Logan Marshall-Green stars in the upcoming ultra-violent sci-fi/horror/action film, Upgrade, directed by Australia's own Leigh Whannell. The film follows Marshall-Green's quadriplegic character Grey, as he's upgraded with a new form of artificial intelligence chip that restores all of his former functions – as well as turning him into a killing machine. He then uses his newfound gifts to seek revenge on the men that killed his wife. Here is another science-fiction premise that may have some actual future plausibility, with a mix of gory violence to spice up the entertainment factor. Judging by this trailer, Whannell looks to have hit the mark. While I think the red band trailer below may be revealing too much awesome action already, it does do plenty to get action/sci-fi/horror fans excited.

Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally, it provides an analytical method to update the decoding matrix which maps the labels into the original space and is used during the test phase. Experimental results show the effectiveness of this approach in terms of running times and the prediction performance over different measures. Keywords: data stream classification, multi-label data, label compression 1. Introduction Standard classification is the task of assigning the correct class to previously unknown test instances based on training instances. Training data consist of a set of features and an associated target class or class label. Many modern data mining applications, however, need to deal with more than one label per instance.

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.