Ali Akbari

#Graph databases can uncover intricate relationships hidden in #data, but the key lies in having a graph model that accurately represents the use case. Kelsen Diogenes, during his Master's degree at the University of Auckland - Data Science, developed a property graph #model. This model specifically targets #fraud patterns within publicly available Company Office Data in New Zealand. By encoding suspicious patterns as queries in #Cypher, and executing them against a #Neo4j database, potential instances of fraudulent behavior like beneficial ownership, income tax evasion, money laundering, or Phoenix companies can be identified. Please find a teaser presentation below, and I will send the full report (anonymised) on request: https://lnkd.in/gurDqjWZ

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✨V4E1 Spotlight✨ Fairness in AI models is a matter of growing importance in the field. Kejun Dai presents different frameworks for evaluating bias in AI models. This research involved learning each framework and running benchmark experiments. Read the full article here: https://lnkd.in/gKeSPbtD

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Hi Kanban Singapore folks, There's an upcoming workshop on Systems Thinking—a powerful approach to understanding and managing complex systems. Systems Thinking helps us see the big picture by recognising patterns and interconnections within systems. This is particularly relevant to Kanban, especially with the STATIK (Systems Thinking Approach to Implementing Kanban) method, which we use to optimise our workflows. By applying Systems Thinking, we can better identify bottlenecks, understand feedback loops, and make more informed decisions to improve our processes. The workshop will cover: - Introduction to Systems Thinking - How to apply it in various contexts - Hands-on practice with causal loop diagrams It's a fantastic chance to deepen our knowledge and enhance our Kanban practices. If you're interested in joining, register via Telegram here: https://lnkd.in/gYydvTiS. Cheers, Debbie #KanbanSingapore #KanbanSG #Kanban #KanbanUniversity #YesWeKanban #LeSS #lessworks

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This is the prediction I was recently talking about - that the regulatory space and recommendations for government organisations will quickly trickle in to other enterprises. Here we go! This type of explosion in positions for ‘head of AI’ type roles will put a drain on an already small talent pool. When looking for someone to help shape your industries uptake of AI, look for someone who ✅ understands the regulatory space and how it applies to your industry ✅ is able to think about and make educated predictions of future changes in this space ✅ is concerned with ethical and responsible AI ✅ is able to examine how AI can help achieve business objectives and contribute to company culture and ethos in a wholistic way ✅ can contribute to increasing everyone’s AI literacy ✅ is able to also lead and shape change management to being everyone along ✅ can think BIG - this stuff moves so quickly - it’s not about tomorrow, it’s about examining what the future of work looks like and how we can start equipping ourselves with the skills and structure we need NOW this is going to be SUCH an interesting space to watch

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https://lnkd.in/gwaDVmnR Very informative article by the Dean of Big Data Bill Schmarzo. I especially liked the way he outlined the contribution of each of these legs specifically in the cited industry use cases While the GenAI wave has kinda democratised the usage of this powerful tech, somewhere we lose the importance of what Bill Schmarzo calls the “predictive, understandable, and continuously learning legs of AI”. #genAI #bigdata

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Major news flash: leveraging part of Google’s $1B Digital Futures Initiative we just signed a 1-plus-1-equals-15 deal. Bringing together the two AI and Science powerhouses CSIRO and Google, this partnership will be an inflection point in AI-powered scientific discovery. Press releases with more info here: Google: https://lnkd.in/gDxbsR4b CSIRO: https://lnkd.in/gG2EyqMz Professionally speaking, for my whole career I’ve been passionate about two things more than about anything else- and by far at that: #science and #AI. Can you imagine the enthusiasm I’m feeling at this moment about getting to marry the two so spectacularly…we only got here because of so many others who share that enthusiasm and determination - just calling out a few names on both sides of the aisle: Jon Whittle Scott Riddle Astitva Chopra Heather Tompkins LINDA ALLARD Dr Rebecca Michael Eliza Keck Alison Donnellan Grace Chung Melanie Silva - and thank you to many more whose relentless work has made this possible, you know who you are. To all scientists out there: don’t just watch this space: get in touch and check out the links in the press releases - you do NOT need to be an AI expert or seasoned data scientist to have fun at this party…this is for all scientists and we’re just getting started!! Nicholas Thompson Cade Metz Melissa Heikkilä Mike Cherney https://lnkd.in/giitUt7E

