Thank you ICLR! Super glad that we'll be continuing this partnership!
MACHINE LEARNING
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Global Leader Labels 300K Examples 16x Faster with AI
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In 3 weeks, a global credit leader labeled 300K+ examples 16x faster than manual efforts Boosted AI accuracy by 6+ points Scaling high-quality, reliable output Let’s unlock results like this for your models #AI #SnorkelAI #MachineLearning
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Synthetic Data for AI Fraud Detection: Stanford HAI Seminar
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Join us for this week's @StanfordHAI seminar with IBM Researcher Erik Altman, where we will explore use cases for synthetic data sets (SDS), designed to train AI to detect fraud and other criminal activities. https://
stanford.io/43hXsEb -
Meta Perception Encoder: Advanced Vision Model for Image and Video Tasks
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Introducing Meta Perception Encoder: a vision encoder setting new standards in image & video tasks. It excels in zero-shot classification & retrieval, surpassing existing models.
— AI at Meta (@AIatMeta) 6 mai 2025
Learn more about Meta Perception Encoder, read the research paper, and download the code and dataset… pic.twitter.com/EQVkSVZwgYIntroducing Meta Perception Encoder: a vision encoder setting new standards in image & video tasks. It excels in zero-shot classification & retrieval, surpassing existing models. Learn more about Meta Perception Encoder, read the research paper, and download the code and dataset
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Jason Wei’s Top 10 AI Research and Development Preferences
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My personal preferences: 1. ChatGPT
2. Math competitions
3. Test-time compute
4. Specialist models (IK, I flipped on this one)
5. Principled science
6. Reinforcement learning
7. Higher-quality data
8. Scientific paper
9. Open source
10. Yes!
Bonus: Ilya Sutskever -
Ten Binary Questions Revealing AI Researcher Preferences
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Ten binary-choice questions that reveal a lot about your taste as an AI researcher. What are your preferences? 1. ChatGPT or Claude?
2. LMSYS or math competitions?
3. More training compute or more test-time compute?
4. Single general model or many specialist models?
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Google’s Gemini 2.5 excels in coding tasks
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BREAKING 🚨: Google released an updated gemini-2.5-pro-preview-05-06 model that tops web dev arena and excels at coding tasks. Now available on both Gemini and AI Studio. https://t.co/S8P1PHJ7ok pic.twitter.com/xtMZEUTsVq
— 🚨 AI News | TestingCatalog (@testingcatalog) 6 mai 2025BREAKING : Google released an updated gemini-2.5-pro-preview-05-06 model that tops web dev arena and excels at coding tasks. Now available on both Gemini and AI Studio.
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Best Course to Learn Up-to-Date RAG Techniques
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“Best course I found to learn up-to-date RAG techniques.” — Patrick Drechsler (Data Scientist)
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RAG Pipeline Building and Assessment Course Praised
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“Insightful, practical and very thorough…the course greatly expanded my knowledge of building and assessing RAG pipelines.” — Eoin McGrath (Machine-Learning Engineer)
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Launch of Computer Use in smolagents with Qwen-VL grounding
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We're launching Computer Use in smolagents! 🥳
— m_ric (@AymericRoucher) 6 mai 2025
-> As vision models become more capable, they become able to power complex agentic workflows. Especially Qwen-VL models, that support built-in grounding, i.e. ability to locate any element in an image by its coordinates, thus to… pic.twitter.com/mI8MuWZkISWe're launching Computer Use in smolagents! -> As vision models become more capable, they become able to power complex agentic workflows. Especially Qwen-VL models, that support built-in grounding, i.e. ability to locate any element in an image by its coordinates, thus to