Collaborating Authors


One in 1,000 years? Old flood probabilities no longer hold water


Australia's catastrophic east coast floods have been described by the NSW premier as a "one in 1,000-year event, a term that has created a great deal of confusion. Lengthy explanations that these terms are not the same as "occurring 1,000 years apart" or "once every 1,000 years" have only added to the confusion. The simplest explanation is that the actual meaning of "one in 1,000 years" is "having a probability of 0.1 percent in any given year" (1 in 1,000), which raises the question: why don't people simply say that? The main reason is that these terms date back to a time when most people didn't think in terms of probabilities, and even those who did were confused about how they worked. The daily weather forecast includes a percentage probability of rain, and longer-term forecasts give the probabilities of higher or lower than average rainfall according to El Nino and La Nina cycles.

Endeavour Energy showcases 5G drones for electricity grid repair


Endeavour Energy, together with Optus, Amazon Web Services, and Unleash live, has deployed its first 5G and AI-enabled drones to improve restoration times for unplanned electricity outages, particularly during natural disasters such as storms, floods, and bushfires. As part of the first demonstration, Endeavour Energy flew the drones over physical electricity infrastructure located in Sydney's western suburb of St Marys. During the flyover, footage of damaged assets was streamed in real-time using 5G to Endeavour Energy's training ground in Hoxton Park. With the demonstration a success, according to Optus, Endeavour Energy will now deploy the solution across infrastructure assets in Penrith and Blacktown, which would remove the need to use a large fleet of vehicles, helicopters, and technicians to physically identify and carry out remediation. "We're thrilled to work with Optus, AWS, and Unleash live, with the support of the Australian government to expedite the use of 5G drone technology to make faster decisions and expedite critical maintenance to continue to keep the lights on for our customers," Endeavour Energy chief asset and operating officer Scott Ryan said.

Artificial Intelligence Work Group Project Australia


The Final Report also makes specific recommendations for the introduction of legislation which regulates the use of facial recognition and other biometric technology, and for a moratorium on the use of this technology in AI-informed decision-making until such legislation is enacted. The recommendations of the AHRC have been submitted to the Australian Government. The Australian Government has the ability to determine whether to adopt the recommendations of the Report or not. The adoption of the AHRC's recommendations for the introduction of specific legislation governing the use of AI would signal a change in the approach to the regulation of AI and other emerging technologies that has been adopted in Australia to date. Free data access is an issue in the use of AI tools in the provision of legal services in Australia. The success of an AI tool will be determined by the size and diversity of the sample data which is used to train that tool. There are a number of factors that contribute to free data access in Australia and generally these factors apply across the spectrum of different categories of AI tools discussed in question 2 (being litigation, transactional and knowledge management tools).

NSW government AI projects face ethics assessment under new assurance framework


All New South Wales government agencies using AI will be required to meet best practice ethical requirements under the country's first mandated AI Assurance Framework, which comes into effect today. The framework, which was developed by the NSW Advisory Committee led by NSW chief data scientist Ian Oppermann, has been designed to ensure AI-based government projects are safe, ethical, and can be integrated with future technologies. It also assists agencies with risk mitigation strategies and establish clear governance and accountability measures. "From diagnosing sepsis in hospital patients to identifying drivers illegally using mobile phones while driving, the NSW government is already using AI to improve the lives of NSW residents," Oppermann said. "As the technology evolves and becomes more sophisticated, the Framework will ensure projects remain transparent and include the highest levels of privacy, security and assurance, so customers can feel even more confident when dealing with the NSW government. "Mandating the framework will ensure all NSW government services using AI are required to implement strong privacy and data management safeguards." Projects with budgets of more than AU$5 million or supported by the Digital Restart Fund will also be subject to assessment by the AI Review Committee to ensure compliance under the new mandate. The state government added the only exceptions where the AI Assurance Framework will not apply is when a project uses an AI system that is a widely available commercial application, and the solution is not being customised in any way or being used other than intended. The framework is part of the state government's AI strategy in which it has pledged that transparency will be the focus and vowed to make the state the digital capital of the southern hemisphere in the next three years. "AI stands for absolutely imperative for the new New South Wales.

NSW to gain two autonomous tunnelling machines for Sydney Metro West project


Autonomous tunnel boring machines (TBM) will be used to help build two nine-kilometre rail tunnels as part of the 24-kilometre Sydney Metro West project, the New South Wales government announced on Wednesday. The autonomous machines are being built as part of an AU$2.16 billion Western Tunnelling Package that was awarded to the Gamuda Australia and Laing O'Rourke consortium, which has contracted manufacturer Herrenknecht to design, build, and deliver the machines. According to the state government, the machines will feature artificial intelligence software, developed by Gamuda, that will be used to automatically steer, operate, and monitor various TBM functions. "While an operator remains in control, the autonomous system takes on all repetitive tasks from the operator with greater accuracy," Minister for Transport David Elliot said, claiming that the use of autonomous TBMs will be an Australian first. "The technology also allows the TBMs to be more accurate and precise, reducing the time required to excavate the nine-kilometre tunnels, therefore saving project costs."