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On 15 April 2024, #SMUYPHSL hosted a research seminar by Prof Jeannie Marie Paterson (University of Melbourne) on the topic of 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘈𝘐 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘚𝘤𝘰𝘱𝘦 𝘰𝘧 𝘙𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺 𝘧𝘰𝘳 𝘗𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭 𝘈𝘥𝘷𝘪𝘤𝘦 𝘢𝘯𝘥 𝘈𝘴𝘴𝘪𝘴𝘵𝘢𝘯𝘤𝘦. Having unpacked how generative AI works—in particular, the reliance on statistical probabilities rather than logical reasoning—Prof Paterson observed how its use in the dispensation of advice or information can be problematic. Focusing on the regulation of such use of generative AI, Prof Patterson examined the frequent deployment of exemption clauses and how this might offend certain principles of contract law relating to unfair terms. Through this illuminating seminar, Prof Patterson have inspired us to think harder about the intersection between traditional laws and emerging technologies. #SMULAW #SMUYPHSLResearch #ShapingtheFutureofLaw

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8️⃣6️⃣📈 We surveyed the Australian Data & AI ecosystem late last year and saw 86% of respondents answering their core data assets were in ‘acceptable’ state of worse! Let that sink in, to understand what that means for data-driven reporting, BI, and AI. It has a few logical implications: 1️⃣After pulling a dataset from a database data needs significant further labour intensive curation for any meaningful business or artificial intelligence reducing both ROI and time to value for Data & AI. 2️⃣Organisations will revert back to biased decision making, either by using personal experience from leaders, or based on generic market insights from management consultants often based on terrifying assumptions You start seeing why I personally moved from management consulting to data consulting? Our world is in dire need of less biased decision making. For more, read our State of Data & AI report: https://lnkd.in/gwCHiD_x

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An article “An Effective Approach for Multiclass Classification of Adverse Events Using Machine Learning” authored @Indu Bala，@Thu-Lan Kelly, Renly Lim, @Marianne H. Gillam and Lewis Mitchell from @The University of Adelaide and University of South Australia has been published in Volume 3 Issue 3 of Journal of Computational and Cognitive Engineering (eISSN: 2810-9503). We sincerely recommend it to you. Abstract: Implantable medical devices are commonly used to treat various medical conditions. These devices, however, may cause serious adverse events, including repeated surgical intervention and death. Prolonged use of some implantable medical devices can shorten life expectancy and significantly decrease a person‘s quality of life. Large adverse event databases can be used to predict serious adverse events by training machine learning (ML) models on available data. However, the large volume of data and long free-text response make it challenging to use the databases effectively. This study focuses on one such dataset: the Australian Database of Adverse Event Notifications, comprising text written by patients, or healthcare professionals, or pharmaceutical industry. The study focuses on predicting three significant events: Injury, No Injury, and Death, based on the adverse events reported about the implanted device. A new ML approach called the random regression voting classifier, which combines random forest (RF) and logistic regression (LR), is proposed. The model’s efficiency is evaluated through experiments using techniques, such as Bag of Words, Term-Frequency-Inverse-Document Frequency, and Global Vector, and is compared to existing ML models such as decision tree, RF, kernel support vector machine, Naive Bayes, LR, and XGboost. The results demonstrate a higher performance in predicting adverse events than other considered approaches. The various experimental analyses showed that the proposed approach performed better than other ML models. Read the full paper: https://lnkd.in/gFtSdzQ2 #postmarketsurveillance #adverseevent #implantablemedicaldevice #machinelearning #naturallanguageprocessing

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5 questions with Marcello Cacciato (Metyis) on bringing Generative AI to life Marcello Cacciato, Director in the Data Science practice of Metyis, is an expert Generative AI, having advised multiple organisations on the development and implementation of their Generative AI stra https://lnkd.in/efSSGj3g to-life

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🌍 What is the Australian Government ’s perspective on #AI governance? An exploration of #AI ethics, governance, and risk management led by the Australian Government and the Governance Institute of Australia outlines essential principles for the ethical and responsible implementation of #AI. 🔹 #AI Ethics & Governance: Ethical #AI deployment relies on principles of transparency, accountability, and fairness. Implementing responsible #AI practices can mitigate biases and protect data privacy, fostering public trust and aligning with ESG objectives. 🔹 Risks & Opportunities: While #AI drives innovation and operational efficiency, it also introduces challenges such as privacy risks and unintended biases. Adopting a responsible approach to #AI unlocks its potential for personalized customer experiences and expanded market access. 🔹 #AI as a Catalyst for Growth: #AI promotes strategic growth by enhancing operational efficiency and decision-making. It democratizes information access, empowering sectors like healthcare, finance, and non-profits. 🔹 Impact of the EU #AI Act: This pivotal legislation from the European Commission emphasizes precautionary measures, especially in high-risk #AI applications. Using frameworks like the European Commission's EU #AI Act helps align global #AI governance practices and ensures regulatory compliance. 🔹 Best Practices for Organizations: Risk Management: Regular assessments and audits ensure that #AI systems remain secure and reliable. 🔹 Integration with ESG Goals: Align #AI efforts with broader organizational ethics to support sustainable development. 🔹 Continuous Learning: Strengthen #AI literacy and upskill teams to foster a culture of innovation and accountability. 🔹 ----------------- 📢 For more details, connect with AI & Partners on LinkedIn or reach out at [email protected] to tackle #AI regulations and bolster your #AI initiatives. #AI #Governance #EthicalAI #AIAct #SustainableAI

