Patrick Gebert

📅 On 07.11.2024, we invite you to the ScaDS.AI Dresden/Leipzig training "Big Data Processing on HPC" with Apurv Kulkarni and Wenyu Zhang. Apache Spark and Apache Flink are two typical #BigData analytics frameworks. Their APIs allow the development and testing of an application on a local workstation and later, without changing the source code of the application, distribute work to many computers when the local workstation is not sufficient anymore due to limited resources. The course focuses on the step from a local workstation to an #HPC environment and presents how the typical Big Data analysis workflow can be organized in an HPC environment. In this course participants will be introduced to running a data pipeline and data processing along with managing the configurations on the HPC environment, using Apache Flink and Apache Spark. Register here: 👉 https://lnkd.in/eYfmiMT8

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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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🔎 𝗫𝗔𝗜 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱 - 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗿𝗮𝗰𝗸 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: Debugging and improving models with xai 🖥️ ✏️ 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: @christopher j anders Sebastian Lapuschkin Frederik Pahde Maximilian Dreyer Lorenz Linhardt 🔗 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲 𝗛𝗲𝗿𝗲: https://lnkd.in/dz2UZf9T This track aims to debug, modify, and evaluate machine learning models to enhance their performance and reliability using eXplainable AI (XAI) techniques. The topics include, but are not limited to, (1) identifying and mitigating the effect of learned spurious correlations (also known as Clever Hans effect or shortcut learning) and distribution shifts in the data, (2) diagnosing failure modes and edge cases leading to undesired model behaviour, and (3) improving models through targeted corrections. For each of these points, XAI methods serve as a critical foundation, as they enable to understand model decision strategies (and their flaws), and to identify critical parts of the model or data set that are most amenable to improvement. Additionally, this track covers the development and application of rigorous evaluation methods for verifying that the enhancements achieve the intended outcomes. Join us to discuss best practices, emerging tools, innovative techniques, and case studies showcasing how thoughtful model refinement can lead to fair, robust, and responsible AI solutions. #XAI #debug #machinelearning #spurious #correlations #cleverhans #understanding #model #decisions #artificialintelligence

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AnchorGT: A Novel Attention Architecture for Graph Transformers as a Flexible Building Block to Improve the Scalability of a Wide Range of Graph Transformer Models Quick read: https://lnkd.in/gFiQyv8a Paper: https://lnkd.in/gsJqpi5F #machinelearning #ai

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The Rails 8 feature set perfectly complements SQLite's power in creating resilient, high-performance production apps, but still the question lingers: Can I really go all-in on #SQLite? Join Stephen Margheim at #RailsWorld as he illustrates how to leverage Rails and SQLite's full potential in your next venture, and when SQLite does and doesn't make sense for your application. https://lnkd.in/eaBstScy

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Here is this month’s list of where you can find us for more impactful sessions! Check us out and stay tuned for more details + how to register. 📍 VoxxedDays CERN in Geneva, Switzerland: “TrashTalk - Exploring the JVM memory management” January 14th, 2025 📍 JUG Oberpfalz, Germany: “TrashTalk - Exploring the JVM memory management” January 21st, 2025 📍 PrimeForce Webinar, Zurich, Switzerland: “Wargames - Java vulnerabilities and why you should care” January 28th, 2025 #AzulEvents #JVM #Java #JavaDeveloper #JavaVulnerabilities #Javasupport

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🚀 𝗢𝘂𝘁 𝗻𝗼𝘄: 𝗧𝘄𝗼 𝗵𝗲𝗶𝘀𝗲 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀 𝗼𝗻 𝗟𝗟𝗠 Alexander Schatten, senior researcher at SBA Research, and Marco Jeppe Reiser have published an interesting article: 📰 𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗮𝘁 𝗽𝗿𝗼𝗺𝗽𝘁 𝗹𝗲𝘃𝗲𝗹 𝗹𝗲𝗮𝗱𝘀 𝘁𝗼 Where can AI systems really help programmers, and is the approach taken here truly novel? This article examines their role. Programming involves interpreting real-world problems and processes, abstracting them, and then translating them into a deterministic language understood by technical systems. The goal is to automate tasks and processes and interact with people or other systems as needed. Besides understanding the real-world problem, programming also requires technical consideration of advantages and disadvantages, costs, legal frameworks, dependencies, and more. Actual coding is only a small part of the whole process. Alexander Schatten's second article on heise online explores the role of LLMs in software development. 📰 𝗟𝗟𝗠𝘀 𝗶𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗯𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗳𝗮𝗶𝗹𝗲𝗱 Software systems are a combination of hardware, software, and the people who develop, maintain, and use these systems. This interaction of the three factors creates benefits and the possibility of further system development. Early on, software exhibited characteristics not observed in other technical systems. The first software crisis in the 1960s revealed that hardware and software capabilities were growing faster than those of human engineers, a crisis from which we have not yet fully emerged. One reason is the ever-increasing complexity of software itself and the complexity created by software and its increasing networking with other systems, affecting software further. Get the full articles here:   https://lnkd.in/gA2hHCBb   https://lnkd.in/giAV6f8C    Both articles will soon be published in iX magazine. 📷: Marcel Lehner 

