Anthropic's plan to build a datacentre in Queensland was announced by premier David Crisafulli on Wednesday. Anthropic's plan to build a datacentre in Queensland was announced by premier David Crisafulli on Wednesday. Premier describes deal as'major win' that will deliver more jobs for the state and put more energy into its grid Anthropic, the AI giant behind the large language model Claude, has done a deal to lease the site of its first Australian datacentre in western Queensland . The state premier, David Crisafulli, announced the decision in parliament on Wednesday. The proposed Western Downs digital park is to be built by Singapore-based Zerra DC near Dalby, north-west of Toowoomba, and will connect to the Braemar power station.

The team embedded electrodes into live cockroaches, then outfitted them with cameras and injection devices to deliver medication via remote control. Small robots and drones have increasingly been used to help respond to disasters, reaching locations that are too hard or too dangerous to send humans, such as collapsed buildings . Many of these efforts have been focused on search and rescue. But new research shows how bugs--with their ability to crawl through even narrower gaps in rubbled-- outfitted with electrodes could be used as the next generation of first responders. A research team from the University of Queensland (UQ) and the University of New South Wales (UNSW) have developed the "Paraborg" to not only help with searching for victims, but also administer first aid.

Police forces around the world are adding AI-powered robots. In a groundbreaking fusion of nature and technology, researchers at the University of Queensland have developed remote-controlled beetles equipped with tiny, removable backpacks that could drastically reduce the time it takes to locate survivors in disaster zones. Also known as cyborg beetles, these hybrid helpers are part of an ambitious project to improve emergency response in situations like building collapses, earthquakes or industrial explosions. By combining natural mobility with simple controls, researchers are developing a faster, more flexible way to reach people in hard-to-access areas. A close-up of a cyborg beetle with mounted electronics.

Lung magnetic resonance imaging (MRI) with ultrashort echo-time (UTE) represents a recent breakthrough in lung structure imaging, providing image resolution and quality comparable to computed tomography (CT). Due to the absence of ionising radiation, MRI is often preferred over CT in paediatric diseases such as cystic fibrosis (CF), one of the most common genetic disorders in Caucasians. To assess structural lung damage in CF imaging, CT scoring systems provide valuable quantitative insights for disease diagnosis and progression. However, few quantitative scoring systems are available in structural lung MRI (e.g., UTE-MRI). To provide fast and accurate quantification in lung MRI, we investigated the feasibility of novel Artificial intelligence-assisted Pixel-level Lung (APL) scoring for CF. APL scoring consists of 5 stages, including 1) image loading, 2) AI lung segmentation, 3) lung-bounded slice sampling, 4) pixel-level annotation, and 5) quantification and reporting. The results shows that our APL scoring took 8.2 minutes per subject, which was more than twice as fast as the previous grid-level scoring. Additionally, our pixel-level scoring was statistically more accurate (p=0.021), while strongly correlating with grid-level scoring (R=0.973, p=5.85e-9). This tool has great potential to streamline the workflow of UTE lung MRI in clinical settings, and be extended to other structural lung MRI sequences (e.g., BLADE MRI), and for other lung diseases (e.g., bronchopulmonary dysplasia).

Large language models (LLMs) exhibit excellent performance in natural language processing (NLP), but remain highly sensitive to the quality of input queries, especially when these queries contain misleading or inaccurate information. Existing methods focus on correcting the output, but they often overlook the potential of improving the ability of LLMs to detect and correct misleading content in the input itself. In this paper, we propose a novel three-stage fine-tuning method that enhances the ability of LLMs to detect and correct misleading information in the input, further improving response accuracy and reducing hallucinations. Specifically, the three stages include (1) training LLMs to identify misleading information, (2) training LLMs to correct the misleading information using built-in or external knowledge, and (3) training LLMs to generate accurate answers based on the corrected queries. To evaluate our method, we conducted experiments on three datasets for the hallucination detection task and the question answering~(QA) task, as well as two datasets containing misleading information that we constructed. The experimental results demonstrate that our method significantly improves the accuracy and factuality of LLM responses, while also enhancing the ability to detect hallucinations and reducing the generation of hallucinations in the output, particularly when the query contains misleading information.

