Sebastian Brickel, PhD

In the emerging world of #AI , there is now this in #NLP - Natural Language processing for real time translation . Very fascinating indeed But Computational Linguistic is another new domain. To extract data from conversation, Really.? What all is happening in the connected world Yes heard about ethical AI and data management but the beneath is a critic who terms it as "Colonization" or call it hegemony Really in the connected are these apprehensions genuine ?. Reminded of John Lennon Imagine a world without boundaries and religion , what will one fight for .. So be it being a dreamer..With AI technology, all these  of region and religion are just an inconsequential tags . Machine Learning toward autonomous search for the humanity to be well informed So many project and jobs are happening in NLP and that too Computational Linguistic. Great to be a super specialist in these cutting edge technology NLP . Be it Bangla or Bhojpuri or Sinhalese or Swahili. Now seamlessly connected with no barriers in the world of opportunities https://lnkd.in/g7cv2vAd

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🚀 Excited to share our recent work in #AI4Science for predictive ecotoxicology! 🚀 In today's fast-paced world, the ability to assess the toxicity of chemicals is vital not just for environmental protection but also for boosting economic productivity. Efficiently predicting the harmful effects of chemicals allows industries to make informed decisions, streamline regulatory compliance, and reduce costly in vivo testing. In collaboration with CSIRO’s Environment BU and CSIRO's Data61 as a part of the Spatiotemporal AI portfolio, we have developed and released GRAPE (Graph Neural Networks-enhanced RelAtion Prediction for Ecotoxicology). 🌍 GRAPE uses Graph Neural Networks (GNNs) to predict the toxicity of chemicals across various aquatic species, offering a novel and more efficient approach to complement traditional in vivo testing. Our model achieves significant improvements in recall—boosting performance by over 30% while showing it can generalize to unseen chemicals and species (not seen in the dataset). 🧬 Paper: https://lnkd.in/g5E57mph Code: https://lnkd.in/gQR_mGhY This was led by the great work of Gaurangi Anand, along with Piotr Koniusz, Anu Kumar, Lisa A. Golding, Matthew J. Morgan and Peyman Moghadam. This research was funded by the Machine Learning and Artificial Intelligence Future Science Platform (MLAI FSP) and Science Digital at CSIRO. Cheng Soon Ong Stefan Harrer, PhD Jon Whittle Brett Molony Professor Aaron Quigley Ryan McAllister Navinda Kottege Big thanks to our collaborators at CSIRO. This is a big step forward in making ecological risk assessment more sustainable and scalable. 💡 #ecotoxicology #deeplearning #AI #GNN #sustainability #AIforScience #paperwithcode

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The job of #ContentMarketing teams is highly complex, containing all of the following (and more): - Analyze task performance and optimize content to align with those experiments that are working - Manage content and digital assets for reuse (instead of recreating the wheel) - Publish to multiple channels and digital experiences - Personalize content to meet the individual needs of customers - Experiment at scale and with precision - And... the cycle continues... What if I told you that what's missing from your #martech stack isn't even on your radar right now? To truly align your #content, your user intent #data, and your #contentanalytics you need to invest in a Semantic Layer. Luckily there's an upcoming event where you can learn about it. #ContentStrategy #ContentOperations #MarketingOperations

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🧠 The myth: More data makes an #AI model smarter and therefore more accurate 💪 The truth: It’s not just about data—it’s about #parameters With the around OpenAI’s #GPT4o it might seem that feeding more data into these models makes them smarter, the real game-changer is the number of parameters they have like Meta #Llama 3.1 with 450b parameters 💡 More parameters mean these models can process and understand data in more sophisticated ways, leading to increased accuracy and the appearance of being “smarter.” More parameters = higher trustworthiness, even with more focused data like the proprietary #data in your organization In the world of conversational user interfaces becoming the norm, more parameters will be crucial for a seamless experience. While yet too slow and too expensive for real fast interactions between humans and machines, with #quantumcomputing we will move in a new area of responsible #UI #design #AI #MachineLearning #LLM #conversationaldesign #hmi #Innovation

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Key takeaways from the new article ‘Ten simple rules for recognizing data and software contributions in hiring, promotion, and tenure’, just out in PLOS Computational Biology, which outlines practical steps to update institutional processes to recognize open data & software outputs in academic evaluation. Full article available here https://lnkd.in/eP6wbvFf

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"Streaming scientific software has never been so EESSI" by Alan O'Cais (Research Fellow / Universitat de Barcelona; Research software engineer / CECAM) Abstract: Have you ever wished that all the scientific #software you use was available on all the resources you had access to without having to go through the pain of getting them installed the way you want/need? The European Environment for Scientific Software Installations (#EESSI – pronounced “easy”) is a common stack of scientific software installations for HPC systems and beyond, including laptops, personal workstations and cloud infrastructure. In many ways it works like a streaming service for scientific software, instantly giving you the software you need, when you need it, and compiled to work efficiently for the architecture you have access to. In this talk, we’ll explain what EESSI is, how it is being designed, how to get access to it, and how to use it. We’ll include a number of demonstrations and review significant developments of the last 12 months (including support for #NVIDIA GPUs, and active development for RISC-V systems). #HPC #HPCKP #HPCKP24

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Our book "Generative AI for Effective Software Development" is now available on Springer: https://lnkd.in/et6MYb7c You can also pre-order the paper version on Amazon: https://a.co/d/9Xczbtq 🔎 About the Book: an in-depth empirical exploration of how Generative AI is adopted through detailed chapters featuring case studies, surveys, and experiments. Our book provides both practitioners and academics with a nuanced understanding of the current and future impacts of AI technologies in software engineering. 📘 Why Read This Book? Whether you're a developer looking to integrate AI into your projects, a manager aiming to navigate the evolving tech landscape, or a researcher seeking the latest in empirical AI studies, this book is an invaluable resource. 🌐 Get Your Copy Today! Visit Springer's website to purchase your digital or print copy and stay ahead in the rapidly evolving field of software development.

