Scientific studies do not find any links between video games and gun violence. The claim that they do has been repeatedly tested, studied and debunked. Yet on Monday, US president Donald Trump insisted that "gruesome and grisly video games" were causative in the gun massacre deaths of 22 people in El Paso and another 9 in Dayton (not Toledo) Ohio. Why scapegoat video games and demonise the people who play them? It's established that science, expertise, evidence and the truth are not dominant themes of the Trump presidency, and with increasing numbers of people bleeding to death in US streets, he has to find someone – something – anything!

The recent proliferation of publicly available graph-structured data has sparked an interest in machine learning algorithms for graph data. Since most traditional machine learning algorithms assume data to be tabular, embedding algorithms for mapping graph data to real-valued vector spaces has become an active area of research. Existing graph embedding approaches are based purely on structural information and ignore any semantic information from the underlying domain. In this paper, we demonstrate that semantic information can play a useful role in computing graph embeddings. Specifically, we present a framework for devising embedding strategies aware of domain-specific interpretations of graph nodes and edges, and use knowledge of downstream machine learning tasks to identify relevant graph substructures. Using two real-life domains, we show that our framework yields embeddings that are simple to implement and yet achieve equal or greater accuracy in machine learning tasks compared to domain independent approaches.

Variational Bayes (VB) has become a versatile tool for Bayesian inference in statistics. Nonetheless, the development of the existing VB algorithms is so far generally restricted to the case where the variational parameter space is Euclidean, which hinders the potential broad application of VB methods. This paper extends the scope of VB to the case where the variational parameter space is a Riemannian manifold. We develop, for the first time in the literature, an efficient manifold-based VB algorithm that exploits both the geometric structure of the constraint parameter space and the information geometry of the manifold of VB approximating probability distributions. Our algorithm is provably convergent and achieves a convergence rate of order $\mathcal O(1/\sqrt{T})$ and $\mathcal O(1/T^{2-2\epsilon})$ for a non-convex evidence lower bound function and a strongly retraction-convex evidence lower bound function, respectively. We develop in particular two manifold VB algorithms, Manifold Gaussian VB and Manifold Neural Net VB, and demonstrate through numerical experiments that the proposed algorithms are stable, less sensitive to initialization and compares favourably to existing VB methods.

A PhD student in Australia has developed an automated system to detect, in real time, mysterious radio pulses emanating from the deep universe. The fleeting signals known as fast radio bursts (FRBs) have baffled scientists since they were first discovered in 2007 by a team poring through archival data. Since then, there have been numerous other instances of their detection – though what exactly causes them remains a mystery. The latest breakthrough could be a huge leap forward for scientists' ability to understand the nature of fast radio bursts, allowing them to be captured in detail the moment they reach Earth. A PhD student in Australia has developed an automated system to detect, in real-time, mysterious radio pulses emanating from the deep universe.

Harry Butler Institute, Murdoch University, are helping to protect Australia's biosecurity. In a first of its kind application in Western Australia, AI technology in the field - using IBM Power-9 based hardware and PowerAI Vision technology - is providing scientists with real-time profile of biosecurity threats within seconds, helping them to identify invasive species, even when distinguishing features may not be visible to the human eye.

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods.

AI has the power to boost the economy, improve environmental sustainability and create a more equitable society -- but there are dangers associated with its rise, the panel of experts has told. The report was developed to give Australians a reference point to understand AI, and what living in a future dominated by the technology will really mean. AI refers to a collection of technologies which give machines the ability to perform tasks and solve problems that would otherwise require the human brain to carry out. While the U.S. and China are undoubtedly leaders in AI technology, Australia is punching well above its weight in terms of establishing systems for mining, agriculture, and manufacturing. Australia is also the five-time winner of the world robot soccer competition, the Robocup.

Stunning footage has revealed the first evidence that leopard seals share food -- with the marine mammals caught divvying up a penguin as they feast on their kill. The ground-breaking footage was captured by a drone flying off the coast of the island of South Georgia, in the southern Atlantic. Researchers said that leopard seals are normally regarded as being solitary creatures. The Antarctic predators are largely'intolerant' of each other, but can be forced to hunt alongside each another when congregating in areas of plentiful prey, the experts added. The leopard seal is named for its black-spotted coat, whose pattern is similar to that of the big cat, though the seal's coat is grey rather than golden in colour.

Kespry, a drone-based aerial intelligence solution provider, and DroneBase, a drone services company, have announced a partnership to enable insurance, mining, and aggregates enterprises across North America to expand aerial analytics implementation across their worksites. Kespry's customers will now be able to leverage DroneBase to manage their Kespry deployments as part of Kespry's new Bring Your Own Drone (BYOD) program. BYOD includes a new platform pricing model designed to meet the expanding enterprise aerial intelligence requirements of multi-site mining and aggregates companies, as well as large-scale residential and commercial property insurers. The combination of Kespry and DroneBase brings the best of the platform and services worlds together, offering a cost-effective, productive way of using drone-based analytics across the largest insurance, mining, and aggregates businesses. "We're very pleased to work with DroneBase and its team of dedicated, aerial intelligence professionals to further expand Kespry insurance, mining and aggregates deployments across the country," said George Mathew, CEO, Kespry.

A company's most valuable asset is its human capital. In fact, people skills were ranked as the third most important force that will affect enterprises in the next two years by more than 12,800 C-level executives who participated in the most recent IBM Global C-suite Study. In addition, only half of the 2,100 CHRO participants said they currently have the people skills and resources to execute their business strategies. As Gina Dellabarca, General Manager of Human Resources for Westpac New Zealand, says in the C-suite Study report: "Our most important priority in HR is finding talent for the future, not just for now. We're focused on the formidable challenge of attracting, developing, and retaining employees with skills we haven't yet determined."