Q-CTRL touts error-correction methods boost quantum algorithm success by 1000 times


Australian quantum startup Q-CTRL claims it has increased the likelihood of quantum computing algorithm success on hardware by over 1000 times, after it carried out its latest hardware benchmarking experiments demonstrating its autonomous error-correction techniques. According to the company, most quantum computers are currently error prone meaning that only the shortest and simplest algorithms can run, inhibiting on quantum computational capabilities being delivered to end users. However, through its research activities, the company said it has identified methods using AI and automation to reduce the number of errors. At the same time, the research was completed using conventional cloud access to commercial quantum computers and did not require any special hardware access. "Our benchmarking experiments demonstrate that there's hidden performance inside today's quantum computers that can become available with the right error-correcting software tools -- no changes to hardware are needed," Q-CTRL founder and CEO Professor Michael J. Biercuk.

Artificial Intelligence can now be an Inventor: Where to from Here?


On 30 July 2021, the Federal Court of Australia decided that AI systems can be inventors. In a word-first determination of Thaler v Commissioner of Patents,{[2021] FCA 879, ('Thaler')}, the Honourable Justice Beach found that AI systems can be the inventors on a patent application under Australian patent law. The decision has been appealed to the Full Bench of the Federal Court, which may decide to overrule it. For now, however, the decision is binding in Australia. Read on to find out what a patent is and an overview of the decision.

Queensland government expands police air fleet with new drone trials


The Queensland government has announced that it will invest nearly a million dollars to deliver drones for use in Townsville and Cairns. The government of the Sunshine State detailed that the drones will initially be trialled in each town for 12 months. The remotely piloted aircrafts will be integrated with an aerial platform featuring intelligence, surveillance, and reconnaissance capabilities, plus AI technologies for tracking vehicles and thermal imaging cameras to locate lost people. Police on the ground will then be able to receive a live feed of images being recorded by the aerial platform. The drones form part of an expansion of the Queensland Police's air fleet, which will also see the introduction of new helicopters and fixed wing aircraft.

Australian Space Agency believes existing robotics can be used for terrestrial activities


The Australian Space Agency (ASA) has highlighted that over the next decade Australia has an opportunity to prioritise six areas when it comes to robotics and automation on earth and in space. In publishing its Robotics and Automation on Earth and in Space Roadmap [PDF] on Monday, the ASA detailed that these six areas are remote operations, interoperability, analogue facilities and services, robotic platforms, in-situ resource utilisation services (ISRU), and terrestrial foundation services, such as materials handling and transport. Specifically, the roadmap describes how Australia could leverage its existing expertise in robotics technology and systems, provide solutions in the global marketplace to support the sustainable build-up of space assets and infrastructure, and enable international collaboration with industry. "Robots go places and do things that humans can't, and advances in automation allow us to carry out complex tasks with more sophistication. These technologies are playing an ever-increasing role in supporting human operations on the moon," ASA CTO Nick Larcombe said.

Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes? Artificial Intelligence

Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that take an individual's health information (e.g. their drug history and comorbidities) as inputs, and predict the probability that the individual will have an Acute Coronary Syndrome (ACS) adverse outcome. Using XAI, we quantified the contribution that specific drugs had on these ACS predictions, thus creating an XAI-based technique for pharmacovigilance monitoring, using ACS as an example of the adverse outcome to detect. Individuals aged over 65 who were supplied Musculo-skeletal system (anatomical therapeutic chemical (ATC) class M) or Cardiovascular system (ATC class C) drugs between 1993 and 2009 were identified, and their drug histories, comorbidities, and other key features were extracted from linked Western Australian datasets. Multiple ML models were trained to predict if these individuals would have an ACS related adverse outcome (i.e., death or hospitalisation with a discharge diagnosis of ACS), and a variety of ML and XAI techniques were used to calculate which features -- specifically which drugs -- led to these predictions. The drug dispensing features for rofecoxib and celecoxib were found to have a greater than zero contribution to ACS related adverse outcome predictions (on average), and it was found that ACS related adverse outcomes can be predicted with 72% accuracy. Furthermore, the XAI libraries LIME and SHAP were found to successfully identify both important and unimportant features, with SHAP slightly outperforming LIME. ML models trained on linked administrative health datasets in tandem with XAI algorithms can successfully quantify feature importance, and with further development, could potentially be used as pharmacovigilance monitoring techniques.