MongoDB Atlas was named the most loved vector database 2 straight years by Retool. But I worry that we say things like "vector database" or "NoSQL database" and gloss over the fact that @MongoDB is the best database for *all* kinds of data: relational, unstructured, etc.
DATA
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GraphRAG-UI: User-Friendly Interface for RAG Text Indexing
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GraphRAG-UI
— AK (@_akhaliq) 2 octobre 2024
GraphRAG-UI is a user-friendly interface for GraphRAG, a powerful tool that uses the Retrieval-Augmented Generation (RAG) approach to index and query large text data. This project supports the latest version graphrag-0.3.3 and aims to provide a convenient management… pic.twitter.com/davBb07LcBGraphRAG-UI GraphRAG-UI is a user-friendly interface for GraphRAG, a powerful tool that uses the Retrieval-Augmented Generation (RAG) approach to index and query large text data. This project supports the latest version graphrag-0.3.3 and aims to provide a convenient management
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Mastering Category Encoders for Data Science
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Crack the Code: Mastering Category Encoders for Data Scientists: Image by Author | Canva In data science, handling different types of data is a daily challenge. One of the most common data types is categorical data, which represents attributes or… https://
kdnuggets.com/crack-the-code
-mastering-category-encoders-for-data-scientists?utm_source=dlvr.it&utm_medium=twitter&utm_campaign=crack-the-code-mastering-category-encoders-for-data-scientists
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MIT Researchers Introduce Generative AI for Databases
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MIT researchers introduce generative AI for databases
#AI #AIio #BigData #ML #NLU #Futureofwork @briansolis @Xbond49 @VitalikButerin @DataScienceCtrl @Benioff @johnnosta @IrmaRaste @Tiffani_Bova http://
ow.ly/Lvrk30sGwRX -
ML Transforms Business Decisions Through Data-Driven Insights
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Machine Learning accelerates business decision-making by turning raw data into actionable insights. From predictive analytics to automated processes, companies harnessing ML are staying ahead in an increasingly data-driven world. #infographic by @DataFlairWS via @antgrasso #AI
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Databricks SQL Enhances Data Warehouse with AI-Powered Features
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Over 7k customers use #DatabricksSQL as their data warehouse. We recently added new features & performance improvements that make it simpler, faster & lower cost. Including:
— Databricks (@databricks) 1 octobre 2024
– AI-powered performance
– AI Assistant for SQL Analysts
– AI Functions updates
https://t.co/Rg8870WUIo pic.twitter.com/sfbUtH3pbPOver 7k customers use #DatabricksSQL as their data warehouse. We recently added new features & performance improvements that make it simpler, faster & lower cost. Including: – AI-powered performance
– AI Assistant for SQL Analysts
– AI Functions updates https://
dbricks.co/3XGxZSh -
MPMC Points Beat Low-Discrepancy Methods in Computational Finance
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In computational finance, for example, simulations rely heavily on the quality of the sampling points. In a classical problem from the field in 32 dimensions, MPMC points beat previous state-of-the-art low-discrepancy sampling methods by a factor of 4 to 24.
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AI Advances Point Distribution Uniformity with L2-Discrepancy
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Using AI to generate highly uniform points can be hard because the usual way to measure this is slow to compute & hard to work with. To solve this, the team switched to a quicker, more flexible uniformity measure called L2-discrepancy. For high-dimensional problems, they
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MIT’s Graph Neural Networks Optimize Data Point Distribution Uniformly
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How can we distribute data points more uniformly for multi-dimensional problems in robotics, finance, & computational science? MIT CSAIL’s AI-powered method uses graph neural networks to do this, self-optimizing to avoid clumps & voids. This "Message-Passing Monte Carlo
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MPMC Graph Neural Networks Improve Data Point Uniformity
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MPMC uses graph neural networks to allow points to "communicate" for better uniformity.
— MIT CSAIL (@MIT_CSAIL) 1 octobre 2024
Often, the more evenly you can spread out those data points, the more accurately you can simulate complex systems. pic.twitter.com/sfiYLge84yMPMC uses graph neural networks to allow points to "communicate" for better uniformity. Often, the more evenly you can spread out those data points, the more accurately you can simulate complex systems.