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I'll be speaking on a panel discussion tomorrow at the Generative AI Summit on the topic of "How can we gather accurate and relevant data and optimise it for generative AI use?" https://lnkd.in/gU8ZKsdy

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*AI and ontology for automating IM Compliance as-a-Service* I am absolutely stoked to see the Archives NZ AI Auto-classification for Information Management Proof of Concept, which demostrated the power of AI tools to automate Public Records Act, and Privacy Act compliance, and to identify and surface information of interest to Māori. The full report is now available. This was a concept I had been thinking about since 2017, but the tooling just wasn't easily available back then. So, we ran a project to assess how AI could automate Information Classification in SharePoint/M365, and Shared Drives hosted in the Cloud. I pitched the concept to Amazon Web Services (AWS) and Microsoft in 2020, and much to my surprise , immediately saw the potential for AI in the public sector years before #ChatGPT brought this technology to broad attention. We successfully navigated the highly competitve Dragon's Den pitch through the Westpac NZ Government Innovation Fund in 2021, to put the seed funding in place to run the project, which kicked off in early 2022. it was amazingly successful (but possibly a year, or two ahead of it's time). Anyway, I am super proud of this work, and in particular the awesome team at Archives New Zealand Te Rua Mahara o te Kāwanatanga who made the dream a reality. We also had the benefit of the All-of-Governmnet Ontology, which was a critical intellectual underpinning for the approach (https://lnkd.in/g6bp_wfp) I addition to our technology partners, it wouldn't have been possible without our Public Sector partners Ministry of Justice - New Zealand and Ministry for Primary Industries (MPI) The report also includes the AWS and Microsoft technical reports, on the tools we used and how the AI performed: AWS report on Document Understanding Proof of Concept — Archives New Zealand (PDF 1.2MB) Outcome Document — Proof of Concept with Microsoft Azure Datalake, Purview and Text Analytics for records disposal (PDF 1.6MB) https://lnkd.in/gPMGgUJx Special Thanks to: The governance group: Angela Beaumont (Director of digital delivery, (Archives), Joanna Condon (Archives), Joanne Koreman (Archives), Anahera Morehu (Archives); Antonia Aloe, Brian Nevin, Tony Eyles (DIA); Andre Post (MPI); and Tony Breen (MoJ). The project team: Shakil Akhter, Elisa Capecci, Joanne Koreman, Madeleine Pierpoint, Spencer Tasker (DIA); Tanya Hurley, Sara Knight (MPI); Vanessa Cuthbert-King (MoJ); Marzieh Cameron, Jan Hutar, Carly Lenz, and Joshua Ng (Archives). Technology Partners: Mark Gordon, Craig Hind, Matthew McCulloch (AWS); Nick Cater (Microsoft New Zealand); Bruce Anderson, Richard Malloch and JP Van Heerden (Insight). Information and technical specialists: Namita Parshotam (DIA); Annabel Snow (Deloitte); Team IM.  #Privacy #AI #ML #Cloud #AWS #Microsoft #Archives #InformationManagement #Autoclassification #AIforIM #Compliance #M365 #SharePoint #Government #ontology

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An article “An Effective Approach for Multiclass Classification of Adverse Events Using Machine Learning” authored @Indu Bala，@Thu-Lan Kelly, Renly Lim, @Marianne H. Gillam and Lewis Mitchell from @The University of Adelaide and University of South Australia has been published in Volume 3 Issue 3 of Journal of Computational and Cognitive Engineering (eISSN: 2810-9503). We sincerely recommend it to you. Read the full paper: https://lnkd.in/gFtSdzQ2 #postmarketsurveillance #adverseevent #implantablemedicaldevice #machinelearning #naturallanguageprocessing

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Propensity score matching is one of the most important tools for causal inference/program evaluation considered as a replacement for RCTs when one has observational data. I have a tutorial after having a session in Impact Evaluation Workshop ended last week Pakistan Institute of Development Economics - PIDE where Bilal Khan provided deep insight about PSM. Please find this tutorial and give your advice to improve it (its a draft) https://lnkd.in/dw9sy-xy

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📢 Hey friends of Data Sciences and Machine Learning, I'm looking for a speaker for Perth Machine Learning Group to share ideas, learnings, experiments and knowledge for the August Meetup (In-person). DM me for more details. If you have a vague idea of what you might want to present, ping me anyway! I'll help you polish it! #AI #ML #LLM #SpeakingOpportunity

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