Ondrej Vanek

Where Do You Still Need Machine Learning? (Guest: Srujana Kaddevarmuth) https://buff.ly/4eWRj4a via Andreas Welsch of Intelligence Briefing on Thinkers360 #AI #DigitalTransformation #GenerativeAI

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#EarlyCareer #talk at #IJCAI2024 by🗣️Julia Neidhart, Technische Universität Wien: Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values ➡️https://lnkd.in/dK3Nieyr Abstract: Recent advancements in recommender systems highlight metrics beyond accuracy, including diversity, serendipity and fairness. This paper examines the integration of Large Language Models into recommender systems, focusing on challenges such as interpretability, bias, and user autonomy. LLMs offer enhanced personalization capabilities but also introduce concerns regarding transparency and ethical implications. This work discusses ongoing research efforts aiming to develop transparent, fair, and contextually aware systems. Our approach seeks to prioritize user wellbeing and inclusivity, contributing to a more equitable and functional digital environment through advanced technologies and interdisciplinary insights. #XAI #LLMs #InclusiveTech #NextGenAI #AI TU Wien Informatics

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Building a Knowledge Graph and using it as a #GraphRAG for your #GenAI Apps has proven to noticeably enhance the robustness and correctness of LLM generations. Check the below knowledge graph builder using LLMs to make this task easier! https://lnkd.in/dNxpiUfU

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👾 According to my personal dataset - there is not more than a 7% chance that your certificate on course completion will be A4 Portrait, and in general, you should expect A4 Landscape :) 👾 However, my data can be biased and impacted by regional specifics and personal preferences on course selection criteria. 👾 Talking seriously, I am sharing my certificate on 𝗚𝗲𝗻 𝗔𝗜 𝗧𝗵𝗲𝗼𝗿𝘆, 𝗔𝗴𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 from the 𝗢𝘅𝗳𝗼𝗿𝗱 𝗦𝘂𝗺𝗺𝗲𝗿 𝗠𝗟 𝗦𝗰𝗵𝗼𝗼𝗹 by 𝗔𝗜 𝗳𝗼𝗿 𝗚𝗹𝗼𝗯𝗮𝗹 𝗚𝗼𝗮𝗹𝘀. 👾 This was a great opportunity to hear from people from 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱, 𝗔𝗪𝗦 𝗔𝗜 𝗟𝗮𝗯𝘀​, and representatives from many other organisations and institutions. And to learn more about applications and to expand my thinking. PS: I promise to avoid Gen AI buzz-wording. PPS: I prefer to say Prompt Crafting rather than Prompt Engineering. PPPS: I am not using AI to write my posts. Because I love writing more than prompting. #OxML #OxfordML #AI4GlobalGoals #AI4GG #ml #dl #representationlearning #genai

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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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Welcome to read the article "Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets" which is written by Mr. Markus Schwegler, Prof. Christoph Müller and Alexander Reiterer. This excellent article was published in issue 7 in 2023 and has been viewed 1313 times and cited 3 times! Article link: https://lnkd.in/gMCsed5z MDPI Fraunhofer IPM Furtwangen University Albert-Ludwigs-Universität Freiburg (Albert Ludwig University of Freiburg) #pointcloud #neuralnetwork #integratedgradients

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Today we invite you to watch a recording of Contributed Talks from the ML in PL 2023 conference, where Błażej Osiński talked about: Toward Efficient Machine Learning For Sequential Decision Making. https://lnkd.in/datBvHb8 In this talk, I will present my personal journey toward developing efficient machine learning methods for sequential decision making. This journey spans both academia and industry, tackling diverse challenges that range from Atari games to autonomous driving and robotics. Why should we care about efficient methods for sequential decision making? If we observe the progress of artificial intelligence over the last few decades, it has managed to ascend what were perceived as pinnacles of human intelligence. From chess and Go to StarCraft and No Limit Texas Hold'em, machines have toppled human champions. The emergence of large language models extends this progress to more open-ended challenges. Despite these successes, when it comes to physical interactions with the real world, the achievements of AI are less remarkable. For instance, despite three decades of research, multi-fingered robotic hands are still far less dexterous than those of an average human child. The comparatively slower advancements in the realm of physical interactions can largely be attributed to the massive data requirements of contemporary machine learning methods. Advancements in efficiency could pave the way for truly practical robots capable of alleviating our household chores, rather than just beating us in games or eyeing our jobs. Błażej is a machine learning researcher with experience in sequential decision-making problems (reinforcement learning, imitation learning). He is about to graduate with a Ph.D. from the Uniwersytet Warszawski. He has divided his time between academia and industry, having affiliations with institutions such as Lyft Level 5, Google Brain, and the University of California, Berkeley. He is now pivoting to the ambitious endeavor of explaining cellular biology with machine learning working for Altos Labs.

