For the Defense Department, last month's executive order on artificial intelligence was the starting gun, and the department doesn't mean to lose the …

Polarr, a six-year-old San Jose computer vision startup cofounded by Stanford graduate and Google veterans Borui Wang and Derek Yan, today announced that it has secured $11.5 million in series A funding led by Threshold Ventures, with participation from Cota Capital and Pear Ventures. Wang said the fresh capital -- which brings its total raised to $13.5 million, according to Crunchbase -- will be used to accelerate research and development; expand platform and service support; and grow its technology partnerships in drone, home appliance, ecommerce, and image storage verticals. "As deep learning compute shifts from the cloud to edge devices, there is a growing opportunity to provide sophisticated and creative edge AI technologies to mobile devices," said Wang, who serves as CEO. "This new round of financing is a tangible endorsement of our approach to enable and inspire everyone to make beautiful creations." Threshold Ventures' Chris Kelley and Pear Ventures' Mar Hershenson will join Polarr's board of directors as part of the round.

Abstract: This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image. Most current methods in 3D hand analysis from monocular RGB images only focus on estimating the 3D locations of hand keypoints, which cannot fully express the 3D shape of hand. In contrast, we propose a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose. To train networks with full supervision, we create a large-scale synthetic dataset containing both ground truth 3D meshes and 3D poses. When fine-tuning the networks on real-world datasets without 3D ground truth, we propose a weakly-supervised approach by leveraging the depth map as a weak supervision in training.

With traditional TV viewing on the decline, we discuss several ways Big data and Machine Learning can assist with online video, including redefining user recommendations, improving video buffering and leveraging MAM orchestration. Let's face it: traditional TV is fading. Viewing habits have totally changed, with spectators now favoring online video. In this competitive market where big players like Netflix and Hulu are racing for most eyeballs it might be rather difficult to encourage audiences to stay tuned to your video content. According to NewVantage Venture Partners, Big Data and Machine Learning (ML) deliver true value to enterprises.

When it comes to Sci-Fi movies, artificial intelligence (AI) is one of Hollywood's go-to themes. As a technology of which we're still yet to fully realize the potential, scriptwriters and technology doomsdayers have been busy predicting a time when AI spins out of our control, and we're forced to welcome our robot overlords. But how accurate are some of pop culture's most well-known AI creations? Communications provider Plusnet sought to find out by asking Peter Scott, an author on AI and former computer scientist at NASA, to grade the AI predictions of some of Sci-Fi greatest movies. Ridley Scott's 80s Sci-Fi masterpiece may be held in the highest esteem in film circles, but when it comes to how well it predicts the direction of artificial intelligence, experts find it wanting. Set in 2019, the film is making a prediction 37 years in the future (45 years if you count the release date of the original book on which it's based).

In his new book, Deep Learning, the cardiologist argues that artificial intelligence can bring humanity back to medicine.

One such innovation is the use of artificial intelligence in the industry.

I don't mind letting your programs see my private data as long as I get something useful in exchange. But that's not what happens. A former co-worker told me once: "Everyone loves collecting data, but nobody loves analyzing it later." This claim is almost shocking, but people who have been involved in data collection and analysis have all seen it. It starts with a brilliant idea: we'll collect information about every click someone makes on every page in our app! And we'll track how long they hesitate over a particular choice! And how often they use the back button!