no, it’s determined by the data, ur missing the point
DATA
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NVIDIA RAPIDS Accelerates Pandas Performance for Data Science
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Speed up your #DataScience with NVIDIA GPUs! Dive into our blog "Crush Pandas Speed Barriers on Domino" to see how NVIDIA's RAPIDS boosts Pandas efficiency. #AI #NVIDIA #RAPIDS #DominoDataLab https://
domino.buzz/3NbDaEa #DataScience #AI #NVIDIA #RAPIDS #DominoDataLab -
AI Impact on Traditional Industries: Transformation and Innovation
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The Impact of AI on Traditional Industries https://
linkedin.com/pulse/impact-a
i-traditional-industries-nicolas-babin-2ivietrackingId=E34YFCyjTp20rnsP1T5TBw3D3D/
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#ArtificialIntelligence #innovation #technnology #industry #data @jblefevre60 @Ym78200 @kalydeoo @ipfconline1 @BetaMoroney @LaurentAlaus @PawlowskiMario @CurieuxExplorer @Shi4Tech @smaksked @IanLJones98 @3itcom -
Vector Embeddings vs Word Token Embeddings Distinction
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I’m talking about vectors that are the output of vector-encoding models, commonly called “embeddings”. not word/token vectors, also called embeddings, which you’re thinking of
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Embeddings Model Output vs Token Embeddings Clarification
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great question; when I tweet about this, I will always be talking about the output of an embeddings model; if I mean token embeddings I will specify
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AWS and Snorkel Partner to Unlock Data Value
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We couldn't be more excited to be diving in with @awscloud to unlock the value of data as your differentiator. @SwamiSivasubram congratulations on incredible announcements at this year’s Reinvent. Thank you for hosting @henryehrenberg
, @ajratner
, & the Snorkel team. -
Evaluating Multi-Modal Retrieval-Augmented Generation Systems
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Evaluating Multi-Modal Retrieval-Augmented Generation https://
bit.ly/49Xe6dB
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
RAG relevance for questions using many distant document elements
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RAG can handle all sorts of different questions, it's really appropriate for extracting information from a document. An interesting question is "when the question requires using more many distant elements from doc, is RAG still relevant?"
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RAG retrieval failures due to random variations, need more tests
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Well spotted!
Both variations (blue and green ) are also due to random variations. For instance the RAG fails to retrieve relevant snippets on 1 single example. The tests I ran were not numerous enough yet to smooth these variations out. But maybe I'll run more. -
RAG system reduces token input from 128k to 2k
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Exactly!
To complement on the end: thanks to the RAG system, the model was fed around 2k tokens each time, down from the 128k tokens of the original document.