in the recently announced Galaxy Home, other than perhaps better sound quality. As much as I'm excited about Samsung potentially giving Amazon, Google and Apple some competition in the smart speaker space, I'm pretty sure they have nothing to worry about, if my time with Bixby on the Note 9 is any indication. To be clear, Samsung still hasn't launched the Bixby-powered Galaxy Home speaker, and no one seems to have published an in-depth hands-on with it.

"We created GraphPipe to solve these three challenges. It provides a standard, high-performance protocol for transmitting tensor data over the network, along with simple implementations of clients and servers that make deploying and querying machine learning models from any framework a breeze.

I just got hold of some embarrassing video footage of Texas senator Ted Cruz singing and gyrating to Tina Turner. His political enemies will have great fun showing it during the midterms. Donald Trump will call him "Dancin' Ted." Okay, I'll admit it--I created the video myself. But here's the troubling thing: making it required very little video-editing skill. I downloaded and configured software that uses machine learning to perform a convincing digital face-swap.

Watch out humans, robots could soon compete for your acting roles. Director Tony Kaye is looking to cast an artificial intelligence robot for his upcoming film '2nd Born,' the sequel to the yet-to-be-released '1st Born.' Kaye, who is behind the 1998 crime drama'American History X,' would employ the'real robot' as the lead in 2nd Born, according to Deadline. Artificial Intelligence,' director Tony Kaye wouldn't use a computer-generated robot for the role; instead, it would be an android trained in various acting methods and techniques Artificial Intelligence,' Kaye wouldn't use a computer-generated robot for the role; instead, it would be an android trained in various acting methods and techniques. What's more, Kaye and producer Sam Khoze hope that the robot will be recognized by the Screen Actors Guild. This would make the robot eligible for awards consideration.

Hence, it is seldom the case where a single oracle labels an entire collection. Furthermore, the Web, through its social nature, also exploits the wisdom of crowds to annotate large collections of documents and images. By categorizing texts, tagging images or rating products and places, Web users are generating large volumes of labeled content. However, when learning supervised models from crowds, the quality of labels can vary significantly due to task subjectivity and differences in annotator reliability (or bias) [9], [10]. If we consider a sentiment analysis task, it becomes clear that the subjectiveness of the exercise is prone to generate considerably distinct labels from different annotators. Similarly, online product reviews are known to vary considerably depending on the personal biases and volatility of the reviewer's opinions. It is therefore essential to account for these issues when learning from this increasingly common type of data. Hence, the interest of researchers on building models that take the reliabilities of different annotators into consideration and mitigate the effect of their biases has spiked during the last few years (e.g.

Too many cooks spoil the broth, and the same goes for ants. A study into how ants cooperate has found that the optimum strategy is for most of them not to do any work. The findings may be useful for creating large swarms of robots. Ants create networks of narrow underground tunnels by excavating soil bit by bit as a team.

Imaging technologies, which extend human vision capabilities, are such a natural part of our current everyday experience that we often take them for granted. However, the ability to capture images with new kinds of sensing devices that allow us to see more than what can be seen by the unaided eye has a relatively recent history. In the early 1800s, the first ever photograph was taken: an unassuming picture that required days of exposure to obtain a very grainy image. In the late 1800s, a photograph was used for the first time to see the movement of a running horse that the human eye alone could not see. In the following years, photography played a pivotal role in recording human history, ranging from influencing the creation of the first national parks in the United States all the way to documenting NASA's Apollo 11 mission to put a man on the Moon.

NEW YORK--Amazon is dealing with striking workers in Europe, site disruptions during its Prime Day sale event and protestors inside and out of the Javits Convention Center, site of this week's AWS NYC Summit 2018. None of which appeared to bother Amazon Web Services executives at the Summit, who announced new capabilities for its artificial intelligence machine learning and compute services on the AWS cloud. With artificial intelligence and machine learning services in demand, AWS rolled out improvements to its SageMaker service, which enables users to build and deploy models in the cloud. Dr. Matt Wood, AWS's General Manager for Machine Learning, announced two updates to the help speed up the service: SageMaker Streaming Algorithms and SageMaker Batch Transform. Streaming Algorithms enables users to stream large amounts of training data from the S3 storage service into SageMaker.