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‘The AI Scientist’. A #GenAI agent of agents able to read literature, generate ideas being testable in silico, write code and viz to test it, write publications, review it. Even if outcome is not great yet according to comments, it is a glimpse at the future. Volume of scientific literature will increase, we will all need to adapt. https://lnkd.in/ehXsfVAz https://lnkd.in/e8eeQpGa

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👉🏼 How do large language models answer breast cancer quiz questions? A comparative study of GPT-3.5, GPT-4 and Google Gemini 🤓 Giovanni Irmici 👇🏻 https://lnkd.in/eUj2RKjX 🔍 Focus on data insights: - 📊 The study assessed the performance of three LLMs in answering breast cancer-related questions, revealing significant differences in accuracy among them. - 🧠 GPT-4 outperformed the other models, achieving a 95% correct answer rate, indicating its superior capability in processing complex medical information. - 🔄 No significant difference was found in the accuracy of responses to questions from public databases versus newly formulated questions, suggesting consistent performance across different types of queries. 💡 Main outcomes and implications: - 🌟 The results underscore the potential of LLMs like GPT-4 to enhance decision-making in breast cancer care through accurate information retrieval and summarization. - 🔍 The findings advocate for further refinement of LLMs through in-context training to improve their applicability in clinical settings. - 🏥 This study highlights the importance of integrating advanced AI tools into healthcare, potentially leading to better patient outcomes and more informed clinical decisions. 📚 Field significance: - 💡 The research contributes to the growing body of evidence supporting the use of AI in healthcare, particularly in oncology, where timely and accurate information is crucial. - 📈 It opens avenues for future studies to explore the integration of LLMs in various medical domains beyond breast cancer, enhancing overall healthcare delivery. - 🤖 The study emphasizes the need for ongoing evaluation and development of AI technologies to ensure they meet the rigorous demands of medical practice. 🗄️: [#largeLanguageModels #breastCancer #AIinHealthcare #GPT4 #medicalAI #oncology #dataInsights #clinicalDecisionMaking]

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A very interesting use of #ai GPTs, on a tedious yet crucial task of #datascience.... The #dataanalysis! (even though the real added value is the visual, and the no code - node by node tool is not new...)

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Excited to announce that the preprint for my latest paper "Ensuring Responsible Sourcing of Large Language Model Training Data Through Knowledge Graph Comparison" in collaboration with Carlo Lipizzi has been released on arXiv. With the traditional "black-box" architecture of LLMs, understanding their training data becomes difficult due to a reliance on various metrics and intermediate components (perplexity, embeddings, etc.). We take a novel approach by considering content the LLM produces and avoid statistical methods, instead focusing on broad ideas through knowledge graphs. We utilize similarity metrics for content matching, and explore graph theory in the realm of large language models through a novel isomorphism metric to assess idea structure. We plan on submitting this paper to TACL, and hope that it advances the use of knowledge graphs and other visual knowledge representation techniques in large language models. Stay tuned. Link to the paper: https://lnkd.in/e4pEiD_2

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👉🏼 Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: a cross-sectional study 🤓 Giacomo Rossettini 👇🏻 https://lnkd.in/eg_9XUiC 🔍 Focus on data insights: - The study evaluated the accuracy of three AI chatbots (ChatGPT-4, Microsoft Copilot, Google Gemini) in predicting correct answers in the Italian entrance standardized examination test for healthcare science degrees. - Statistical analysis revealed significant differences in performance among the chatbots, with ChatGPT-4 and Microsoft Copilot outperforming Google Gemini. - Narrative coherence analysis indicated that logical reasoning was the predominant correct answer provided by the AI chatbots. 💡 Main outcomes and implications: - AI chatbots demonstrated promising accuracy in predicting correct answers in the standardized examination test. - ChatGPT-4 and Microsoft Copilot showed superior performance compared to Google Gemini. - The narrative coherence of AI chatbots primarily exhibited logical reasoning in their responses. 📚 Field significance: - Adoption of AI chatbots as educational tools in healthcare science programs may enhance learning experiences. - Candidates should cautiously integrate AI chatbots into their study routines to supplement traditional learning methods rather than replace them. 🗄️: #AI #chatbots #healthcare #education #datainsights

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🚀 Exciting News! 🚀 We are thrilled to announce that Kumo AI's position paper "Relational Deep Learning: Graph Representation Learning on Relational Databases," has been accepted at #ICML 2024! 🎉 Join us in Vienna from July 21-27, where our very own Matthias Fey will be presenting this exciting new research area. 🔗 https://icml.cc/ In collaboration with Stanford University, we introduce Relational Deep Learning (#RDL), a new research area for applying deep learning on relational databases by viewing them as temporal, heterogeneous graphs. This method eliminates the need for manual feature engineering, leading to more accurate models built faster. 🚀 We’ve also developed #RelBench, a benchmark suite to facilitate research in this new area, using diverse datasets from Stack Exchange discussions to Amazon book reviews. 📚 Stay tuned for more updates on our presentation format, and check out our paper here: https://lnkd.in/dk24vbSg #ICML2024 #MachineLearning #AI #GraphNeuralNetworks #RelationalDeepLearning #DataScience #KumoAI #StanfordCollaboration

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