Feng Xia

We are working on an intricate challenge in modern healthcare in #Australia to tackle the portability issue of causal CDSS from one healthcare provider to another healthcare provider by leveraging the meta-heuristic approach and AI. To mitigate this challenge, we have proposed an Adaptive Semantic Framework (ASF) which will do the needful adjustments in the existing CDSS according to a new environment. The abstract of our paper is given below: Clinical Decision Support Systems (CDSS) are a critical part of modern healthcare, assisting healthcare practitioners in making precise decisions. However, most of the existing CDSSs are static, which makes adaptation to a new environment or changes in the same environment challenging. This is where the Adaptive Semantic Framework (ASF) comes in. In a recent paper, a team proposed an ASF for CDSS adaptation to a changing environment. The framework includes various methods such as Knowledge-Based Systems (KBS) and Data-Driven Elements (DDE) to adjust the CDSS to new conditions. This framework addresses two significant challenges of CDSS: its maintenance and the issue of portability across different hospitals with varying data ecosystems. With the ASF, CDSS can now adapt to a new environment or local changes in the same environment, including different types of patients, different ecosystems, and varying guidelines. #CDSS #HealthcareTechnology #AdaptiveSemanticFramework #DataDrivenElements #KnowledgeBasedSystems #Research

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The Kingston AI Group has just released a major new report, “Australia’s AI Imperative” - authored by economist John Mangan, The University of Queensland, the paper examines the impact of AI on the Australian economy This major report was commissioned by the Kingston AI Group, which includes 14 leading university professors from eight universities, and the Chair of Robotics Australia Group. Analysis and insights include: Benefits of AI in Australia — “economic modelling suggests that greater utilisation of AI in key Australian industries will lead to a short-term boost in GDP of more than $200 billion per annum and the creation of an additional 150,000 jobs during the period 2023-2030.”  Costs of not doing AI in Australia — “between 2023 and 2027, Australia could face an opportunity cost of 1.4% (or A$35.7 billion) of GDP per year if it fails to introduce AI systems to world standards in key industries, including technology, finance, healthcare, education, and government.”   This prompts an urgent call for action - Australia must enhance its investment in AI now, so we don’t fall behind globally.  The report highlights an increasing global disparity in AI adoption and innovation initiatives. Building Australia’s AI future — The report paints a vision of what Australia’s economy and societal benefits could look like with strategic AI investment and innovation, including leadership in responsible AI, economic resilience, and competitive advantage on the global stage. Read the full report here and join us in the discussion - https://lnkd.in/gjkqvm4M Thanks are due to: My colleagues in the Kingston AI group for their support of Australia's sovereign AI capability  Peter Corke, Stephen Gould, Anton van den Hengel, Sue Keay, Dana Kulic, Jie Lu, Simon Lucey, Michael Milford, Ian Reid, Benjamin Rubinstein, Svetha Venkatesh, Toby Walsh The Australian Institute for Machine Learning (AIML) staff who supported this project Kathy Nicholson Eddie Major Dr Paul Dalby PhD GIA (Affiliated) The many Universities who supported our work, including Monash University, Monash Engineering, Monash Information Technology and Monash Data Futures Institute for their support of me and Dana Kulic in these initiatives. Ann Nicholson, Jesper Kjeldskov, Dinh Phung, Geoff Webb, Andrea Collevecchio, Claudia Del Campo, PhD Read the full report here and join us in the discussion - https://lnkd.in/gjkqvm4M

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Tomorrow I have the immense pleasure to present "Quantum Cybersecurity Program Management" to my #AIPM friends and the timing could not be better! October is Cybersecurity Awareness Month, and 2025 has be proclaimed by the United Nations as the Year of Quantum Science and Technology. The exiting thing about all this is project management is the enabler! It is a great time to be in project management! https://lnkd.in/gtTcemjz #QuantumCybersecurity #PQC #NIST #ProjectManagement #CrytpographicAgility #RiskManagement #QuantumTechnologies #CybersecurityAwareness #ProgramManagement

