What if your AI agent could learn faster from older data without forgetting or crashing? Researchers from Sany Group present GIPO (Gaussian Importance Sampling Policy Optimization). Instead of hard clipping, it uses a soft Gaussian trust weight to gently limit extreme updates
MACHINE LEARNING
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Nature article warns against using AI as student ghostwriter
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Don’t let your students use #AI as a ghostwriter
by Yanjun Shen @Nature Learn more: https://
bit.ly/41QqUjd #MachineLearning #ArtificialIntelligence #EdTech -
Local models’ progress from a year ago is amazing
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We've come so far from a year ago ngl People used to tell me it's impossible for local models to be where they're now They have no idea how good this thing will be
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EvalVerse: Expert-calibrated VLMs evaluate AI video quality
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Can AI-generated video ever match professional cinema? A team from HKUST, Tencent, and Stanford introduces EvalVerse. It treats video evaluation as a science—using expert-calibrated vision-language models to judge not just if the output follows the prompt, but also its
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Training NLP Models on Amazon Reviews with Python Libraries
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Training AI on Amazon Electronic Reviews Using #Python for Natural Language! – by – @gp_pulipaka
! JupyterLab/Jupyter Notebook WordNet, Lexical Semantic Relation Analyzer
Thesaurus, 155,000 Words
Synset 115,000, 205,000 word-Sense Pair. NLTK Library, spaCy, TextBlob -
GeoAI Machine Learning Predicts Traffic Patterns
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GeoAI! #MachineLearning Help Predict Traffic Carmageddon! by @rachelevagordon. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #GeoSpatial #Linux
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DeepMind LLMs Lean Proof-Search Agents Solve Open Problems
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This new DeepMind research turns LLMs into Lean proof-search agents, so every step must compile and the final proof is mechanically verified. Under this setup, they solved 9 open Erdős problems, proved 44 OEIS conjectures, and helped advance actual research in optimization,
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D-OPSD: Fine-Tuning Fast Models Without Losing Speed
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What if fine-tuning a fast image model didn’t break its speed? Researchers from HKUST, Alibaba Group, UCSD, and CUHK present D-OPSD. It lets step-distilled diffusion models learn new styles or concepts without losing their few-step magic—by having the model act as both
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Free Roadmap for Becoming an AI Researcher
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DROP EVERYTHING The ultimate step-by-step projects roadmap for BECOMING an AI Researcher is now available online to read FOR FREE Covers building – Tokenizers / embeddings
– Positional methods
– Attention / multi-head attention
– Transformer blocks
– Training loops / -
Self-Play RL vs Static Bug Generation in SWE Training
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The key distinction: Synthetic bug generation is static. Self-play SWE-RL is online. The bug generator and bug solver improve together, so the curriculum changes as the model changes. That is what makes this much more interesting than simply manufacturing more training data.