Researchers from the Queensland University of Technology (QUT) have identified how using AI-enabled infrared drones can help provide more accurate estimates about the number of surviving koalas in bushfire affected areas. The new modelling method has been published in the journal Ecology and Evolution and builds on previous research that was published in the natural journal Scientific Reports. At the time, the research showed that using drones and infrared imaging was more reliable and less invasive than traditional animal population monitoring techniques, such as having people look up at trees or bringing dogs in to sniff out koalas. Speaking to ZDNet, research lead Grant Hamilton explained how this latest method takes into account that there are certain challenges faced when trying to detect koalas in dense bushland. "When you talk about detections, we're talking about finding out everything you can spot in an area, but we may not be able to see things and that's partly because we're looking in the canopy; maybe [the koala] is under a branch, or sometimes we spot something twice because the drone flies to and fro," he said.
Applied mathematician Tobin South, the Technical Lead on Clearer Consent, said the goal would be to modify existing chatbot interfaces and off-the-shelf question-answering software to become something fit for purpose on a hospital front line. "A big challenge in healthcare is informed consent, the doctor gives you all these forms, says tick the boxes, don't drive for the next couple of days, but many people don't really understand what's going on," the University of South Australia masters student said. "Lots of people have language barriers to using these forms and there's also cultural considerations that you can't cram onto a one-page form. "The solution involving AI was to develop a way to ask questions in the patient's native language, and have them be able to type their own questions or concerns into the program and have it understand them and give meaningful answers back."
Artificial intelligence (AI) improved skin cancer diagnostic accuracy when used in collaboration with human clinical checks, an international study including University of Queensland researchers has found. The global team tested for the first time whether a'real world', collaborative approach involving clinicians assisted by AI improved the accuracy of skin cancer clinical decision making. UQ's Professor Monika Janda said the highest diagnostic accuracy was achieved when crowd wisdom and AI predictions were combined, suggesting human-AI and crowd-AI collaborations were preferable to individual experts or AI alone "This is important because AI decision support has slowly started to infiltrate healthcare settings, and yet few studies have tested its performance in real world settings or how clinicians interact with it," Professor Janda said. "Inexperienced evaluators gained the highest benefit from AI decision support and expert evaluators confident in skin cancer diagnosis achieved modest or no benefit. "These findings indicated a combined AI-human approach to skin cancer diagnosis may be the most relevant for clinicians in the future." Although AI diagnostic software has demonstrated expert level accuracy in several image-based medical studies, researchers have remained unclear on whether its use improved clinical practice. "Our study found that good quality AI support was useful to clinicians but needed to be simple, concrete, and in accordance with a given task," Professor Janda said. "For clinicians of the future this means that AI-based screening and diagnosis might soon be available to support them on a daily basis.
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) and Queensland-based agtech company Goanna Ag have announced a partnership that will see sensors and analytics be used to help growers better understand how to maximise the use of irrigation water to grow crops. Under the partnership, Goanna Ag will incorporate WaterWise, a CSIRO-developed technology, into its existing GoField irrigation management system. The WaterWise system features in-field sensors that measure the canopy temperature of crops every 15 minutes. The data collected from the sensors are then combined with weather forecasts before machine learning is applied to help predict the crop's water requirements for the next seven days. "Being able to predict when to irrigate will allow our clients -- farmers -- to plan based on what the plant needs," Goanna Ag CEO Alicia Garden said. Waterwise team leader Rose Brodrick added growers will be able to predict future water needs of their crops.
Australia's Department of Defence and Gilmour Space Technologies have signed a deal to build a three-stage rocket to launch small payloads and satellites into orbit by 2022. Under a new strategic agreement, the partnership will see Defence Science and Technology (DST) and the Queensland-based company research and develop defence-related technologies including propulsion, materials, and avionics technologies, to help develop the rocket for launch. "The ultimate goal in the first deal is to help us get to space," Gilmour Space Technologies CEO and founder Adam Gilmour told ZDNet. He revealed how the pair have been in "close discussions" about the agreement for a number of years. "Many of [Defence's personnel have] come to visit our factory, we've gone to visit their premises, we've discussed a lot about technology," Gilmour said.
Robotics, machine learning, data science, and mathematical modelling are just some of the tools that a group of researchers will use to forecast environmental changes across Antarctica as part of a seven-year research project. To be led by Monash University, the Securing Antarctica's Environmental Future (SAEF) project will involve 30 Australian and overseas organisations, including Queensland University of Technology (QUT), University of Wollongong, University of New South Wales, James Cook University, University of Adelaide, the South Australian Museum, and the Western Australian Museum. According to QUT Institute for Future Environments executive director Kerrie Wilson, who will form part of the program's leadership team, the research aims to "bring new perspectives to Antarctic conservation". "Antarctica is facing unprecedented threats from climate change, fishing, visitation, and other human activities. Safeguarding its future will require new ideas, and collaborations between different fields of science," she told ZDNet.
This is a group for anyone interested in Artificial Intelligence (AI). All skill levels are welcome. The main focus of this group is to help people understand AI scenarios through real, hands-on technical solutions and explore how to develop AI solutions in today's world. We will look at a variety of technology from different providers. Guest speakers will include people working in the industry to demonstrate new technology.
On 9th April 2020, Queensland AI hosted a special online panel, exploring a very topical question, "Can AI help in the fight against COVID-19?" During the hour-long webinar discussion, Nicholas Therkelsen-Terry, CEO of Max Kelsen and Head of Queensland AI, hosted a multi-disciplinary panel of medical and AI experts directly involved in the COVID-19 response. Over the hour, the panellists discussed the role of artificial intelligence and data science in the fight against COVID-19. The panellists brought a range of perspectives and a wealth of experience to the discussion, creating a balanced conversation that considered both the clinical and technical sides of the equation. A shift away from classic statistical analysis using P-value confidence indicators and a movement towards a more precautionary, cost-benefit analysis approach for guiding health policy is key to gaining control of the pandemic.
Does AI have the power to control the spread of infection of COVID-19, discover cures and vaccines, and aid in the treatment of the critically ill? Or should AI practitioners step back and let the epidemiologists, clinicians, and microbiologists manage the response? Can we trust AI to guide decision making? Do we have access to the data we need and how can we share it whilst balancing patient privacy? We have assembled a world class panel of AI and medical experts to tackle our biggest crisis.