Australians like the idea of driverless cars, but first they want to take a look under the hood. Preliminary results from a national study undertaken by the Australian Driverless Vehicle Initiative (ADVI) suggest Australians are fairly keen to give up the steering wheel. The survey of more than 5,000 Australians over the age of 18 found that a majority liked the idea of using a driverless vehicle when they were tired, when driving was "boring or monotonous," or if they had consumed alcohol or drugs. Despite the enthusiasm, Michael Regan, chief scientist at the Australian Road Research Board, who oversaw the survey, told Mashable that respondents had reservations ranging from security to financial liability. "The big thing people are concerned about is vehicle security," he said.

Seven in 10 Australians trust autonomous vehicles to take over when they feel tired, bored, or physically and mentally incapable of driving manually, according to a study by the Australian Driverless Vehicle Initiative (ADVI). More than 5,000 Australians aged 18 and over were surveyed by ADVI and its academic partners, including the University of New South Wales, through an 80-question survey designed to help guide research, marketing, and vehicle design efforts. According to ADVI's preliminary findings, 69 percent of survey respondents said they would rather a driverless car take the lead when driving was "boring or monotonous", and 60 percent said they would prefer an autonomous vehicle during traffic congestion. Participants said the most likely activity they would spend their time doing in driverless cars was observing scenery at 78 percent, followed by interacting with passengers on 76 percent, resting came in at 52 percent, and doing work-related activities polled at 36 percent. Almost half, 47 percent, of Australians surveyed felt self-driving vehicles would be safer than human drivers.

BRISBANE, Australia – A New Zealand tourist was so afraid of an Australian man she met through the dating app Tinder that she fell 14 floors to her death while trying to escape from his apartment balcony, a prosecutor told a court on Monday. A woman was so terrified of her Tinder date'she fell 14 floors to her death in bid to escape him' https://t.co/E6KTEeSwdG Gable Tostee, 30, pleaded not guilty in the Queensland state Supreme Court in Brisbane to the murder of 26-year-old Warriena Wright in Gold Coast city in the early hours of Aug. 8, 2014. Tostee and the Lower Hutt woman met for the first time in the tourist center of Surfers Paradise on the night she died. Prosecutor Glen Cash told the jury that Tostee did not throw Wright to her death, but intimidated and threatened her to an extent that she felt the only way to escape was to climb down from his balcony.

Machine learning is kind of magic right? But is it the kind of magic that can make us rich? And I don't mean lucrative consulting gig rich, I mean digging valuable metals out of the ground rich. Also I'd been meaning to try out some transfer learning and looking around for a good topic to try it on. Transfer learning is where you take a pre-trained convolution (or other) network and use it for your task.

Self-driving cars sporting Tesla shells as their exterior and featuring technology from German manufacturer Bosch were to be tested on Australia's public roads beginning this week. An autonomous automobile developed by German manufacturer Bosch in collaboration with the Australian government were to be tested on Australia's public roads this week. The cars, which employ video cameras, radar, LiDAR, and global-positioning systems to sense their environment, reportedly are designed to navigate roads with or without driver input. University of Southern California professor Jeffrey Miller suggests the six LiDAR sensors in the vehicle indicate Bosch depends more on LiDAR than on cameras for situational awareness. Carnegie Mellon University's John Dolan says the sensors are "fairly standard for research-level autonomous cars," although he notes the stereo cameras are less common.

Daniel Lélis Baggio started his work in computer vision through medical image processing at InCor (Instituto do Coração Heart Institute) in São Paulo, where he worked with intra-vascular ultrasound image segmentation. Since then, he has focused on GPGPU and ported the segmentation algorithm to work with NVIDIA's CUDA. He has also dived into six degrees of freedom head tracking with a natural user interface group through a project called ehci (http://code.google.com/p/ehci/). He now works for the Brazilian Air Force. Shervin Emami (born in Iran) taught himself electronics and hobby robotics during his early teens in Australia.

Samsung Australia has partnered with Deakin University and independent research group Unisono to develop Internet of Things (IoT)-based smart home technology and software in a bid to overcome healthcare challenges. The partnership will see the trial of three smart home technology-focused projects designed to guide Australian research and development programs that will explore how IoT technology and applied learning-based systems of artificial intelligence can support assisted living programs in healthcare. Ian Aitken, CTO and head of New Business Development at Samsung Australia, told ZDNet he believes IoT and the concept of a smart home has not progressed much further than sensors opening a garage or switching lights on or off. He said the partnership with Deakin and Unisono will instead focus on improving things for people at home and will also aim to speed up the availability and deployment of such technology for people who need it. "We really want to put the smarts into smart homes. We see the traditional way of doing it -- just turning things on and off -- not quite there," he said.

The folk at Orion Health have been busy following the announcement of its intention to put machine learning to work in its products within a year. It has just released a report, in conjunction with the University of Auckland, which makes clearer how that will work. The Introduction to Machine Learning in Healthcare report is co-authored by Dr Kevin Ross and Dr Kathryn Hemstalk, with an introduction by Orion Health CEO Ian McCrae. Among other things, the report lifts the lid on data that is useful for the practice of precision medicine, and which will become part of an electronic health record and provides an introduction to different types of machine learning models. The report's publication coincides with the official launch of Precision Driven Health at the University of Auckland, which the company described in a statement as'one of the largest data science research initiatives to be undertaken in this country'.

Whether they practice medicine in a hospital or a community clinic, healthcare workers are facing an exponential increase in the amount of patient information needed to effectively treat their patients. This will require the application of sophisticated'big data' techniques such as machine learning to process, analyse and surface information that will assist in creating more personalised healthcare plans. That's according to Orion Health CEO Ian McCrae, whose company has today released a report on the application of machine learning in healthcare. The report's publication coincides with the official launch at the University of Auckland of Precision Driven Health - one of the largest data science research initiatives to be undertaken in this country, which aims to position New Zealand at the forefront of precision health globally. "The electronic health record is fast becoming the most powerful tool in the medical toolkit. Today it contains a patient's medical record, soon it will include genetic, environmental and social data and will be critical in the application of precision medicine or personalised healthcare," Mr McCrae says.

In much the same way children have to be told not only what they are saying is wrong, but why it is wrong, artificial intelligence (AI) systems need to be able to receive and act on similar feedback, according to Monash University Department of Electrical and Computer Systems Engineering Head Professor Tom Drummond. "I think fundamentally we need ways of giving machine learning systems more feedback than'yes' or'no'," Drummond told journalists at the GTCx Australia conference on Tuesday. "Rich feedback is important in human education, I think probably we're going to see the rise of machine teaching as an important field -- how do we design systems so that they can take rich feedback and we can have a dialogue about what the system has learnt?" To illustrate his point, Drummond used the example of a system that was able recognise and caption images of fire hydrants, and appeared to be working well until one of the images was modified. "You take Photoshop, and you colour it green, and it says: 'A red fire hydrant'."