In the past decade, countries and regions around the globe have developed strategic roadmaps to guide investment and development of robotic technology. Roadmaps from the US, South Korea, Japan and EU have been in place for some years and have had time to mature and evolve. Meanwhile roadmaps from other countries such as Australia and Singapore are just now being developed and launched. How did these strategic initiatives come to be? What do they hope to achieve? Have they been successful, and how do you measure success?

Late last year, we wrote about how Australia was paying a stupendous amount of money to try using drones and artificial intelligence to detect sharks off of popular beaches. We were skeptical, mostly because it's hard to make a convincing argument that shark attacks are actually that big of a problem, in Australia or anywhere else, compared to other, bigger problems that we might want to address first. One of those bigger problems, in Australia and in many other places, is drowning--in Australia in 2016, about 120 people drowned on the Australian coast, 60 times more people than were fatally attacked by sharks. Fortunately, the drones doing the shark spotting also happen to carry life preserver pods along with them, and last week, a drone being used for training managed to save a pair of struggling swimmers 700 meters off the coast of New South Wales. According to the Sydney Morning Herald, lifeguards were using the drone during a training session when "a call came through of two distressed swimmers."

"A collection of smart devices may not make you smarter. There seems to be a gap between what technology has to offer and what we are naturally able to do" Suranga Nanayakkara slips a black ring onto his finger and points. This ring, he explains, helps visually impaired people read by converting text into speech. Nanayakkara points at a poster on the wall more than a metre away, clicks a small button on the side of the ring, and almost instantaneously a female voice starts reading out the poster's header through the headphones he's wearing. Such optical character recognition technology, or OCR, already exists but is often locked inside clunky highlighter-style devices that are slow and cumbersome.

By Varun Mathur Mobile banking was one of the first apps for smartphones. Financial brands led the charge on the first mainstream wearable, the Apple Watch. Now, conversational interfaces including voice-first interfaces are the next frontiers to explore as consumers increasingly look for speed, convenience and security in their banking tasks. Why should banks invest in voice? Because, it is the most natural means of communication and can lead to more personalized interactions.

Deep learning has revolutionized how we process the vast firehoses of data that define modern life. Yet, the daily drumbeat of AI headlines tends to center on the commercial applications of AI and how it is reshaping how companies do business. In a refreshing twist, a new open AI challenge by the World Bank, in collaboration with WeRobotics and OpenAerialMap, illustrates the incredible potential of deep learning for humanitarian applications, especially in the critical hours and days after a major natural disaster. One of the most exciting application areas of modern deep learning tools has been the use of neural networks to examine imagery at accuracy and detail levels impossible just a few years ago. Today state-of-the-art neural systems can examine hundreds of millions of images, cataloging them into tens of thousands of categories, estimating the location they were taken, their emotion, look in the background for pollution and natural disaster damage and even estimate the level of "violence" they portray, while creating new models is increasingly becoming point-and-click.

From hurricanes to wildfires, 2017 brought the world a number of natural disasters -- as well as some tech to deal with them. We have more information than ever following a disaster thanks to unmanned aerial vehicles (UAVs) and sophisticated satellites that can capture images of disasters from the air, but we are still working on ways to process the data so it is valuable for relief efforts. That's where deep learning comes in, says the World Bank in collaboration with WeRobotics and OpenAerialMap. On Jan. 10, 2018, World Bank issued an artificial intelligence (AI) challenge to explore how deep learning could be used in the wake of natural disasters. Deep learning is what enables AI to recognize patterns in images, sounds, and other data using a neural network that mirrors our own grey matter.

CANBERRA – A flying drone has dropped a flotation device to two teens caught in a riptide in heavy seas off the Australian coast in what officials describe as a world-first rescue. Monty Greenslade and Gabe Vidler got into trouble on Thursday at Lennox Head, 750 kilometers (470 miles) north of Sydney. They were about a kilometer (0.6 mile) from lifeguards who were about to start training with the new drones, equipped with a camera, rescue gear and six rotors. After a friend raised the alert, lifeguard Jai Sheridan said he piloted the drone to the swimmers and dropped a rescue pod minutes faster than lifeguards could have reached the pair by conventional means. "I was able to launch it, fly it to the location, and drop the pod all in about one to two minutes. On a normal day, that would have taken our lifeguards a few minutes longer to reach the members of the public," Sheridan said in a statement.

We present a mixed multinomial logit (MNL) model, which leverages the truncated stickbreaking process representation of the Dirichlet process as a flexible nonparametric mixing distribution. The proposed model is a Dirichlet process mixture model and accommodates discrete representations of heterogeneity, like a latent class MNL model. Yet, unlike a latent class MNL model, the proposed discrete choice model does not require the analyst to fix the number of mixture components prior to estimation, as the complexity of the discrete mixing distribution is inferred from the evidence. For posterior inference in the proposed Dirichlet process mixture model of discrete choice, we derive an expectation maximisation algorithm. In a simulation study, we demonstrate that the proposed model framework can flexibly capture differently-shaped taste parameter distributions. Furthermore, we empirically validate the model framework in a case study on motorists' route choice preferences and find that the proposed Dirichlet process mixture model of discrete choice outperforms a latent class MNL model and mixed MNL models with common parametric mixing distributions in terms of both in-sample fit and out-of-sample predictive ability. Compared to extant modelling approaches, the proposed discrete choice model substantially abbreviates specification searches, as it relies on less restrictive parametric assumptions and does not require the analyst to specify the complexity of the discrete mixing distribution prior to estimation. 2 1. Introduction

With an affection for nerd culture that is inversely proportional to its budget, this lo-fi sci-fi comedy is destined for laugh-filled late-night viewing. "This Giant Papier-Mâché Boulder Is Actually Really Heavy" pays homage to favorites like "Doctor Who" and "Battlestar Galactica" while looking like it cost less than a cosplay effort to make. Serious fan Jeffrey (Daniel Pujol) drags his friends Tom (cowriter and director Christian Nicolson) and Gavin (Lewis Roscoe) to a science-fiction convention. There, they get far more than their passes offer when they're sucked into an alternate universe that looks just like a black-and-white B movie set in space, where they're the heroes who have to fight intergalactic supervillain Lord Froth (Joseph Wycoff) alongside heroine Emmanor (Sez Niederer). Fans of the silliness of "Mystery Science Theater 3000" and "Galaxy Quest" will find that Nicolson and his co-writer Andrew Beszant are on their wavelength with this inventive New Zealand film.

Two teenage boys, struggling to make their way back to shore, were saved by a drone in Australia on Wednesday, in what officials say is a first-of-its-kind rescue mission by an unmanned aircraft -- one captured by the drone's camera and later broadcast by Arab News and other outlets. A beachgoer caught a glimpse of the distressed swimmers, 15 and 17, who were fighting dangerous waves off Australia's Far North Coast in New South Wales. He alerted lifeguards, who happened to be in the middle of a drone training session, learning how the unmanned aerial vehicles (UAV) work. Lifeguard supervisor Jai Sheridan got the call and piloted the drone, dubbed "Little Ripper," toward the swimmers, about 2,300 feet over the ocean. Within 70 seconds the drone was hovering over the boys and had dropped a self-inflating rescue pod into the water.