Nitrate ($\text{NO}_3^-$) is a form of dissolved inorganic nitrogen derived primarily from anthropogenic sources. The recent increase in river-discharged nitrate poses a major risk for coral bleaching in the Great Barrier Reef (GBR) lagoon. Although nitrate is an optically inactive (i.e., colourless) constituent, previous studies have demonstrated there is an indirect, non-causal relationship between water surface nitrate and water-leaving reflectance that is mediated through optically active water quality parameters such as total suspended solids and coloured dissolved organic matter. This work aims to advance our understanding of this relationship with an effort to measure time-series nitrate and simultaneous hyperspectral reflectance at the Fitzroy River estuary, Queensland, Australia. Time-series observations revealed periodic cycles in nitrate loads due to the tidal influence in the estuarine study site. The water surface nitrate loads were predicted from hyperspectral reflectance and water salinity measurements, with hyperspectral reflectance indicating the concentrations of optically active variables and salinity indicating the mixing of river water and seawater proportions. The accuracy assessment of model-predicted nitrate against in-situ measured nitrate values showed that the predicted nitrate values correlated well with the ground-truth data, with an $R^2$ score of 0.86, and an RMSE of 0.03 mg/L. This work demonstrates the feasibility of predicting water surface nitrate from hyperspectral reflectance and salinity measurements.

High stakes decision-making often requires a continuous interplay between evolving evidence and shifting hypotheses, a dynamic that is not well supported by current AI decision support systems. In this paper, we introduce a mixed-initiative framework for AI assisted decision making that is grounded in the data-frame theory of sensemaking and the evaluative AI paradigm. Our approach enables both humans and AI to collaboratively construct, validate, and adapt hypotheses. We demonstrate our framework with an AI-assisted skin cancer diagnosis prototype that leverages a concept bottleneck model to facilitate interpretable interactions and dynamic updates to diagnostic hypotheses.

Bennett's tree kangaroos, one of Australia's most mysterious marsupials, have long eluded researchers. Our new study, published in Australian Mammalogy today, has achieved a breakthrough: using thermal drones to detect these rare animals with unprecedented efficiency. Tree kangaroos are found only in the tropical rainforests of Australia and New Guinea. Unlike their ground-dwelling relatives, they spend their lives in treetops, feeding on leaves and vines. Their dependence on rainforest trees makes them vulnerable to deforestation and climate change.

I didn't intend to pursue computer science. I was a midwife, focused on women-centered care in our private practice--my third career after trying social work and nursing. I only went to the faculty dean to discuss how I might focus my part-time science degree if I were to go full-time. I left his office as part of the first cohort in the new Information Technology (IT) program at the University of Queensland, and I finished my degree with a university medal. My motivation was not love of technology.

Aligning large language models (LLMs) with human expectations requires high-quality instructional dialogues, which can be achieved by raising diverse, in-depth, and insightful instructions that deepen interactions. Existing methods target instructions from real instruction dialogues as a learning goal and fine-tune a user simulator for posing instructions. However, the user simulator struggles to implicitly model complex dialogue flows and pose high-quality instructions. In this paper, we take inspiration from the cognitive abilities inherent in human learning and propose the explicit modeling of complex dialogue flows through instructional strategy reuse. Specifically, we first induce high-level strategies from various real instruction dialogues. These strategies are applied to new dialogue scenarios deductively, where the instructional strategies facilitate high-quality instructions. Experimental results show that our method can generate diverse, in-depth, and insightful instructions for a given dialogue history. The constructed multi-turn instructional dialogues can outperform competitive baselines on the downstream chat model.