5/ A key takeaway? Data is everything. The success of #GenerativeAI depends on having the right data strategy. At WAICF, AWS showed how AI + cloud + structured data = an unstoppable force for innovation. #MachineLearning #AIInnovation
MACHINE LEARNING
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The Hyper-Growth Of AI: What Leaders Need To Consider
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The Hyper-Growth Of AI: What Leaders Need To Consider
#AI #AIio #BigData #ML #NLU #Futureofwork @timnitgebru @oriolvinyalsml @ceobillionaire @soumithchintala @waitin4agi_ @sallyeaves @bernardmarr http://
ow.ly/rxgf30sK3mP -
Self-Data Distilled Fine-Tuning: Smaller LLMs Without Quality Loss
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How do you make large language models smaller and faster—without sacrificing quality? Use a novel approach – Self-Data Distilled Fine-Tuning (Self-Data FT) that Uses the unpruned model to generate a distilled dataset, preserving knowledge. Retains 91.2% accuracy on
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Snorkel embedding models fine-tuning webinar live demo
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Next up: fine-tuning embedding models with Snorkel’s experts. Join us online this Wed, Jan 19 for a live demo and Q&A session : https://
snorkel.ai/webinar/weekly
-demo-with-qa/
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Foundational approach to book with potential RL bonus material
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Thanks for the comment! I tried to keep the book more "foundational" rather than chasing the latest research (that's what I do in my blog). But yeah, maybe I can add some bonus material to the GitHub repo on implementing RL with verifiable rewards. (I don't want to do just
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Large Reasoning Models Excel at Competitive Programming Tasks
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Competitive Programming with Large Reasoning Models El-Kishky et al.: https://
arxiv.org/abs/2502.06807 #ArtificialIntelligence #DeepLearning #ReinforcementLearning -
31 Production-Ready RAG Implementations with LangChain
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Advanced RAG Techniques Discover a powerful collection of 31 production-ready RAG implementations, from basic to advanced, built with LangChain. Each technique comes with detailed documentation and practical code examples to enhance your RAG systems. Level up your RAG
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Puzzles as metric of human performance exceeding in AI
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smart humans can do very well on these puzzles, so it is a metric of exceeding/matching human performance. not unlike chess/go that came before it
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Scale AI Launches Enigma Eval Leaderboard and Research Paper
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Site: https://
scale.com/leaderboard/en
igma_eval
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Paper: https://
static.scale.com/uploads/654197
dc94d34f66c0f5184e/EnigmaEvalv4.pdf
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