The future of luxury cars will be entirely personalized, fully autonomous and exceedingly comfortable -- at least if Rolls Royce has anything to do with it. On Thursday, the venerable British automaker, which is now owned by BMW Group BMW, 0.53%, unveiled its first self-driving car, the Vision Next 100 concept car, a spectacularly ornate, futuristic land yacht that wouldn't look out of place in the sci-fi classic "Tron." At nearly 20 feet long and 5 feet tall, the car features a zero-emissions powertrain, glass canopy roof and virtually invisible wheels, which are tucked inside the sleek chassis (Got a flat? The robot will fix it). But it's inside where the Vision Next 100 really shines, sporting a silk sofa -- or as Rolls puts it, "a beautifully textured, ivory-coloured luxurious throne upon which our passengers are conveyed" -- deep wool carpet, hand-crafted Madagascar wood paneling and a big-screen TV.

Their squishy robotic hands can gather coral samples more delicately than robots, and in places humans can't reach. Developed with support from a National Geographic Innovation Challenge Grant, the hands were first tested in tanks in March 2015 and then taken to the Red Sea in May. After a successful expedition, Wood and Gruber hope the technology may have even broader applications.

Recently, mobile operators in many developing economies have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, a consistent difficulty has been identifying individuals most likely to benefit from access to the new technology. Here, we combine terabyte-scale data from three different mobile phone operators from Ghana, Pakistan, and Zambia, to better understand the behavioral determinants of mobile money adoption. Our supervised learning models provide insight into the best predictors of adoption in three very distinct cultures. We find that models fit on one population fail to generalize to another, and in general are highly context-dependent. These findings highlight the need for a nuanced approach to understanding the role and potential of financial services for the poor.

When I spoke to the director, he referenced movies including the original Jurassic Park, where the combination of CGI for longer shots and live-action robots for close up. The behind the scenes video below shows how puppeteering on a custom-built rig or green suited dancers wearing the shoes created movement that's realistic, without the uncanny valley that overused computer renderings can produce, even in current movies. There are some instances of computer generated rendering for close-ups, like when the shoes are waving their shoelaces in the breeze. As described by St. Lucia's Jean-Philip Grobler, he and Norton took their cues not just from cheesy 80s movies (note the quick zooms) but also Pixar's knack for bringing forth a hidden life from everyday objects. The human characters in the video are played by the rest of the band, transforming the song from Grobler's idea of his own journey from South Africa to the US, to something else entirely.

Oscar Pistorius has removed his prosthetic legs in court to demonstrate how he walks without them. The athlete's lawyer, Barry Roux, asked the court's permission. The hearing is reconsidering Pistorius' sentence after a court found him guilty of murdering Reeva Steenkamp, overturning an earlier manslaughter verdict.

Innovation is among the key factors driving a country's economic and social growth. But what are the factors that make a country innovative? How do they differ across different parts of the world and different stages of development? In this work done in collaboration with the World Economic Forum (WEF), we analyze the scores obtained through executive opinion surveys that constitute the WEF's Global Competitiveness Index in conjunction with other country-level metrics and indicators to identify actionable levers of innovation. The findings can help country leaders and organizations shape the policies to drive developmental activities and increase the capacity of innovation.

Hindsait's AI platform identifies and extracts actionable data from information trapped in clinical notes and other electronic records for improved patient outcomes at lower costs Based on its recent analysis of the market for artificial intelligence (AI) systems for the healthcare industry, Frost & Sullivan recognizes Hindsait, Inc. with the 2016 Global Frost & Sullivan Award for Visionary Innovation Leadership. Hindsait has demonstrated a strong commitment to breaking down communication barriers and enhancing healthcare delivery using AI and cognitive computing technology. With its novel software-as-a-service (SaaS) platform, Hindsait helps payers reduce costs and improve the quality of services while simultaneously enabling healthcare providers, such as accountable care organizations (ACOs) and hospitals, to reduce risks and offer superior, more affordable care. Although there are organizations that apply AI and cognitive computing in healthcare, many of them simply adapt approaches applied in other industries to healthcare. Hindsait, on the other hand, is dedicated to the needs of the healthcare industry.

Can artificial intelligence help visually impaired people recognize objects around them and improve their quality of life? EyeSense, an iPad app developed in Egypt, has the ability to "learn" objects in its environment, having been trained by its users. A visually impaired person can point their device in the direction of where they think something might be--say, a coffee cup--and a voice will say that the app recognizes that object. "The key strength of the app is that it also recognizes basic facial expressions, like winks or smiles. This enhances human interaction," says Joanna Marczak, a spokesperson for its developer, ID Labs.

In this work, we consider compressed sensing reconstruction from $M$ measurements of $K$-sparse structured signals which do not possess a writable correlation model. Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how this signal model can be used within a Bayesian framework of signal reconstruction. By deriving a message-passing inference for general distribution restricted Boltzmann machines, we are able to integrate these inferred signal models into approximate message passing for compressed sensing reconstruction. Finally, we show for the MNIST dataset that this approach can be very effective, even for $M < K$.

This tutorial will describe how a surprisingly small amount of code can be used to build a recommendation engine using the MapR Sandbox for Hadoop with Apache Mahout and Elasticsearch. This tutorial will run on the MapR Sandbox. The tutorial also requires Elasticsearch and Mahout to be installed on the sandbox. Step 1: Indexing the movie meta data in Elasticsearch In Elasticsearch, documents contain fields which are, by default, all indexed. Typically documents are written as a single-level JSON structure.