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🚀 Breakthrough Research Reveals How Web Link Patterns Reflect Economic Performance Our latest research paper, "Harmony in the Australian Domain Space," will be presented at the ACM, Association for Computing Machinery Web Science Conference (WebSci 2024) in Stuttgart, Germany today. 📊 Key Findings: Harmonic Centrality Analysis We used a measurement of website centrality known as harmonic centrality to explore the relationship between economic diversity at the firm level and the structure of the web within the Australian domain space. Our findings reveal consistent patterns in harmonic centrality distributions across different years. Cluster Movements: The temporal movement of domain names across six clusters provides insights into the dynamics of the Australian Domain Space, including trends towards concentration in a few domain names. Predictive Capabilities: Our research shows that harmonic centrality not only measures the structural influence of web domains but also correlates with non-structural features such as company revenue and social media presence. This has profound implications for potential follow-on research that could use web data to improve economic modelling. 🌍 Global Relevance: The observed homogeneity in harmonic centrality distributions across 35 OECD countries suggests that our findings can be generalized and applied to broader international contexts, making this research relevant beyond Australia. 💡 Benefits of this Research: Enhanced Understanding: Provides a new perspective on the relationship between web structure and economic diversity, aiding policymakers and businesses in strategic decision-making. Innovative Metrics: Introduces harmonic centrality as a robust metric for assessing the influence and connectivity of web domains, with potential applications in various industries. Data-Driven Insights: Utilizes large-scale, publicly accessible datasets to offer reproducible and reliable insights into web and network analysis. 🔗 Read the Full Paper: I invite you to read our detailed findings and methodologies below. Thank you to my amazing co-authors Xian (Elaine) Gong, Marian-Andrei Rizoiu, and Paolo Boldi, and our wonderful collaborators for making this research possible. We are excited to share our work with the Web Science community and look forward to the discussions and feedback. #WebSci2024 #DataScience #EconomicDiversity #HarmonicCentrality #Research #Innovation #computationalsocialscience

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AI CEOs: A Ponderous Perspective on the Future of Corporate Control? This is an excellent, thought-provoking piece. Can AI replace top-tier executives? The University of Waikato Ethics Professor Nick Agar suggests a future where AI algorithms, not humans, occupy the corner offices. The rising CEO salaries prompt the question: Is automation the future of leadership? https://lnkd.in/gv24pwJR AI's capability to learn complex tasks, like driving, could extend to a corporate setting. An AI CEO could potentially outperform human decision-making, pushing companies to new efficiency heights. However, this vision stirs complex questions: What principles will guide AI leaders - relentless profit or societal good? #AIinLeadership #FutureOfCorporate #Automation #AIExecutives #AIandEthics #AIefficiency #AIforGood #LeadershipInnovation

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Really great to have a chapter in the new book released today: “Researching Incidental Vocabulary Learning in a Second Language”. Edited By Mark Feng Teng, Barry Lee Reynolds. Well done Mark and Barry on this terrific work!! Amazing vocabulary scholars throughout this volume, with minor exception of Chapter 7. My chapter is: “Data Science and Incidental Vocabulary Learning” Abstract: Chapter 7 provides a characterization of data science approaches to incidental vocabulary learning. It begins with an overview of data science generally, and its methodological value to incidental vocabulary learning research. Chapter 7 provides an illustrative review of several recent studies of incidental vocabulary learning that utilize data science and discusses some critical issues in data science projects, such as the underlying data, data handling, modelling parameters, and types of outcomes. The chapter focuses on natural language processing data science, as this has been most relevant in incidental vocabulary learning research. It provides some methodological guidance on data science research designs, including an example future study. The chapter argues for the value and increased use of data science in incidental learning research to inform decision making, see what is and is not possible, and to test hypotheses in lieu of, or in support of, intervention studies Link: https://lnkd.in/gv9MtPkj

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CSIRO writes "Everyday AI: Artificial intelligence in pregnancy and the early years - AI is breaking new ground in the earliest stages of our lives, revolutionising fertility care and improving the wellbeing of premature babies by enabling early detection of developmental challenges." https://lnkd.in/eF2i5cj4. #artificialintelligence #pregnancy #earlyyears #fertilitycare #revolution #healthcare #csiro

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Great that awareness is starting to get out there in the mainstream media. Of course one of the main challenges is that we don't even really understand how to talk about these risks yet let alone control them. We certainly need more folks working at the interface between technical nuance and big picture governance, making sure that policies and procedures are aware of how things actually work, are technically actionable, and can meaningfully achieve the goals with known and controlled side effects.

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[𝐀𝐈 𝐟𝐨𝐫 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲] Published in 𝐍𝐚𝐭𝐮𝐫𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞, our revolutionary AI tool 𝐏𝐒𝐈𝐂𝐇𝐈𝐂 predicts protein-ligand interactions using only sequence data! This method outperforms traditional structure-based systems, significantly accelerating and reducing the cost of drug discovery.

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Research Australia is in #WesternAustralia for a Research Roundtable to discuss #AI in #research and explore the great #science and #innovation of #WesternAustralia #science #universities

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When OpenAI released ChatGPT in December 2022, many people (including me) thought customer assistance chatbots would be the first commercial application. For example, you have a query about Telstra mobile phone plans, you call the Telstra web site, a chatbot picks up the call and answers all your questions. It has been almost two years and we are yet to see a successful chatbot on any large company web site. This is because LLMs have proven to be quite bad in retrieving specific information on demand from a company database. This problem can be solved by combining ad hoc measures (such as text-based search and clustering) combined with LLM transformers. To support this assertion, I posted on Substack description of a python/Jupyter notebook program with the source code available on github as open source.  I am using Llama 3.1 on my local computer and my blog post archive as a proxy for a textual knowledge base. I am now convinced that local LLMs are feasible but need to be combined with careful source material design. If you are interested in LLMs for small company knowledge base Q&A, I suggest you start here.

