AI Glasses teach in real time. While doing the job.
MACHINE LEARNING
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Context Breakdown: The Hidden Challenge in AI System Reliability
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Most AI systems don’t fail because of bad prompts.
They fail because context breaks at scale. If you’re building AI agents, LLM workflows, copilots, or automation, this is the layer that quietly decides whether your system is reliable or unpredictable. We’re hosting a 5-hour, -
Multimodal Sleep Foundation Model for Disease Prediction
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A multimodal sleep foundation model for disease prediction – Nature Medicine https://
buff.ly/W0r4qKh
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
FysicsWorld: First Unified Multimodal AI Benchmark
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With only one test, you could measure an AI's true mastery of sight, sound, and language together! But how? Fudan University & Fysics AI present FysicsWorld, the first full-modality benchmark. It's a unified test that pushes models to understand, generate, and reason across
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ScaledML Returns After 6 Years with ML Industry Foresight
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ScaledML is back! After a 6 year hiatus. Consistently 2-3 years ahead of where ML will be. Examples of foresight at SML:
– OpenAI in 2016,2017,2019 announced GPT-2 & RL efforts
– Turing award for Deep Learning announced by Turing award winner on morning of award
– Groq chip -
Modern Deep Learning Detectors: Why HOG Is Outdated
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Still using HOG for object or pedestrian detection? You probably shouldn’t.
— Satya Mallick (@LearnOpenCV) 7 janvier 2026
HOG was great years ago, but today it’s slow, fragile to viewpoint changes, and struggles with small or occluded objects.
Modern deep learning detectors in OpenCV — like U-Net (faces), YOLO, and… pic.twitter.com/YM3oDyF0XXStill using HOG for object or pedestrian detection? You probably shouldn’t. HOG was great years ago, but today it’s slow, fragile to viewpoint changes, and struggles with small or occluded objects. Modern deep learning detectors in OpenCV — like U-Net (faces), YOLO, and
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AI-driven detection of cyberharassment using graph studies
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Oui, c’est un move intéressant. Je dis « réducteur » parce que les chercheuses de l’I3S de Nice m’avaient expliqué que la détection du cyberharcèlement repose en grande partie sur des études de graphes et donc sur des données massives. Et oui, elles ne pouvaient pas accéder aux
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Keras API: Accessing JAX’s Full Power Without Reinventing
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The point of the Keras API is to let you tap into everything JAX has to offer but without reinventing the wheel
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Technical evaluation of AI-based harassment detection systems
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J’update mon message : apparement c’est basé sur la lecture des messages. Et non pas sur de l’harcèlement publique donc. Donc on détecte uniquement le harcèlement des messages. Ok c’est un choix. Réducteur, mais un choix. D’un point de vue technique c’est ok, mais ça veut
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Limitations of AI-based automated detection in current platform environments
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Personne ne me demande mon avis, mais je le donne gratuitement. C’est une bonne idée, mais malheureusement avec 9 ans de retard. La tech des plateformes aujourd’hui ne permet plus d’automatiser ce type de détection avec de l’IA. Seuls des chercheurs ont accès aux données, sous