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Microsoft has just release their graphRAG solution as open source a few days ago.   Retrieval-Augmented Generation is a technique that enables LLMs to enrich their output with data that was not part of the training corpus or was not yet available during the training of the model. RAG or Baseline RAG uses vector embeddings to represent chunks of text and vector similarity as the search technique. While these embeddings capture semantic meaning, they lose structural information about how different pieces of information related to each other.   This is exactly where Knowledge Graph excel and therefore GraphRAG aims to leverage knowledge graphs to create a weave of information that captures broader context or connections between different pieces of information and can be used as input to the LLM to improve its capability to reason about private data and better answer to complex questions. As AI continues to evolve, approaches like GraphRAG will be crucial in unlocking the full potential of LLMs for businesses so to see Microsoft release their GraphRAG implementation as open source its very positive and exciting. You can find more information on Microsoft´s GraphRAG solution as well as the GitHub link on the following page - https://lnkd.in/dZfZGiqW   #RAG #GraphRAG #RetrievalAugmentedGeneration #EnterpriseAI #BusinessIntelligence #AI #MachineLearning #KnowledgeGraphs #LLM #DataScience

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The second article in our series, written by Paweł Strzałkowski, is now available on the blog! 📝 In this post, he explores the practical implementation of Vector Search - a technique for finding items based on semantic similarities rather than exact string matches. Paweł covers the theory behind Vector Search, discusses its application in image recognition, and shares a complete photo search application - all developed in Ruby. Enjoy reading! 💫 https://lnkd.in/dT3s6V98

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#ICYMI From data-oriented to domain-centric: explore the spectrum of #Java persistence patterns with Otavio Santana’s #InfoQ article: https://bit.ly/48he8vI Delve into Driver, Mapper, DAO, Active Record, and Repository for robust architectural foundations. #SoftwareDevelopment #SoftwareArchitecture #Frameworks

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AI implementations typically fail. The recent RAND report suggest that over 80% of AI projects fail, with other sources indicating the failure rate could be as high as 90%. This is significantly higher than the failure rate for traditional IT projects. Riga Business School, together with Latvian IT cluster, EnterCreative and Ventspils High Technology Park, offers an innovative #EU-funded training program “Development of digital skills of EDIC companies”. I was honored to be invited to teach several courses as part of program. One of these courses is a 3-day #PracticalAI workshop. My goal is not to overwhelm participants with the intricacies of AI but to help them improve the success rate of AI implementations. Drawing from my 25 years of experience as a partner at Deloitte, I have designed this course from scratch, focusing on both the failures and successes I have witnessed in organizations, within and outside AI realm. Build or buy AI? How to select tech? How to manage AI vendors and consultants? Whom and where to hire? How to compensate? How to phase implementation? What are KPIs? What happens now with your ERP? With your DWH?  How implicit corporate knowledge gets into AI?... Direct answers to direct questions. If you are interested, register to the course: https://lnkd.in/d2q-SPYF #AI #ERP #DWH Janis Paksis

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What Karl says. There's going to be two amazing talks about Hopsworks based #ai and #ml systems: * Build a personalized Bitcoin (BTC) virtual assistant in Python with Hopsworks and LLM function calling by Javier de la Rúa Martínez (https://lnkd.in/eQfWpFRU ) * Build TikTok's Personalized Real-Time Recommendation System in Python with Hopsworks by Jim Dowling (https://lnkd.in/eXEHePjG) If you are in the #berlin area next week - go and take a look!

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#DataMasking is not easy. As you can see from several posts from the #SyntheticData world, they assume that anonymizing data is not possible - we Mage Data beg to differ. We have been successfully implementing solutions across global giants, passing penetration tests, getting patents, etc. But we also have fun. Here is a simple example - when Data Masking works, it has to create useful data - useful for the purpose that it is being generated. But it should also be irreversible - so it cannot be used in data theft. We call it "Perfectly Useful, Entirely Useless" (tm) data. Just created a small video to illustrate why old data masking approaches either made the data useless, or left it too easy to reverse. In my example, all identifiers - whether direct, or indirect; whether obvious, or not - were identified and anonymized. Please share your comments! Would love to hear your thoughts, and learn more. #Anonymization #GDPR Shout out to our friends Kalaido.ai - we used Mage Data to anonymize the data, and then Kalaido to generate images - great tool, and we are having tons of fun with it.

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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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Top 3 articles and my 480 personal hands-on hours with ingenuity (effortless), beyond primary works, on GenAI in last 5 months: mostly all hand-picked topics have been written only after practical deployments, executions and validations. Despite of my quite busy schedule throughout this year on delivery excellence case (financial services client), cloud transformation delivery (automotive client), client related travels, Azure cloud (global chief role), GenAI (thought leadership), Inventions creation (patents), Mentoring, Azure global team & community leadership, new Inventions’ reviews (EU, UK cloud sw IDT chair) and creating new Inventors (Master Inventor) - I have written 15 original articles in last 2 quarters on various aspects of GenAI based on my practical hands-on, market pulse, clients’ and readers’ interest. This work took 480 hours of my personal time (mostly nights) - resulted into more wisdom across the spectrum of modern digital transformation. I discovered various ways to fail and unlocked more mantras to succeed - as I drive enterprises to achieve production scale. 1. 💡Prompt Engineering, a practical hands-on approach: https://lnkd.in/eqbsf-mg 2. 💡AI LLM Orchestration Framework: https://lnkd.in/exBrjTRr 3. 💡LLaMA 2, Theory and Hands-on: https://lnkd.in/e8JZRcdV #largelanguagemodels #artificialintelligence #generativeai #promptengineering #llmops #machinelearningengineer

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