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In this tutorial, Hari Subhash demonstrates how to create a 1.2m node graph network - while running computational expensive analytical queries every 5 seconds using just an XS CPU only instance of Kinetica. All using SQL, relational tables, and without complex data engineering (or the need for a separate graph database). 👉 https://lnkd.in/eqxKZ_D9 #graphdatabase #graphdatascience #graphsolvers #shortestpath #kafka

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baiosphere publishes the #MunichAILecture by Professor Alexei Efros of the University of California, Berkeley. Efros has made a name for himself by optimising #machinelearning methods and researching #unlabelleddata used for #computervision or #patternrecognition. Efros gave his lecture in Munich under the thesis that far too little credit is generally paid to data, but listen for yourself: https://lnkd.in/dbFhqKXd #bigdata, #artificialintelligence, #ai, #KI, #artificialintelligence, #research, #researchwithKI, #IT4Science, #discoverLRZ Artificial Intelligence News, Artificial Intelligence, Hochschule Bayern e.V., Universität Bayern e.V., Digitalverbund Bayern, Erlangen National High Performance Computing Center (NHR@FAU), FAU Erlangen-Nürnberg, Technische Universität Nürnberg, TUM.ai, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Data Science Women, Data Science AI Learner Community, IT4Innovations National Supercomputing Center

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Join us as we unpack the future of AI with Carsten Kraus in this follow-up article from Launch's #NavigatingAbroad series. Learn about the impactful role of small language models in making AI more accessible and the intricate dynamics of reinforcement learning. What implications will these technological advances have on the future landscape of AI? #AIUnpacked #FutureOfAI #LanguageModels #ReinforcementLearning #TechInnovation

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Red Dot vs. Micro Prism Though similar in appearance and basic function, it’s what's inside our Liberator II and Raider 1x that separates them. Learn more on our blog in our latest head-to-head comparison. 🔴 https://lnkd.in/gqjF5qzT

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🤠 Wrangling petabytes of unstructured data and metadata for #AI is a huge IT challenge! We're here to help. The backbone of every storage system is its #metadata database and efficiently managing #UnstructuredData is business-critical. 💡 The powerful integration of GRAU DATA GmbH MetadataHub + #ScalityRING empowers IT teams and developers to handle massive volumes of unstructured data, making it AI-ready. Transform how unstructured data and its associated metadata are managed to make it actionable with: ✳️ faster data access, ✳️ more visibility, ✳️ unlocked AI-ready workflows, ✳️ and improved AI outcomes. *************** Get the details in our blog: https://bit.ly/40t6JbI ************** P.S. Scaling metadata is one of the #10DimensionsOfScale that every storage architecture should be required to handle. You can learn more about #MultidimensionalScaling and #RedefiningScale for AI in the blog.

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The gap to existing state-of-the-art LLMs Extended Long Short-Term Memory (xLSTM), a type of neural network model designed to process sequences of data, such as in language modeling. At the ICLR 2024, Sepp Hochreiter and team unveiled the xLSTM, a new AI architecture. It introduces new features like exponential gating and updated memory structures to improve performance over traditional LSTMs and compete with newer models like Transformers. The xLSTM achieves better results by enabling parallel processing and using advanced memory techniques. This makes it more efficient, particularly for tasks involving long sequences of data.

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📅 Mark your calendars for September 18th in #Vienna! At the #DataScience MeetUp, Andreas from Neo4j and Sebastian will talk about the relevance of #KnowledgeGraphs for Gen #AI, and explain why proper #GraphVisualization is critical for understanding your #data. We look forward to seeing you there! https://lnkd.in/e-EKZ9fZ

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Want to excel as a machine learning engineer? In his recent video Boris Meinardus a student researcher at TU Darmstadt, summarizes the seven most important skill sets based on insights from ML engineers at top companies like Google, Meta, NVIDIA, and OpenAI. Key Skills: 1. Programming Skill 2. Mathematics 3. Soft Skills 4. Familiarity with Core Libraries 5. Machine Learning Knowledge 6. Model Evaluation 7. Software Engineering Skills

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