Last chance to apply to the @StanfordAILab Postdoctoral Fellows Program! Join @Stanford
’s legacy of excellence in AI and machine learning, and collaborate with world-class experts in advancing cutting-edge research. Application ends today, April 30: https://
ai.stanford.edu/postdoctoralfe
llows/
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MACHINE LEARNING
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Stanford AI Lab Postdoctoral Fellows Program Application Deadline
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The Leaderboard Illusion: Rethinking AI Evaluation Benchmarks
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The Leaderboard Illusion Singh et al. : https://
arxiv.org/abs/2504.20879 #ArtificialIntelligence #DeepLearning #MachineLearning -
Length-Controlled AlpacaEval: Debiasing Automatic AI Evaluators
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Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators Dubois et al.: https://
arxiv.org/abs/2404.04475 #ArtificialIntelligence #DeepLearning #MachineLearning -
Eversource uses MATLAB to automate grid planning simulations
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@EversourceCorp is partnering with MathWorks to automate probabilistic load flow (PLF) for grid planning—cutting simulation time by 95%. See how @MATLAB is powering smarter energy systems: https://
spr.ly/60192QI6H #CleanEnergy #EVs -
Critique du classement LMArena : illusion des performances modèles
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There's a new paper circulating looking in detail at LMArena leaderboard: "The Leaderboard Illusion" https://arxiv.org/abs/2504.20879 I first became a bit suspicious when at one point a while back, a Gemini model scored #1 way above the second best, but when I tried to switch for a few
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DeepSeek Releases Prover-V2-671B Math Model
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DeepSeek just uploaded a new model — not R2, but DeepSeek-Prover-V2-671B, a powerful math model built on DeepSeek-V3.
Link: https://
huggingface.co/deepseek-ai/De
epSeek-Prover-V2-671B/tree/main
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MLOps vs LLMOps: Understanding Key Differences and Challenges
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MLOps ≠ LLMOps 🧠⚙️ LLMs come with unique challenges that traditional MLOps just can’t handle. In our latest blog, we break down: 🔹 MLOps vs. LLMOps 🔹 Lifecycle differences 🔹 Scaling challenges 🔹 Why LLMOps is essential now 📖 Read here: aryaxai.com/article/understa…
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RoboBee Achieves Safe Landings with Insect-Inspired Leg Design
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Une nouvelle étude de @SciRobotics met en évidence comment une conception et une stratégie de contrôle des pattes inspirées des insectes permettent au RoboBee de @Harvard d'exécuter des atterrissages sûrs, contrôlés et précis lorsqu'il vole entre les feuilles d'une plante.… pic.twitter.com/eGbRF8INdr
— VISION IA (@vision_ia) 30 avril 2025Une nouvelle étude de @SciRobotics met en évidence comment une conception et une stratégie de contrôle des pattes inspirées des insectes permettent au RoboBee de @Harvard d'exécuter des atterrissages sûrs, contrôlés et précis lorsqu'il vole entre les feuilles d'une plante.
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R-Sparse: Rank-Aware Activation Sparsity for LLM Inference
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R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference
Paper: https://
arxiv.org/pdf/2504.19449
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Code: https://
github.com/VITA-Group/R-S
parse
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Large Sensor Model Improves Wearable Exercise Activity Recognition
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We trained our Large Sensor Model (LSM) on over 40 million hours of de-identified multimodal sensor data from 165K users to demonstrate how it could improve performance in wearable tasks like exercise and activity recognition. Here’s what we found → https://t.co/zD3Tx7ShpO pic.twitter.com/5A2auMYgpd
— Google AI (@GoogleAI) 29 avril 2025We trained our Large Sensor Model (LSM) on over 40 million hours of de-identified multimodal sensor data from 165K users to demonstrate how it could improve performance in wearable tasks like exercise and activity recognition. Here’s what we found → https://
goo.gle/4fYDQJ4