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This week as my feed filled with posts about OpenAI's latest GPT, they all seemed to skip a fundamental aspect of AI: the hardware - aka the high performance computing resources that are required to train and these models. The sustainability of AI is a topic that doesn't get as much discourse as AI itself - which is odd since without the infrastructure, AI models simply couldn't exist. So I was glad to contribute to a panel held by the The Australian Academy of Science where we discussed how Australia could improve our high-performance computing capabilities. The report is now released - you can access it via the link below but here's a TLDR: - Need for a National Strategy: Currently, there is no national strategy for the next generation of HPCs in Australia. Given the escalating needs for research and the need to keep pace with global advancements, such a strategy is critical. - Bolstering AI Capabilities: Australia’s research HPC also bolsters our AI capabilities. Without computing power on our own shores, Australia’s AI capability relies on other nations. - Potential of Quantum and Cloud Computing: Despite their potential, quantum computing and cloud computing cannot replace traditional HPC functions for scientists in the near term. - Ideal Host for Future HPC: Australia’s stability, connectivity, and renewable energy capacity position us as an ideal host for future regional HPC, especially an Asia-Pacific Tier-0 exascale facility. - Securing Sovereign Capability: Securing sovereign capability depends on both investment in infrastructure and investment in software readiness programs. Similarly, the role of training, education, data management and AI is crucial to support future growth. Link to full report: https://lnkd.in/gms43cV8 #HPC #AI #quantum #exascale

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There are a few good reasons for Australia to develop our own LLM, including the need to maintain our own culture, and control over the flow of our own information. The primary argument, however, is that LLMs are the infrastructure of an AI-enabled economy, and if Australia is going to make the transition we need to own and control our own infrastructure. The transition is already here, it's just that it's not currently Australian, it's American. The idea that an American should tell us that we should just leave it to them is no great surprise I suppose. It's the modern equivalent of colonialism, and they want to own the railroads.

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A concise summary of the NICE position statement on the use of AI in evidence generation: Use of AI in evidence generation: NICE position statement https://lnkd.in/g33fARPx This statement relates to the potential use of AI in developing and reporting evidence considered by NICE. It does not consider the evaluation of health technologies that use AI methods to perform their functions (AI-enabled technologies). - Trust in AI: Emphasizes the need to build trust in AI applications for decision-making, advocating for human involvement in the process. - Principle of Augmentation: AI should augment, not replace, human expertise, ensuring informed human oversight in AI-driven processes. - Transparency: Submitting organizations must declare AI usage, explain methods, and report on human input in submissions. - Security Measures: Organizations are required to implement robust security measures to prevent unauthorized access and manipulation of data. - Applications of AI: This section highlights potential uses of AI, such as automating data extraction and generating summaries from clinical studies. - Natural Language Processing: Discusses the role of NLP in analyzing large datasets and simplifying complex information for a broader understanding. - Future Directions: Encourages ongoing research and development to overcome barriers in AI-driven evidence generation for health technology assessment. #AI #HealthTech #EvidenceGeneration #NICE #Innovation #Healthcare #DataSecurity

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*Open PhD positions - high-dimensional quantum computing in silicon* My group at #unsw has several open PhD positions, to conduct research on the encoding of quantum information in high-dimensional nuclear spins in silicon. These atomic-size systems can be used to efficiently encode logical qubits. Spread the word! https://lnkd.in/gNpQQPXq #PhD #quantumcomputing #quantum

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NZ has the third highest rate of AI adoption by employees according to the piece by Chris Keall, but we're still mostly supporting point applications and meta-analysis in our research community. Our low population, developed and reasonable society, and history of facing up to and benefiting from societal and technological change gives Aorearoa NZ more to gain than other countries, and less likely risk, from applying AI broadly to enable higher productivity driving more fulfilling lives. Collaborating well within Aotearoa and across the world, we can be a major contributor to the fundamental AI research that will drive productivity and effectiveness gains that benefit the country, its societies and it's people. If you'd like to work with NAOInstitute's Strong AI Lab to help NZ lead in developing powerful, beneficial AI, let us know.

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(cont. from https://lnkd.in/gRfY-jRt) SPIRE paper: Online Computation of String Net Frequency with Peaker Guo (University of Melbourne), Tony Wirth (University of Sydney, University of Melbourne), Justin Zobel (University of Melbourne) The net frequency (NF) of a string in a text is the number of occurrences of the string in the text with unique left and right extensions. NF was originally introduced for Chinese NLP tasks but there is a lack of understanding of the properties of NF and the absence of efficient algorithms for computing NF. At CPM 2024, we showed that NF is combinatorially interesting and how NF can be computed efficiently in the offline setting. For many applications, however, facilitating the computation in an online manner is highly desirable. We are the first to solve the problem in the online setting, and we do so in optimal time.

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