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MACHINE LEARNING
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Training on Test Labels vs Inputs: ARC Benchmark Ethics
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Training on test refers to using the *labels* in the test set. Not the *inputs*. Including test inputs is standard common in semi-supervised/self-supervised. Whether or not this approach is against the ARC rules, I don't know. But it is *not* what "training on test" means.
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AI Economy Impact on Data Privacy: Threat or Solution?
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Will The AI Economy Strike The Final Blow To Data Privacy Or Save It?
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @lexfridman @sama @kaifulee @ID_AA_Carmack @karpathy @2morrowknight @ylecun -
Unsupervised Learning Confusion: Training on Test Data Explained
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Man discovers unsupervised learning and confuses it with what "training on test" actually means.
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How AI Models Learn: Debunking Data Reproduction Myths
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I’m tired of explaining how AI models learn. They don’t copy or reproduce data in the way you think they do.
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Stanford Hiring Data Science Postdocs in Causal Inference
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We're hiring data science postdocs! Work on interdisciplinary projects in causal inference, ML, econometrics, and more. Recent PhDs with coding expertise and cross-disciplinary experience are encouraged to apply: https://
datascience.stanford.edu/programs/data-
science-fellows
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Computer Vision: Much More Than Just Artificial Intelligence
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Computer Vision isn’t “just AI.”
— Satya Mallick (@LearnOpenCV) 19 décembre 2025
It’s how machines interpret visual data—from cameras to X‑rays to telescopes—solving tasks like detection, recognition, OCR, and 3D reconstruction. #ComputerVision #AI #MachineLearning #DeepLearning #ImageProcessing pic.twitter.com/KhVODmTBBUComputer Vision isn't "just AI."
It's how machines interpret visual data—from cameras to X‑rays to telescopes—solving tasks like detection, recognition, OCR, and 3D reconstruction. #ComputerVision #AI #MachineLearning #DeepLearning #ImageProcessing -
Is Gemini 3 Pro the model behind this reasoning?
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If reasoning – is it Gemini 3 Pro in this case?
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Increasing Context Length and Model Capacity: Expensive Trade-offs
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Either
a) you increase the context length and/or let the model crawl through stored data
b) you start of with a model that has large enough capacity and periodically update the weights
Both options are expensive, so I am not sure they actually want this right now. -
Mixture of Experts Models: Current Perspectives and Efficiency
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Poll: How Are You Thinking About Mixture of Experts (MoE) Models Today? MoE architectures scale model intelligence by routing each request to a subset of specialized “experts,” improving efficiency. Which best matches your current perspective?