AI data center boom could be bad news for other infrastructure projects
Improvements to roads, bridges, and other infrastructure could take a hit as data center construction accelerates.
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NVIDIA to reportedly increase production of H200 AI chip, as U.S. eases China export ban
Key Highlights: The past year has been anything but quiet for the global chip industry. While trade policy under the Trump administration has been a major talking point for months, a latest decision this week has suddenly pushed NVIDIA back into the spotlight. After prolonged uncertainty around AI chip exports, the U.S. government has finally become lenient. NVIDIA is reassessing H200 AI chip production capacity after getting green signal from Trump administration According to an exclusive report by […]
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WW-PGD: Projected Gradient Descent optimizer
Announcing: ๐ช๐ช-๐ฃ๐๐ โ ๐ช๐ฒ๐ถ๐ด๐ต๐๐ช๐ฎ๐๐ฐ๐ต๐ฒ๐ฟ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ฒ๐ฑ ๐๐ฟ๐ฎ๐ฑ๐ถ๐ฒ๐ป๐ ๐๐ฒ๐๐ฐ๐ฒ๐ป๐ I just released WW-PGD, a small PyTorch add-on that wraps standard optimizers (SGD, Adam, AdamW, etc.) and applies an epoch-boundary spectral projection using WeightWatcher diagnostics. Elevator pitch: WW-PGD explicitly nudges each layer toward the Exact Renormalization Group (ERG) critical manifold during training. ๐ง๐ต๐ฒ๐ผ๐ฟ๐ ๐ถ๐ป ๐๐ต๐ผ๐ฟ๐ โข HTSR critical condition: ฮฑ โ 2 โข SETOL ERG condition: trace-log(ฮป) over the spectral tail = 0 WW-PGD makes these explicit optimization targets, rather than […]
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As AI Grows More Complex, Model Builders Rely on NVIDIA
Unveiling what it describes as the most capable model series yet for professional knowledge work, OpenAI launched GPT-5.2 today. The model was trained and deployed on NVIDIA infrastructure, including NVIDIA Hopper and GB200 NVL72 systems. Itโs the latest example of how leading AI builders train and deploy at scale on NVIDIAโs full-stack AI infrastructure. Pretraining: The Bedrock of Intelligence AI models are getting more capable thanks to three scaling laws: pretraining, post-training and test-time scaling. Reasoning models, which […]
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3 Feature Engineering Techniques for Unstructured Text Data
Machine learning models possess a fundamental limitation that often frustrates newcomers to natural language processing (NLP): they cannot read.
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Bringing state-of-the-art Gemini translation capabilities to Google Translate
Weโre bringing Geminiโs state-of-the-art translation model to Google Translate for text, and more new features.
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You can now have more fluid and expressive conversations when you go Live with Search.
When you go Live with Search, you can have a back-and-forth voice conversation in AI Mode to get real-time help and quickly find relevant sites across the web. And now, โฆ
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New method improves the reliability of statistical estimations
Letโs say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county. They might train a machine-learning model to estimate the magnitude of this association, since machine-learning methods are especially good at learning complex relationships. Standard machine-learning methods excel at making predictions and sometimes provide uncertainties, like confidence intervals, for these predictions. However, they generally donโt provide estimates or confidence intervals when determining whether two variables are related. […]
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Enabling small language models to solve complex reasoning tasks
As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a wide margin on complex tasks. Try playing Sudoku with one, for instance, where you fill in numbers one through nine in such a way that each appears only once across the columns, rows, and sections of a nine-by-nine grid. Your AI opponent will either fail […]
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Spectral Community Detection in Clinical Knowledge Graphs
Introduction How do we identify latent groups of patients in a large cohort? How can we find similarities among patients that go beyond the well-known comorbidity clusters associated with specific diseases? And more importantly, how can we extract quantitative signals that can be analyzed, compared, and reused across different clinical scenarios? The information associated to [โฆ] The post Spectral Community Detection in Clinical Knowledge Graphs appeared first on Towards Data Science.
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