A fragment of a bronze military diploma from Sardinia, issued by the emperor Trajan to a sailor on a warship, as restored by Aeneas. If you believe the hype, generative artificial intelligence (AI) is the future. However, new research suggests the technology may also improve our understanding of the past. A team of computer scientists from Google DeepMind, working with classicists and archaeologists from universities in the United Kingdom and Greece, described a new machine-learning system designed to help experts to understand ancient Latin inscriptions. Named Aeneas (after the mythical hero of Rome's foundation epic), the system is a generative neural network designed to provide context for Latin inscriptions written between the 7th century BCE and the 8th century CE.

We introduce MecQaBot, an open-source, affordable, and modular autonomous mobile robotics framework developed for education and research at Macquarie University, School of Engineering, since 2019. This platform aims to provide students and researchers with an accessible means for exploring autonomous robotics and fostering hands-on learning and innovation. Over the five years, the platform has engaged more than 240 undergraduate and postgraduate students across various engineering disciplines. The framework addresses the growing need for practical robotics training in response to the expanding robotics field and its increasing relevance in industry and academia. The platform facilitates teaching critical concepts in sensing, programming, hardware-software integration, and autonomy within real-world contexts, igniting student interest and engagement. We describe the design and evolution of the MecQaBot framework and the underlying principles of scalability and flexibility, which are keys to its success. Complete documentation: https://github.com/AliceJames-1/MecQaBot

Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is trained on massive amounts of text data and uses deep learning techniques to learn patterns and relationships within the data, enabling it to generate coherent and contextually appropriate text. This position paper proposes using GPT technology to generate new process models when/if needed. We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes. ProcessGPT can be designed by training a generative pre-trained transformer model on a large dataset of business process data. This model can then be fine-tuned on specific process domains and trained to generate process flows and make decisions based on context and user input. The model can be integrated with NLP and machine learning techniques to provide insights and recommendations for process improvement. Furthermore, the model can automate repetitive tasks and improve process efficiency while enabling knowledge workers to communicate analysis findings, supporting evidence, and make decisions. ProcessGPT can revolutionize business process management (BPM) by offering a powerful tool for process augmentation, automation and improvement. Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting data engineers in maintaining data ecosystem processes within large bank organizations. Our scenario highlights the potential of this approach to improve efficiency, reduce costs, and enhance the quality of business operations through the automation of data-centric and knowledge-intensive processes. These results underscore the promise of ProcessGPT as a transformative technology for organizations looking to improve their process workflows.

A scanned lace monitor lizard (Varanus varius) image produced by using new technology. Blue-tongue lizards and sulphur-crested cockatoos are among the native animals frequently smuggled overseas. While the number of live animals seized by the Australian Government has tripled since 2017, the full scale of the problem eludes us as authorities don't often know where and how wildlife is trafficked. Now, we can add a new technology to Australia's arsenal against this cruel and inhumane industry. Our research shows the potential for new technology to detect illegal wildlife in luggage or mail.

This paper presents Macquarie University's participation to the two most recent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task~B (BioASQ10b), Phase~B. In these tasks, participating systems are expected to generate complex answers to biomedical questions, where the answers may contain more than one sentence. We apply query-focused extractive summarisation techniques. In particular, we follow a sentence classification-based approach that scores each candidate sentence associated to a question, and the $n$ highest-scoring sentences are returned as the answer. The Synergy Task corresponds to an end-to-end system that requires document selection, snippet selection, and finding the final answer, but it has very limited training data. For the Synergy task, we selected the candidate sentences following two phases: document retrieval and snippet retrieval, and the final answer was found by using a DistilBERT/ALBERT classifier that had been trained on the training data of BioASQ9b. Document retrieval was achieved as a standard search over the CORD-19 data using the search API provided by the BioASQ organisers, and snippet retrieval was achieved by re-ranking the sentences of the top retrieved documents, using the cosine similarity of the question and candidate sentence. We observed that vectors represented via sBERT have an edge over tf.idf. BioASQ10b Phase B focuses on finding the specific answers to biomedical questions. For this task, we followed a data-centric approach. We hypothesised that the training data of the first BioASQ years might be biased and we experimented with different subsets of the training data. We observed an improvement of results when the system was trained on the second half of the BioASQ10b training data.

We are looking for an outstanding academic leader at Level E (Professor) with an excellent track record of teaching and research in the discipline of Artificial Intelligence (AI). The appointee will provide leadership and mentorship in the AI discipline within the School of Computing and leverage your international standing to provide a focal point for the further development of the discipline at Macquarie University. You will have a distinguished scholarly research record and a renowned reputation in the field. Your role will ensure research, teaching, and engagement activities within this discipline excel, you will mentor and support staff to develop them and grow their careers. You will demonstrate research excellence, contribute to the development and management of our teaching programs, provide leadership amongst national and international peers, and represent the University at public forums.

InteliCare has been awarded a $100,000 grant from the New South Wales Smart Sensing Network ("NSSN") to develop its machine learning (ML) capability in conjunction with the University of Sydney (USyd) and Macquarie University (MU). The company is negotiating an agreement with USyd, MU and the NSSN to use these funds to help fund a one-year joint project delivered by the universities' Computer Science Departments. The goal is to build ML algorithms that can predict and prevent chronic disease and mental health deterioration that can lead to a loss of independence and an increased risk of injury. In addition to the NSSN funds, InteliCare will provide a co-contribution of $152,898 in cash and the universities will provide $161,021 of in-kind support. Ongoing development beyond the initial project will require the company to budget from working capital.

"Together they created Appen, arguably one of the greatest IT success stories in Australia," said University of Sydney Vice-Chancellor and Principal Professor Stephen Garton AM. "Their company was founded long before artificial intelligence became fashionable and is a testament to the foresight of the Vonwillers." Honorary degrees are awarded to individuals who have made an outstanding contribution to the wider community or who have achieved exceptional academic or creative excellence. Husband and wife team Chris and Julia Vonwiller have been admitted to the degree of Doctor of Engineering (honoris causa). Dr Vonwiller is a respected linguist who studied at Macquarie University and graduated in 1980 with a Bachelor of Arts (Honours). She completed her PhD in linguistics at Macquarie University in 1989 before working as a researcher at the University of Sydney.

Teamwork isn't just a human characteristic: Colonies of army ants will form living'scaffolding' to protect members from falling. The insects are blind and have no designated leader but, according to new research, they're able to use simple behavioral rules to develop these safety structures without the need for direct communication. Once a scaffold was built, worker ants were almost 100 percent protected from falling off steep inclines. Understanding how they design such complex structures could help engineers development self-healing materials and swarm robotics, researchers said. Army ants in Central American rainforests will build scaffolds out of their body to help them traverse steep terrain.

When we strive for patient safety, we aim for the absence of preventable harm to a patient in the provision of care and a reduction in the risk of unnecessary harm associated with healthcare. From better training to improvements in workplace culture, there are countless ways to curb harm and the risk of adverse care events, but undoubtedly one of the most promising is the increasing use of digital technologies. This webinar will feature presentations from experts in the fields of patient safety and artificial intelligence (AI). To begin, Professor Johanna Westbrook will speak on her research on electronic medications management (eMM) systems in Australian hospitals. Specifically, she will discuss her findings from an innovative stepped-wedge trial designed to determine whether medication error rates significantly declined following implementation of an eMM system in a tertiary paediatric hospital. From there, Associate Professor Shlomo Berkovsky will discuss his work on the use of AI for categorising frail elderly patients.