deepmind just published something wild they built an AI that discovers its own reinforcement learning algorithms. not hyperparameter tuning. not tweaking existing methods. discovering ENTIRELY NEW learning rules from scratch. and the algorithms it found were better
MACHINE LEARNING
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2D Gaussian Splatting: Real-Time Neural Rendering and Reconstruction
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🚀2D Gaussian Splatting: Real-Time, Geometry-Aware Radiance Field Reconstruction
— Satya Mallick (@LearnOpenCV) 29 octobre 2025
In this week’s deep dive, we unpack how 2D Gaussian Splatting (2DGS) redefines the future of real-time neural rendering and reconstruction. By collapsing volumetric 3D Gaussians into surface-aligned… pic.twitter.com/FBuCopT64x2D Gaussian Splatting: Real-Time, Geometry-Aware Radiance Field Reconstruction In this week’s deep dive, we unpack how 2D Gaussian Splatting (2DGS) redefines the future of real-time neural rendering and reconstruction. By collapsing volumetric 3D Gaussians into surface-aligned
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PyTorch Deep Learning Certificate Now Available at DeepLearning.AI
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An exciting new professional certificate: PyTorch for Deep Learning taught by @lmoroney is now available at https://t.co/R0m408f8CA. This is the definitive program for learning PyTorch, which is one of the main frameworks researchers use to build breakthrough AI systems. If you… pic.twitter.com/zZp6Socoyi
— Andrew Ng (@AndrewYNg) 29 octobre 2025An exciting new professional certificate: PyTorch for Deep Learning taught by @lmoroney is now available at http://
DeepLearning.AI. This is the definitive program for learning PyTorch, which is one of the main frameworks researchers use to build breakthrough AI systems. If you -
Model Introspection: Comparing Intent Against Actual Output
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This reveals a mechanism that checks consistency between intention and execution. The model appears to compare "what did I plan to say?" against "what actually came out?"—a form of introspective monitoring happening in natural circumstances.
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AI Models Demonstrate Deliberate Cognitive Control Mechanisms
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We also found evidence for cognitive control, where models deliberately "think about" something. For instance, when we instruct a model to think about "aquariums” in an unrelated context, we measure higher aquarium-related neural activity than if we instruct it not to.
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Visualizing Llama 3.1 Tensor Operations Through GGML Debug Output
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The three.js visualization parses the output of llama-cpp's ggml debug output (of unsloth llama 3.1) to directly obtain all the tensor calculations happening under the hood. Operations (MUL_MAT, ROPE, RESHAPE, ADD) are grouped into query, key, value, MLP, and residual stream
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Compute Trade-offs: Factual Knowledge vs Dense Core Efficiency
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There are two kinds of people in AI: those who are happily surprised a model would know random facts like these (worth burning compute and parameters), and those who think it's a complete waste of both for a dense core.
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Jeff Dean Praises Team Pushing Machine Learning Boundaries
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I love working with members of this team, who are pushing the boundaries of machine learning!
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DeepSeek-OCR OmniDocBench: Document Recognition Benchmark
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Detailed report + code: https://
github.com/alphaXiv/DeepS
eek-OCR-OmniDocBench/blob/main/REPORT.md
… Datasets page: http://
alphaxiv.org/datasets/shang
hai-ai-laboratory/omnidocbench
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Which open-source model is criminally underrated?
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Which open-source model is criminally underrated?