A couple of years ago, Zipline created a national drone delivery system to ship blood and drugs to remote medical centers in Rwanda. Now it has developed what it claims is the world's swiftest commercial delivery drone, with a top speed of 128 kilometers an hour (a hair shy of 80 miles per hour). Zipline is hoping its new fixed-wing aerial robot, which is both speedier and easier to maintain than its predecessor, will help it win business in an industry that's attracted plenty of big players. They include Amazon, which has been testing its Prime Air drone delivery service for years in the UK and elsewhere, and Project Wing, part of Alphabet's secretive X lab, which is using its drones to deliver pharmaceuticals and burritos in a pilot project in Australia. Soon these and other companies will be able to experiment more in America, too.

Other agencies also sounded the public alarm. Air traffic controllers said they were concerned about the increasing numbers of drones flying illegally in controlled airspace, while regulators said flying drones into a flight path was inexcusable and the "height of stupidity."

WELLINGTON – When a drone flew within meters of a landing plane last week, endangering 278 passengers and crew, Air New Zealand responded by saying that such reckless drone operators should be thrown in prison. Other agencies also sounded the public alarm. Air traffic controllers said they were concerned about the increasing numbers of drones flying illegally in controlled airspace, while regulators said flying drones into a flight path is inexcusable and the "height of stupidity." Yet AP has found that none of the agencies involved notified police about the drone. Not while it was endangering the plane, nor later to try to track down the perpetrator.

Globally, the economic divide is growing. The rich are getting richer and the poor are getting poorer. In Australia, more than a quarter of households have recently experienced a decrease in income. The reasons for the growing economic divide are many and complex. They include factors such as job insecurity, wage cuts and underemployment.

The subgraph isomorphism problem involves deciding whether a copy of a pattern graph occurs inside a larger target graph. The non-induced version allows extra edges in the target, whilst the induced version does not. Although both variants are NP-complete, algorithms inspired by constraint programming can operate comfortably on many real-world problem instances with thousands of vertices. However, they cannot handle arbitrary instances of this size. We show how to generate "really hard" random instances for subgraph isomorphism problems, which are computationally challenging with a couple of hundred vertices in the target, and only twenty pattern vertices. For the non-induced version of the problem, these instances lie on a satisfiable / unsatisfiable phase transition, whose location we can predict; for the induced variant, much richer behaviour is observed, and constrainedness gives a better measure of difficulty than does proximity to a phase transition. These results have practical consequences: we explain why the widely researched "filter / verify" indexing technique used in graph databases is founded upon a misunderstanding of the empirical hardness of NP-complete problems, and cannot be beneficial when paired with any reasonable subgraph isomorphism algorithm.

Lawmakers in the United States and Europe are demanding to know more about the company's privacy practices after a whistleblower said consultancy Cambridge Analytica improperly accessed data to target U.S. and British voters in elections.

The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public. Robotics and AI amplify human potentials, increase productivity and are moving from simple reasoning towards human-like cognitive abilities. Current AI technologies are used in a set area of applications, ranging from healthcare, manufacturing, transport, energy, to financial services, banking, advertising, management consulting and government agencies. The global AI market is around 260 billion USD in 2016 and it is estimated to exceed 3 trillion by 2024. To understand the impact of AI, it is important to draw lessons from it's past successes and failures and this white paper provides a comprehensive explanation of the evolution of AI, its current status and future directions.

As with most people, coffee is one of the most important rituals in my morning routine. There's something about the aroma and taste that kick-starts my ability to have a great day. So imagine my surprise when a favorite coffee shop was closed before I had to jump on an early-morning flight. The employees were in the shop, but the gate locked out coffee aficionados, like me, who really needed that jolt of caffeine. Although this experience was understandably a letdown, it was also a source of inspiration.

The dark web is a difficult place to police. Comprising millions of websites shrouded in anonymity, it is an online playground for dangerous criminals and political plotters - anyone trying to evade authority or do the wrong thing. But one Australian Federal Police officer hopes to develop an artificially intelligent (AI) "crawler" that could scan the dark web for illegal activity and alert authorities to anything suspicious. If successful, the AI crawler would - for example - make it easier and faster to track down paedophiles, a task that at present is time-consuming and requires investigators to look at thousands of confronting images and material. The officer, Janis Dalins, has been developing the crawler as part of his PhD.

Ongoing developments in neural network models are continually advancing the state-of-the-art in terms of system accuracy. However, the predicted labels should not be regarded as the only core output; also important is a well calibrated estimate of the prediction uncertainty. Such estimates and their calibration is critical in relation to robust handling of out of distribution events not observed in training data. Despite their obvious aforementioned advantage in relation to accuracy, contemporary neural networks can, generally, be regarded as poorly calibrated and as such do not produce reliable output probability estimates. Further, while post-processing calibration solutions can be found in the relevant literature, these tend to be for systems performing classification. In this regard, we herein present a method for acquiring calibrated predictions intervals for neural network regressors by posing the regression task as a multi-class classification problem and applying one of three proposed calibration methods on the classifiers' output. Testing our method on two exemplar tasks - speaker age prediction and signal-to-noise ratio estimation - indicates both the suitability of the classification-based regression models and that post-processing by our proposed empirical calibration or temperature scaling methods yields well calibrated prediction intervals. The code for computing calibrated predicted intervals is publicly available.