An amphibious supercar design that would allow its mega rich owners to look stylish and beat city traffic by travelling through water at high speed has been unveiled. According to its creators, the electric powered Amphi-X would reach speeds of more than 260 mph (418 km/h) on land and an average speed of over 90 mph (145 km/h) in water, the same as a modern speed boat. The car has been designed for the world's super rich to avoid congested roads of big cities, but comes with a hefty £2.5 million price tag. An amphibious supercar design that would allow its mega rich owners to look stylish and beat city traffic by travelling through water at high speed has been unveiled. The car has been designed for the world's super rich to avoid congested roads of big cities, but comes with a hefty £2.5 million price tag.

This AI will tell people when theyre likely to die -- and thats a good thing. Thats because scientists from the University of Adelaide in Australia have used deep learning technology to analyze the computerized tomography (CT) scans of patient organs, in what could one day serve as an early warning system to catch heart disease, cancer, and other diseases early so that intervention can take place. Using a dataset of historical CT scans, and excluding other predictive factors like age, the system developed by the team was able to predict whether patients would die within five years around 70 percent of the time. The work was described in an article published in the journal Scientific Reports. The goal of the research isn't really to predict death, but to produce a more accurate measurement of health, Dr. Luke Oakden-Rayner, a researcher on the project, told Digital Trends.

When you're lost, Siri can be your best friend. But if she can't retrieve the right address from your contacts, she can drive you crazy. And so it is with the legion of virtual personal assistants that are entering our lives. From Amazon's Alexa to Google's Home, people are busy talking to intelligent machines as never before. It's estimated that more than 60 percent of internet traffic is now generated by machine-to-machine, and person-to-machine, communication.

As I look out at 2019, there's certainly plenty to be optimistic about – revolutionary new technologies are fueling the 4th Industrial Revolution and positively impacting how we work, live, connect and play. It's exciting to see technologies such as Artificial Intelligence (AI), Mixed Reality (MR) and the Internet of Things (IoT) becoming key drivers for digital transformation and making a positive and lasting impact on our society and environment. In Australia, for example, the Department of Primary Industry and Resources for the Northern Territory Government is using AI and IoT to identify and analyze hundreds of precious fish species in one of Australia's largest harbors. This massive undertaking will ensure fisheries' resources are sustainably-managed, protected and developed for future generations. Although there is good reason to be generally optimistic about economic and societal prospects for 2019, some business leaders have also confided in me that they see significant challenges ahead.

ABB's future of mining infographic shows how to drive profits World's largest flotation cells improve copper and molybdenum recovery in Mexico PRESS RELEASE: The solution will be released in early 2019 as part of MICROMINE's fleet management and mine control solution, Pitram. Using the processes of computer vision and deep machine learning, on-board cameras are placed on loaders to track variables such as loading time, hauling time, dumping time and travelling empty time. The video feed is processed on the Pitram vehicle computer edge device, the extracted information is then transferred to Pitram servers for processing and analyses. ABB's future of mining infographic shows how to drive profits World's largest flotation cells improve copper and molybdenum recovery in Mexico MICROMINE Chief Technology Officer Ivan Zelina explained the solution intelligently considered the information gathered to pinpoint areas of potential improvement that could bolster machinery efficiency and safety. "Pitram's new offering takes loading and haulage automation in underground mines to a new level," Mr Zelina said.

Azure Data Box Disk, an SSD-based solution for offline data transfer to Azure, is now generally available in the US, EU, Canada, and Australia, with more country/regions to be added over time. Each disk is an 8 TB SSD that can copy data up to USB 3.1 speeds and support the SATA II and III interfaces. The disks are encrypted using 128-bit AES encryption and can be locked with your custom passkeys. When this feature is enabled, you will be able to copy data to Blob Storage on Data Box using blob service REST APIs. The following Azure IoT Hub Device Provisioning Service features are now generally available: Symmetric key attestation support; Re-provisioning support; Enrollment-level allocation rules; and Custom allocation logic.

Considering the fact that present day ball speeds exceed 130mph, the time required by the receiver to make a decision regarding the opponents' intention, and initiate a response could exceed the flight time for the ball [1], [2], [3], [4]. Several studies have shown that this reactive ability is the product of pattern recognition skills that are obtained through a "biological probabilistic engine", that derives theories regardingopponents intentions with the partial information available[1], [5], [6]. For instance, it has been shown that expert tennis players are better at detecting events in advance [1], [7] and posses better knowledge/ expertise of situational probabilities [3]. Further investigation of human neurological structures have revealed that those capabilities occur due to a bottom-up computational process [1] within the human brain, from sensory memory to the experiences stored in episodic memory [8], [9] and knowledge derived in semantic memory [9], [10]. Despite the growing interest among researchers in the machine learning domain in better understanding factors influencing decision making in fastball sports, there have been very few studies transferring the observations of the underlying neural mechanisms to neural modelling in machine learning.Current state-of-the-art methodologies try to capture the underlying semantics through a handful of handcrafted features, without paying attention to essential mechanisms in the human brain, where the expertise and observations are stored and knowledge is derived.

Consumer faith in businesses in most markets worldwide is wallowing in "stagnant distrust" – according to the Edelman Trust Barometer – and Australia is no exception. The recent Banking Royal Commission exposed some shocking misdemeanors across the financial services sector, Facebook appears to be trapped in an endless cycle of data misuse scandals, while breaches of customer data held by Australia's biggest businesses are now depressingly frequent. According to a recent CA Technologies commissioned report by analyst firm Frost & Sullivan, Australian consumers had the lowest overall level of'digital trust' – their confidence in brands to appropriately collect, store and use their digital information – in the world. Across nearly all sectors consumer trust is low and in decline, ranking only slightly higher than citizens' trust in politicians and the media. At first appraisal, an organisation's use of artificial intelligence related technologies would appear to put those pitifully low trust stocks at risk.

Technological development has become a rat race. In the competition to lead the emerging technology race and the futuristic warfare battleground, artificial intelligence (AI) is rapidly becoming the center of global power play. As seen across many nations, the development in autonomous weapons system (AWS) is progressing rapidly, and this increase in the weaponization of artificial intelligence seems to have become a highly destabilizing development. It brings complex security challenges for not only each nation's decision makers but also for the future of the humanity. The reality today is that artificial intelligence is leading us toward a new algorithmic warfare battlefield that has no boundaries or borders, may or may not have humans involved, and will be impossible to understand and perhaps control across the human ecosystem in cyberspace, geospace and space (CGS).

Researchers at the Queensland University of Technology have developed an algorithm that uses a pair of machine learning techniques to quickly and accurately predict aircraft trajectories and flight paths. The algorithm was designed to increase the understanding of how airspace is used from a defence perspective, but could also be applied to civilian air traffic control or any scenario in which movement needs to be analysed. Data is fed into two neural networks - deep neural networks, which analyse data at multiple levels to predict the probability of outcomes with increasing accuracy; and memory networks, which feature a memory component that can be read from and written to. "In essence, it's built to measure a trajectory in and predict a trajectory out," professor Clinton Fookes from the university's Vision and Signal Processing discipline said. "But as it's taking in the trajectory of the target object, it's also taking in the trajectories of neighbouring objects to create an awareness of what's around the target and how those objects are moving."